How digital media drive affective polarization through partisan sorting
Open accessAbstract
Politics has in recent decades entered an era of intense polarization. Explanations have implicated digital media, with the so-called echo chamber remaining a dominant causal hypothesis despite growing challenge by empirical evidence. This paper suggests that this mounting evidence provides not only reason to reject the echo chamber hypothesis but also the foundation for an alternative causal mechanism. To propose such a mechanism, the paper draws on the literatures on affective polarization, digital media, and opinion dynamics. From the affective polarization literature, we follow the move from seeing polarization as diverging issue positions to rooted in sorting: an alignment of differences which is effectively dividing the electorate into two increasingly homogeneous megaparties. To explain the rise in sorting, the paper draws on opinion dynamics and digital media research to present a model which essentially turns the echo chamber on its head: it is not isolation from opposing views that drives polarization but precisely the fact that digital media bring us to interact outside our local bubble. When individuals interact locally, the outcome is a stable plural patchwork of cross-cutting conflicts. By encouraging nonlocal interaction, digital media drive an alignment of conflicts along partisan lines, thus effacing the counterbalancing effects of local heterogeneity. The result is polarization, even if individual interaction leads to convergence. The model thus suggests that digital media polarize through partisan sorting, creating a maelstrom in which more and more identities, beliefs, and cultural preferences become drawn into an all-encompassing societal division.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes a computational model in which digital media drive affective polarization not through echo chambers but through nonlocal interaction that aligns local political cultures into partisan sorting. Simulations show that increasing nonlocal interaction can sharply raise sorting when influence homophily is high, while purely local interaction keeps sorting low.
Key findings
- In example runs with high influence homophily (h = 8; n = 10, |A| = 196, c = 4, m = 10), γ = 1 quickly reaches a high sorting value ψ; γ = 0.5 rises more slowly and settles around ψ = 0.3; γ = 0 remains near the expected ψ = 0.
- Across 8,000 simulation runs varying γ and h, low influence homophily (h = 1) produces no partisan sorting regardless of γ; high homophily (h = 8) produces a rapid increase in sorting past a threshold of nonlocal interaction; intermediate homophily (h = 4) shows bistability between low- and high-sorting end states.
- The same general γ–ψ relationship appears on random regular, scale-free, and connected caveman networks (1,600 runs over the parameter space), though the clique-dominated caveman network requires higher γ before sorting rises.
- With n = 10 dynamic attributes and m = 10 possible states, the chance that two groups share at least one attribute is about 65% (1 − (9/10)^10 ≈ 0.65), which can prevent sorting from reaching 1 and affects convergence probability.
Methods
- Agent-based opinion-dynamics model: agents on a graph (primarily a two-dimensional torus lattice with Moore neighborhood), each with n dynamic attributes taking integer values 1–m and a fixed partisan attribute S (k = 2); partisanship is weighted by c = 4. Each asynchronous update selects a random agent; interlocutors are local neighbors with fraction γ replaced by nodes sampled randomly from the entire network; influence follows an urn model based on relative similarity with homophily parameter h; sorting ψ is the fraction of shared dynamic attributes among same-party agents minus that among different-party agents.
- Simulations: 8,000 runs varying γ and h (Fig. 2 reports h = 1, 4, 8), plus 1,600 runs on three common network structures (random regular, scale-free, connected caveman). Convergence is operationalized as no update during 10 runs across the full number of agents, or after 1,000,000 iterations.
- Analysis: final ψ as a function of γ; PCA on the matrix of distances between agents and ridgeline plots to visualize group distributions over time; grid visualizations treating dynamic attributes as a base-m number.
Limitations
- Stated: partisan affiliation is treated as fixed, assuming it changes on a slower timescale; the model focuses on k = 2 bipolarization (though expandable to k > 2); nonlocal interlocutors are chosen without self-similarity bias, leaving selective-exposure-like bias to future research; the torus lattice is a poor representation of real-world networks, though other network structures are tested; convergence is challenging to prove analytically, so an empirically chosen stopping metric is used.
- Stated: the model is minimal and theoretical; it does not directly measure affective polarization but uses sorting ψ as a proxy linked in the literature; no empirical validation against real-world digital media data is presented.
- Evident: parameter choices (c = 4, h values, n = 10, m = 10, |A| = 196 or 625) and simulation stopping rules may affect results; causal claims about digital media are model-derived, not empirically tested; the paper notes academic debate about how far political divisions dominate cultural life, and cites a study finding most lifestyle preferences remain shared across the political divide.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
20 of 56 cited sources checked · 6 supported · 0 flagged · accuracy not yet measured
- Premisecitation
The online news audience is less ideologically segregated than in-person interactions with family, friends, coworkers, and political discussants.
“Research using internet traffic data has even suggested that the online news audience is in fact less ideologically segregated than in-person interactions with family, friends, coworkers, and political discussants (70–72).”
Quote in the paperNot a substantial claim2 of 3 sources checked- cites [70] M. Gentzkow, J. M. Shapiro, Ideological segregation online and offline. Q. J. Econ. 126, 1799– 1839 (2011). source
Section 4.1 reports that Internet news segregation is lower than segregation in face-to-face interactions involving families, workplaces, and trusted acquaintances. Section 2.3 describes measures for friends and acquaintances, but our copy does not identify political discussants as a measured group, so that specific comparison is not established by this source. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [71] D. C. Mutz, P. S. Martin, Facilitating communication across lines of political difference: The role of mass media. Am. Polit. Sci. Rev. 95, 97– 114 (2001). source
The section “Sources of Exposure to Cross-Cutting Political Views” finds that newspapers and television news expose people to more differing political views than interpersonal discussants, including workplace contacts. It does not establish the claim about online news audiences or use internet traffic data; “Implications of Availability and Selectivity” instead notes that people may select political websites that share their views. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured - cites [72] D. C. Mutz, J. J. Mondak, The workplace as a context for cross-cutting political discourse. J. Polit. 68, 140– 155 (2006). source
- Premisecitation
Individuals exposed to opposing views on Twitter became more polarized, not less.
“Bail et al. (75) similarly found in a field experiment on Twitter that individuals who were exposed to opposing views became more polarized, not less (see also ref. 25).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [75] C. A. Bail, Exposure to opposing views on social media can increase political polarization. Proc. Natl. Acad. Sci. U.S.A. 115, 9216– 9221 (2018). 30154168 PMC6140520 source
The Abstract reports that Republicans expressed substantially more conservative views after following a liberal Twitter bot. Democrats became only slightly more liberal after following a conservative bot, and that effect was not statistically significant. The citing sentence overgeneralizes the increase in polarization to exposed individuals as a whole; the Discussion and Conclusion also cautions against generalizing beyond the study population. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Premisecitation
Digital media brings individuals into interaction with others outside their local network.
“The parameter γ thus represents a simple way of capturing the empirically observed effect of digital media: digital media means that individuals are brought into interaction with others outside their local network.”
Quote in the paperSubstantial claim1 of 3 sources checked- cites [25] C. A. Bail, Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing (Princeton University Press, 2021). source
- cites [26] A. Guess, B. Nyhan, B. Lyons, J. Reifler, Avoiding the echo chamber about echo chambers. Knight Foundation 2, 1– 25 (2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [27] P. Törnberg, J. Uitermark, Tweeting ourselves to death: The cultural logic of digital capitalism. Media Cult. Soc. 44, 1– 20 (2022). source
- Premisecitation
When cleavages and conflicts come to align (sorting), the effects intensify prejudice and conflict between opposing political groups.
“Correspondingly, when cleavages and conflicts come to align—what we will refer to as sorting—the effects are synergetic in terms of intensifying prejudice and conflict between opposing political groups (18, 44).”
Quote in the paperSubstantial claim1 of 2 sources checked - Premisecitation
Partisanship is emerging as an important social identity under which many societal divisions and conflicts are coming to align.
“Many scholars link these negative feelings to shifts in identity, with partisanship emerging as an important social identity under which many societal divisions and conflicts are coming to align (15–18).”
Quote in the paperSubstantial claim1 of 4 sources checked- cites [15] S. Iyengar, G. Sood, Y. Lelkes, Affect, not ideology: A social identity perspective on polarization. Public Opin. Q. 76, 405– 431 (2012). source
- cites [16] S. Iyengar, S. J. Westwood, Fear and loathing across party lines: New evidence on group polarization. Am. J. Pol. Sci. 59, 690– 707 (2015). source
- cites [17] L. Mason, “I disrespectfully agree”: The differential effects of partisan sorting on social and issue polarization. Am. J. Pol. Sci. 59, 128– 145 (2015). source
- cites [18] L. Mason, A cross-cutting calm: How social sorting drives affective polarization. Public Opin. Q. 80, 351– 377 (2016). source
- Interpretationreasoning
Polarization on digital media affords a form of politics rooted in identity rather than opinion.
“The suggestion is, in short, that polarization on digital media is driven by conflict rather than isolation (117), affording a form of politics rooted in identity rather than opinion (4).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [4] P. Törnberg, C. Andersson, K. Lindgren, S. Banisch, Modeling the emergence of affective polarization in the social media society. PLoS One 16, e0258259 (2021). 34634056 PMC8504759 source
- Findingown data
With γ = 0, ψ remains fixed at a low level.
“When γ is high, ψ quickly reaches a high level; when γ is 0.5, ψ rises more slowly and settles on a lower value; and with γ = 0, ψ remains fixed at a low level.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingown data
At high levels of influence homophily (h = 8), there is a rapid increase in sorting at a certain threshold of nonlocal interaction.
“At high levels of influence homophily (h = 8; Fig. 2C), the result is a rapid increase in sorting at a certain threshold of nonlocal interaction.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingown data
The evolution of ψ depends on γ.
“Fig. 1 shows that the evolution of ψ indeed depends on γ.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingown data
When γ is high, ψ quickly reaches a high level.
“When γ is high, ψ quickly reaches a high level; when γ is 0.5, ψ rises more slowly and settles on a lower value; and with γ = 0, ψ remains fixed at a low level.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingown data
At low levels of influence homophily (h = 1), there is no partisan sorting, regardless of γ.
“At low levels of influence homophily (h = 1; Fig. 2A), the result is no partisan sorting, regardless of γ.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingreasoning
The model described an emergent causal mechanism through which more interaction outside one's local bubble can bring about the type of partisan sorting that has been linked to affective polarization.
“The model described an emergent causal mechanism through which more interaction outside one’s local bubble can bring about the type of partisan sorting that has been linked to affective polarization.”
Quote in the paperSubstantial claim - Findingreasoning
The level of sorting does not always reach 1, as the groups by chance come to share some attributes.
“This can also be seen in Fig. 2C: the level of sorting does not always reach 1, as the groups by chance come to share some attributes.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingreasoning
When interaction takes place across space, the tendency is for groups to converge along the lines of partisan identity.
“However, when interaction takes place across space, the tendency is for groups to converge along the lines of partisan identity.”
Quote in the paperSubstantial claim - Findingreasoning
When individuals interact in clusters, the result tends to be local convergence, resulting in a stable plural patchwork of cross-cutting conflicts.
“When individuals interact in clusters, the result tends to be local convergence, resulting in a stable plural patchwork of cross-cutting conflicts.”
Quote in the paperSubstantial claim - Findingreasoning
The combination between stronger influence between similar individuals and the empirically observed increase in interaction with others can provide an alternative to selective exposure for explaining the link between media and polarization.
“For the media literature, the model showed that the combination between stronger influence between similar individuals and the empirically observed increase in interaction with others can provide an alternative to selective exposure for explaining the link between media and polarization.”
Quote in the paperSubstantial claim - Findingreasoning
If we follow the affective polarization literature's focus on sorting as a driver of affective polarization, polarization can occur even if individual interaction leads to convergence.
“For the opinion dynamics literature, the model showed that if we follow the affective polarization literature’s focus on sorting as a driver of affective polarization, polarization can occur even if individual interaction leads to convergence.”
Quote in the paperSubstantial claim - Findingown data
The relationship between γ and ψ remains in random regular, scale-free, and connected caveman network structures: more nonlocal interaction tends to lead to increased partisan sorting.
“As can be seen, the relationship between γ and ψ remains in all these structures: more nonlocal interaction tends to lead to increased partisan sorting.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingreasoning
When influence homophily is intermediate (h = 4), the system exhibits bistability: for higher values of γ, the system can end up in either a nonsorted or highly sorted end state, depending on initial conditions and stochastic dynamics.
“When influence homophily is in between low and high (h = 4; Fig. 2B), the system exhibits bistability: for higher values of γ, the system can end up in either a nonsorted or highly sorted end state, depending on initial conditions and stochastic dynamics.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingreasoning
With 10 flexible attributes and 10 possible states, the probability that the two groups will share at least one attribute is around 65%.
“This result can best be understood by considering that with 10 flexible attributes and 10 possible states, the probability that the two groups will share at least one attribute is around 65% (since 1−(m−1m)n=1− (910)10≈ 0.65).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Findingown data
Highly clique-based networks, such as the connected caveman network, require higher levels of γ before partisan sorting rises.
“It is worth noting that highly clique-based networks, such as the connected caveman network (Fig. 3C), require higher levels of γ before partisan sorting rises.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Premisecitation
Empirical evidence has been accumulating against the selective exposure/echo chamber hypothesis.
“However, empirical evidence has been accumulating against this hypothesis, leading some researchers to suggest that the echo chamber may be an intellectual cul-de-sac (14), instead calling for alternative explanations.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] A. Bruns, Are Filter Bubbles Real? (John Wiley & Sons, 2019).
- Interpretationreasoning
By connecting individuals outside their local networks, digital media drive a global alignment of conflicts by effacing the counterbalancing effects of local cultures.
“By connecting individuals outside their local networks, digital media drive a global alignment of conflicts by effacing the counterbalancing effects of local cultures.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Premisecitation
Civil war is 12 times less probable in societies where ethnicity is cross-cut by another social identity such as class, geography, or religion.
“scholars have found that civil war is 12 times less probable in societies where ethnicity is cross-cut by another social identity such as class, geography, or religion (ref. 45; see also refs. 46, 47).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [45] J. R. Gubler, J. S. Selway, Horizontal inequality, crosscutting cleavages, and civil war. J. Conflict Resolut. 56, 206– 232 (2012). source
- Premisenone given
Individuals tend to be less influenced by ideas from the opposing side.
“Regardless of the underlying mechanism at play, the field has gathered substantial evidence for observation that individuals tend be less influenced by ideas from the opposing side.”
Quote in the paperSubstantial claim - Interpretationown data
The paper's computational model isolates an emergent mechanism through which digital media may drive partisan sorting.
“Combining insights from these literatures, the paper thus presents a computational model that isolates an emergent mechanism through which digital media may drive partisan sorting.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Interpretationreasoning
Convergence can lead to sorting-the type of partisan alignment linked to a rise in affective polarization.
“While convergence may not be capable of producing divergence, we will show that it can lead to sorting—i.e., to the type of partisan alignment which in the literature has been linked to a rise in affective polarization.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Premisecitation
The American electorate has come to be organized in two increasingly homogeneous parties, with a variety of social, cultural, economic, geographic, and ideological cleavages falling in line with the partisan divide.
“The American electorate has thus come to be organized in two increasingly homogeneous parties, with a variety of social, cultural, economic, geographic, and ideological cleavages falling in line with the partisan divide (19, 50, 51).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [19] M. Levendusky, The Partisan Sort: How Liberals Became Democrats and Conservatives Became Republicans (University of Chicago Press, 2009). source
- cites [50] A. I. Abramowitz, K. L. Saunders, Ideological realignment in the US electorate. J. Polit. 60, 634– 652 (1998). source
- cites [51] N. McCarty, K. T. Poole, H. Rosenthal, Polarized America: The Dance of Ideology and Unequal Riches (MIT Press, 2016).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Exposure to out-party news sources intensifies polarization.
“For instance, Garrett et al. (74) found survey evidence suggesting that exposure to out-party news sources will in fact intensify polarization.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [74] R. K. Garrett, Implications of pro-and counterattitudinal information exposure for affective polarization. Hum. Commun. Res. 40, 309– 332 (2014). source
- Interpretationreasoning
As each shared attribute increases the strength of influence between the groups, the result is a certain probability of convergence which is a function of c and h.
“As each shared attribute increases the strength of influence between the groups, the result is a certain probability of convergence which is a function of c and h.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Interpretationcitation
Digital media may disturb the balancing mechanism of plural societies by pushing conflicts and cleavages to align.
“Digital media may in this way disturb the balancing mechanism (24) of plural societies, by pushing conflicts and cleavages to align”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] L. Coser, The Functions of Social Conflict (The Free Press, New York, NY, 1956).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisenone given
Alignment of difference can produce a sense of social distance.
“The paper brought to this literature the idea from the affective polarization literature that alignment of difference can produce a sense of social distance, thus shifting the focus from divergence to sorting in explaining the dynamics of polarization.”
Quote in the paperNot a substantial claim - Premisecitation
Sorting causes partisans to "dislike, even loathe" one another.
“sorting causes partisans to “dislike, even loathe” one another (15)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [15] S. Iyengar, G. Sood, Y. Lelkes, Affect, not ideology: A social identity perspective on polarization. Public Opin. Q. 76, 405– 431 (2012). source
- Interpretationreasoning
Attempts of media platforms to reduce polarization by acting against echo chambers-algorithmically increasing exposure to opposing ideas-may backfire, instead resulting in intensified polarization and conflict.
“This suggests that the attempts of media platforms to reduce polarization by acting against echo chambers—algorithmically increasing exposure to opposing ideas—may backfire, instead resulting in intensified polarization and conflict.”
Quote in the paperSubstantial claim - Premisereasoning
Digital media brings us to interact outside of our local bubble.
“it is not lack of exposure to competing ideas that lead to polarization but precisely that digital media brings us to interact outside of our local bubble.”
Quote in the paperSubstantial claim - Interpretationreasoning
Digital media do not isolate us from opposing ideas.
“Digital media do not isolate us from opposing ideas; au contraire, they throw us into a national political war, in which we are forced to take sides.”
Quote in the paperSubstantial claim - Interpretationreasoning
There is a feedback loop between partisan sorting and affective polarization.
“These dynamics thus suggest a feedback loop between partisan sorting and affective polarization: sorting causes partisans to “dislike, even loathe” one another (15), in turn reducing their mutual social influence which further intensifies the process of sorting.”
Quote in the paperSubstantial claim - Interpretationreasoning
If we do not assume the existence of negative influence, the diversity of cultural preferences-and thus the likelihood of two groups by chance sharing some preferences-will play a role in determining the level of social sorting.
“This suggests that if we do not assume the existence of negative influence, the diversity of cultural preferences—and thus the likelihood of two groups by chance sharing some preferences—will play a role in determining the level of social sorting.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
- Interpretationcitation
Polarization on digital media is driven by conflict rather than isolation.
“The suggestion is, in short, that polarization on digital media is driven by conflict rather than isolation (117), affording a form of politics rooted in identity rather than opinion (4).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [117] R. Collins, C-escalation and d-escalation: A theory of the time-dynamics of conflict. Am. Sociol. Rev. 77, 1– 20 (2012). source
- May combine two claims. 24
- Classified as probably not a substantial claim. 7
May combine two claims.
PremisecitationDigital media do not isolate individuals from opposing ideas; they bring them to interact with individuals outside their local bubble and force them to take sides.
“This literature suggests that digital media do not isolate us from opposing ideas; au contraire, they bring us to interact with individuals outside our local bubble, and they throw us into a political war, in which we are forced to take sides (25–27).”
Quote in the paperSubstantial claim1 of 3 sources checked- cites [25] C. A. Bail, Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing (Princeton University Press, 2021). source
- cites [26] A. Guess, B. Nyhan, B. Lyons, J. Reifler, Avoiding the echo chamber about echo chambers. Knight Foundation 2, 1– 25 (2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [27] P. Törnberg, J. Uitermark, Tweeting ourselves to death: The cultural logic of digital capitalism. Media Cult. Soc. 44, 1– 20 (2022). source
In “Political discourse as self-presentation” (page unavailable), the paper says messages from opposing groups are common on social media and can fuel outrage and strengthen group identity; it also describes a broader political culture war. But it says social media cluster users into echo chambers or filter bubbles, and it does not establish that users interact with people outside those groups or are forced to take sides. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationThe political divide has remained relatively stable in the United States in terms of issue positions, while many other indicators point to a significant increase in partisan polarization and animosity in the mass public.
“While the political divide has remained relatively stable in the United States in terms of issue positions (33), many other indicators point to a significant increase in partisan polarization and animosity in the mass public (18, 34): Democrats and Republicans are growing increasingly cold to each other, coming to dislike or even disdain their partisan opponents (15).”
Quote in the paperSubstantial claim1 of 4 sources checked- cites [33] M. P. Fiorina, Unstable Majorities: Polarization, Party Sorting, and Political Stalemate (Hoover Press, 2017).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [18] L. Mason, A cross-cutting calm: How social sorting drives affective polarization. Public Opin. Q. 80, 351– 377 (2016). source
- cites [34] Pew Research Center, Partisanship and Political Animosity in 2016: Highly Negative Views of the Opposing Party and Its Members (2016). https://www.pewresearch.org/politics/2016/06/22/partisanship-and-political-animosity-in-2016/. Accessed 17 September 2022.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [15] S. Iyengar, G. Sood, Y. Lelkes, Affect, not ideology: A social identity perspective on polarization. Public Opin. Q. 76, 405– 431 (2012). source
May combine two claims.
PremisecitationPartisan social media users tend to interact across the ideological divide, but this interaction tends to be contentious and conflictual rather than rational deliberation.
“Research on social media data tells a similar story: while partisan social media users do tend to interact across the ideological divide (62), this interaction is not characterized by rational arguments and deliberation but tends to be contentious and conflictual, suggesting that users may not be engaging in good faith attempts at seeing things through other perspectives (98–100).”
Quote in the paperSubstantial claim2 of 4 sources checked- cites [62] P. Barberá, J. T. Jost, J. Nagler, J. A. Tucker, R. Bonneau, Tweeting from left to right: Is online political communication more than an echo chamber? Psychol. Sci. 26, 1531– 1542 (2015). 26297377 source
- cites [98] G. Evolvi, Hate in a tweet: Exploring internet-based islamophobic discourses. Religions (Basel) 9, 307 (2018). source
Section 3 (Brexit and Twitter Islamophobia; page unavailable) says Islamophobic Twitter groups rarely converse with Muslims and instead establish an antagonistic relationship with them. Section 5.2 describes derogatory tweets intended to upset other users. This supports the contentious-interaction portion of the citing sentence, but the paper’s post-Brexit Islamophobia findings do not establish that characterization for partisan social media users generally. The separate claim that users interact across the ideological divide is cited to another source (62). (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [99] R. Moernaut, J. Mast, M. Temmerman, M. Broersma, Hot weather, hot topic. Polarization and sceptical framing in the climate debate on Twitter. Inform. Commun. Soc. 25, 1047– 1066 (2022). source
The Conclusion reports that when interactions occurred between climate sceptics and believers on Twitter, they tended to be antagonistic and aimed at delegitimizing the other group rather than fostering constructive debate. This supports the contentious-interaction portion attributed to this source, but the study does not establish that pattern for partisan social media users generally. The separate claim that users interact across the ideological divide is cited to another source (62). (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [100] A. Gruzd, J. Roy, Investigating political polarization on Twitter: A Canadian perspective. Policy Internet 6, 28– 45 (2014). source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSocial media is characterized by intense interaction across the political divide.
“This evidence has been taken to suggest that in contrast to the assumptions of the selective exposure hypothesis, social media is in fact characterized by intense interaction across the political divide (73).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [73] A. Keuchenius, P. Törnberg, J. Uitermark, Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter. PLoS One 16, e0256696 (2021). 34464396 PMC8407581 source
The Results (Coalitions and divisions in the debate; page unavailable) document antagonistic interaction between opposing groups in the Dutch Black Pete debate on Twitter. But the Conclusion and discussion says the study concerns one debate, time period, country, and platform, and leaves broader generalization to future research. It does not establish that social media generally is characterized by intense interaction across political divides. (LLM, conf=0.84)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationStudies on social media have found limited evidence for homogeneous clusters on most social media platforms; users may be unlikely to rebroadcast opposing content but are often exposed to it and may respond.
“Studies on social media have found limited evidence for the described homogeneous clusters on most social media platforms: while users may be unlikely to rebroadcast content from the opposing side, they are often exposed to it and may even respond (11, 62–69).”
Quote in the paperSubstantial claim4 of 9 sources checked- cites [11] R. Karlsen, K. Steen-Johnsen, D. Wollebaek, B. Enjolras, Echo chamber and trench warfare dynamics in online debates. Eur. J. Commun. 32, 257– 273 (2017). 28690351 PMC5482382 source
The Trench warfare dynamics section says there is little evidence that people avoid political diversity online, and the Conclusion reports that people frequently encounter opposing arguments and can interact with those holding opposite views. The paper does not establish this pattern across most social media platforms or address whether users rebroadcast opposing content; its Conclusion cautions that the dynamics may differ by platform. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [62] P. Barberá, J. T. Jost, J. Nagler, J. A. Tucker, R. Bonneau, Tweeting from left to right: Is online political communication more than an echo chamber? Psychol. Sci. 26, 1531– 1542 (2015). 26297377 source
- cites [63] S. Goel, W. Mason, D. J. Watts, Real and perceived attitude agreement in social networks. J. Pers. Soc. Psychol. 99, 611– 621 (2010). 20731500 source
- cites [64] E. Bakshy, S. Messing, L. A. Adamic, Exposure to ideologically diverse news and opinion on Facebook. Science 348, 1130– 1132 (2015). 25953820 source
- cites [65] E. Colleoni, A. Rozza, A. Arvidsson, Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. J. Commun. 64, 317– 332 (2014). source
- cites [66] E. Lawrence, J. Sides, H. Farrell, Self-segregation or deliberation? Blog readership, participation, and polarization in American politics. Perspect. Polit. 8, 141– 157 (2010). source
The Blog Authors section says cross-ideological links are infrequent, but some involve substantive argument and conversation, supporting the possibility of responses to opposing views. The Homophily and Polarization in the Blogosphere section instead finds that 94% of political blog readers read only blogs from one ideological side. The paper studies political blogs, not most social media platforms, and does not establish that users are often exposed to opposing content. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [67] C. Vaccari, Of echo chambers and contrarian clubs: Exposure to political disagreement among German and Italian users of Twitter. Soc. Media Soc. 2, 1– 24 (2016). source
The Introduction and Conclusion report that German and Italian Twitter users encountered opposing views, even though homogeneity was the modal outcome. The paper does not establish that homogeneous clusters have limited evidence across most social media platforms, nor do the cited passages assess whether users rebroadcast or respond to opposing content. Our copy provides no page numbers for these passages. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [68] S. Yardi, D. Boyd, Dynamic debates: An analysis of group polarization over time on twitter. Bull. Sci. Technol. Soc. 30, 316– 327 (2010). source
- cites [69] G. De Francisci Morales, C. Monti, M. Starnini, No echo in the chambers of political interactions on Reddit. Sci. Rep. 11, 2818 (2021). 33531520 PMC7854755 source
The Introduction and Discussion (pages unavailable) report that, in Reddit discussions of the 2016 US election, opposing political groups interacted more often than expected and Clinton supporters were particularly likely to answer Trump supporters. That supports the possibility of exposure and response, but the paper does not establish limited homogeneous clustering on most social media platforms; its Discussion describes echo chambers on other platforms. It also does not establish a general pattern of users declining to rebroadcast opposing content. (LLM, conf=0.88)
Experimental — accuracy not yet measured
May combine two claims.
PremisecitationPartisans consume media from across the partisan divide but do not trust what opposing media channels tell them.
“In other words, partisans consume media from across the partisan divide but do not trust in what opposing media channels tell them.”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [96] A. M. Guess, P. Barberá, S. Munzert, J. Yang, The consequences of online partisan media. Proc. Natl. Acad. Sci. U.S.A. 118, e2013464118 (2021). 33782116 PMC8040813 source
The Discussion (page not available) says exposure to cross-cutting information is relatively common. It also reports reduced overall trust in mainstream media and suggests people may discount challenging content. It does not establish that partisans specifically distrust what opposing media channels tell them; that conclusion is more definite and more source-specific than the paper's finding. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [97] M. Jurkowitz, A. Mitchell, E. Shearer, M. Walker, US media polarization and the 2020 election: A nation divided (Pew Research Center, 2020). https://www.pewresearch.org/journalism/2020/01/24/u-s-media-polarization-and-the-2020-election-a-nation-divided/. Accessed 17 September 2022.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationThe negative influence/backfire effect is elusive, with limited relevance for politics outside the laboratory; while we may be less influenced by political opponents, we are unlikely to be negatively influenced by them.
“This has brought researchers to conclude that negative influence effect is elusive, with limited relevance for politics outside the laboratory (88, 90, 93–95): while we may be less influenced by our political opponents, we are unlikely to be negatively influenced by them.”
Quote in the paperNot a substantial claim2 of 5 sources checked- cites [88] D. M. Kahan, “The politically motivated reasoning paradigm, part 1: What politically motivated reasoning is and how to measure it” in Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource, R. A. Scott, S. M. Kosslyn, Eds. (John Wiley & Sons, Hoboken, NJ, 2015), pp. 1– 16. source
- cites [90] T. Wood, E. Porter, The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Polit. Behav. 41, 135– 163 (2019). source
- cites [93] R. S. Nickerson, Confirmation bias: A ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 175 (1998). source
- cites [94] D. M. Kahan, Ideology, motivated reasoning, and cognitive reflection: An experimental study. Judgm. Decis. Mak. 8, 407– 424 (2012). source
Section 5.1 reports ideologically motivated reasoning in an experiment, and Section 3.3 discusses how biased assessment of evidence might polarize beliefs. Neither passage addresses whether messages from political opponents cause a negative influence or backfire effect, or whether such an effect is rare outside the laboratory. Searches for those terms and for opposing-message effects found no passage settling the claim. (LLM, conf=0.72)
Experimental — accuracy not yet measured - cites [95] K. Takács, A. Flache, M. Mäs, Discrepancy and disliking do not induce negative opinion shifts. PLoS One 11, 1– 21 (2016). PMC4917087 27333160 source
The Summary and Conclusion reports no support for negative opinion shifts in the authors’ controlled experiments. It does not establish that the effect has limited relevance to politics outside the laboratory: the authors explicitly say they cannot exclude negative influence in field settings, and they did not study political opponents as such. (LLM, conf=0.86)
Experimental — accuracy not yet measured
May combine two claims.
InterpretationcitationDigital media are spaces for social identity formation and for symbolic displays of solidarity with allies and difference from outgroups.
“The model thus suggests rethinking digital media as not merely arenas for rational deliberation and political debate but as spaces for social identity formation and for symbolic displays of solidarity with allies and difference from outgroups (27).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [27] P. Törnberg, J. Uitermark, Tweeting ourselves to death: The cultural logic of digital capitalism. Media Cult. Soc. 44, 1– 20 (2022). source
May combine two claims.
Findingown dataWhen γ is 0.5, ψ rises more slowly and settles on a lower value.
“When γ is high, ψ quickly reaches a high level; when γ is 0.5, ψ rises more slowly and settles on a lower value; and with γ = 0, ψ remains fixed at a low level.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
May combine two claims.
FindingreasoningThe result is the crystallization of conflicting identities and the intensification of polarization, driven by a process in which sorting begets sorting and polarization begets polarization.
“The result is the crystallization of conflicting identities and the intensification of polarization, driven by a process in which sorting begets sorting and polarization begets polarization.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningDigital media may drive a sorting of differences, which has been linked to a breakdown of social cohesion and rising affective polarization.
“This paper provides a causal mechanism to explain this rise in polarization, by identifying how digital media may drive a sorting of differences, which has been linked to a breakdown of social cohesion and rising affective polarization.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
May combine two claims.
InterpretationreasoningThe mechanism is that diverse and nonlocal interactions of digital media drive plural conflicts to align along partisan lines, not that isolation drives divergence of opinions.
“The suggested mechanism essentially turns the echo chamber on its head: it is not that isolation drives divergence of opinions but that the diverse and nonlocal interactions of digital media drive plural conflicts to align along partisan lines.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
May combine two claims.
Premisenone givenSuch exposure does not seem to lead to political moderation: individuals are readily influenced by those they view as already similar to themselves, while disregarding messages from those they view as the outgroup.
“At the same time, such exposure does not seem to lead to political moderation: individuals are readily influenced by those they view as already similar to themselves, while disregarding messages from those they view as the outgroup.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningDigital media can intensify affective polarization by contributing to a runaway process in which more and more issues become drawn into a single growing social and cultural divide, in turn driving a breakdown of social cohesion.
“The model thus suggests that digital media can intensify affective polarization by contributing to a runaway process in which more and more issues become drawn into a single growing social and cultural divide, in turn driving a breakdown of social cohesion.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
Classified as probably not a substantial claim. May combine two claims.
InterpretationreasoningThe two points of empirical evidence (digital media increases cross-cutting interaction; exposure does not moderate) provide the foundation for an alternative hypothesis for media polarization.
“The suggestion of this paper is that these two points of empirical evidence may not only be reason to reject the echo chamber hypothesis (26), but they also provide the foundation for an alternative hypothesis for media polarization.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationIndividuals are more influenced by those similar to them.
“Following the idea that we are more influenced by those similar to us, the node is then influenced as a function of the similarity with its interlocutors.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [104] R. Axelrod, The dissemination of culture: A model with local convergence and global polarization. J. Conflict Resolut. 41, 203– 226 (1997). source
May combine two claims.
PremisecitationDigitalization does not appear to lead to a reduction of interaction across the political divide; it confronts us with diverse individuals, perspectives, and viewpoints, often in contentious ways.
“In summary, the empirical literature suggests that digitalization does not appear to lead to a reduction of interaction across political divide, but quite the opposite: it confronts us with diverse individuals, perspectives, and viewpoints, often in contentious ways.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe degree to which political divisions have come to dominate cultural life remains subject to academic debate, with a recent study finding that while some lifestyle preferences are politically polarized, most remain shared across the political divide.
“The degree to which political divisions have come to dominate cultural life, however, remains subject to academic debate, with a recent study finding that while some lifestyle preferences are politically polarized, most remain shared across the political divide (54).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [54] S. Praet, A. M. Guess, J. A. Tucker, R. Bonneau, J. Nagler, What’s not to like? Facebook page likes reveal limited polarization in lifestyle preferences. Polit. Commun. 39, 311– 338 (2022). source
Classified as probably not a substantial claim.
PremisecitationThe causal link between digital media and polarization runs not through divergence of opinions but via the dynamics of partisan sorting.
“According to these scholars, sorting is thus central to understanding the rise in polarization and the growing sense of difference, distrust, and disdain between opposing partisans, suggesting that the causal link between digital media and polarization runs not through divergence of opinions but rather via the dynamics of partisan sorting (19).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] M. Levendusky, The Partisan Sort: How Liberals Became Democrats and Conservatives Became Republicans (University of Chicago Press, 2009). source
May combine two claims.
InterpretationcitationThe paper's two points of empirical evidence may be reason to reject the echo chamber hypothesis and provide the foundation for an alternative emergent causal mechanism underlying the polarizing effects of digital media.
“The suggestion of this paper has been that these two points of empirical evidence may not only be reason to reject the echo chamber hypothesis (26), but that they also provide the foundation for an alternative emergent causal mechanism underlying the polarizing effects of digital media.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [26] A. Guess, B. Nyhan, B. Lyons, J. Reifler, Avoiding the echo chamber about echo chambers. Knight Foundation 2, 1– 25 (2018).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe simulations use an empirically chosen convergence metric, verified on a large number of cases and with substantial margin.
“Unless otherwise specified, the simulations here therefore use an empirically chosen metric for when convergence has occurred, verified on a large number of cases and with substantial margin: the simulation ends either when no update has occurred during 10 runs across the full number of agents or to prevent runs becoming infinitely stuck in a dynamic equilibrium, after 1,000,000 iterations.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningDigital media intensify polarization not as echo chambers but as a sorting machine, fueling a runaway social process that destabilizes plural societies by drawing more and more issues into a single expanding social and cultural divide.
“Digital media intensify polarization not as echo chambers but as a sorting machine, fueling a runaway social process that destabilizes plural societies by drawing more and more issues into a single expanding social and cultural divide.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningSorting reduces partisans' mutual social influence, which further intensifies the process of sorting.
“in turn reducing their mutual social influence which further intensifies the process of sorting.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataIf individuals are more influenced by those who are similar to them, interaction outside of local bubbles can result in increased partisan sorting, in turn associated to affective polarization.
“This is a striking result, showing that if individuals are more influenced by those who are similar to them, interaction outside of local bubbles can result in increased partisan sorting, in turn associated to affective polarization.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
- The authors state this as an assumption: Social similarity is relative to the larger social context.
- The authors state this as an assumption: Nonlocal interlocutors are chosen without bias toward self-similarity.
May combine two claims.
InterpretationreasoningIt is not lack of exposure to competing ideas that leads to polarization but precisely that digital media brings us to interact outside of our local bubble.
“it is not lack of exposure to competing ideas that lead to polarization but precisely that digital media brings us to interact outside of our local bubble.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThis creates a maelstrom in which additional identities, beliefs, and cultural belonging become sucked into a growing and all-encompassing societal division, which threatens the very foundation of social cohesion.
“creating a maelstrom in which additional identities, beliefs, and cultural belonging become sucked into a growing and all-encompassing societal division, which threatens the very foundation of social cohesion.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningDigital media throw us into a national political war, in which we are forced to take sides.
“Digital media do not isolate us from opposing ideas; au contraire, they throw us into a national political war, in which we are forced to take sides.”
Quote in the paperNot a substantial claim
The authors state this as an assumption.
Partisan affiliation is fixed throughout the model run, assuming it changes on a slower timescale than other attributes.
“Each agent i also has a single attribute, Si, corresponding to partisan affiliation, which held fixed throughout the model run (see also ref. 58).”
“We here treat partisan affiliation as fixed, assuming that it will change on a slower timescale than the other attributes.”
The authors state this as an assumption.
Social similarity is relative to the larger social context.
“The social similarity is in other words treated as relative: that is, how similar we see ourselves to a particular individual depends on the larger social context (for instance, while the differences between Trotskyists and Stalinists may appear as a large chasm for members of the Party, they may for outsiders appear as a matter of relatively trivial detail).”
The authors state this as an assumption.
Nonlocal interlocutors are chosen without bias toward self-similarity.
“While the selective exposure hypothesis would suggest that these individuals would be selected with a bias toward self-similarity, we will here let the choice be unbiased as we are seeking to examine whether sorting can appear without such empirically questioned assumptions.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Because there is a large volume of people to connect with online, there are significantly more extreme voices to affiliate with than in prior social groups limited by geography.
“Because there is such a large volume of people to connect with in the online context, there are significantly more extreme voices to affiliate with than there were in prior social groups that were limited by geography (Petersen et al. 2020, Törnberg 2022).”
From Social Media and Morality. (2024) · cites this paper as Törnberg 2022
The Discussion (page not provided) says digital media connect people outside their local social bubbles, supporting the comparison with geographically limited groups. It does not say that online networks offer significantly more extreme voices to affiliate with; the paper instead explains polarization through nonlocal interaction and partisan sorting. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNo solo la homogeneidad polariza; la interacción intergrupal también lo hace.
“algo que permite afirmar -de nuevo-que no solo la homogeneidad polariza, la interacción intergrupal también lo hace (Törnberg, 2022).”
From Política, uso de medios y polarización afectiva en España (2024) · cites this paper as Törnberg, 2022
The Results (Fig. 2; page unavailable) report that interaction outside local networks can increase partisan sorting associated with affective polarization. The claim omits a key condition: the model finds this effect when people are more influenced by similar others; with low influence homophily, it finds no partisan sorting regardless of the level of nonlocal interaction. The paper studies nonlocal interaction, which is not necessarily the same as intergroup interaction. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Influence-homophily values disagree with Fig. 2
MinorThe Results text says the systematic simulations used h values of 1, 2, and 5, but the Fig. 2 caption identifies the three plotted values as 1, 4, and 8.
“we carry out 8,000 simulation runs for three values of h (1, 2, 5)”
“when the level of influence homophily is (A) low (h = 1), (B) medium (h = 4), and (C) high (h = 8).”
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Partisans without Constraint: Political Polarization and Trends in American Public Opinion
Baldassarri, D., & Gelman, A. (2008) · American Journal of Sociology · doi:10.1086/590649 · Cited by 874 · cited 1× · see citing works
“While polarization has traditionally been understood as divergence in issue position, this literature thus shifts our understanding by describing polarization as linked to a process in which the political divide coming to encompass more and more issues, preferences, and identities (53).”
BackgroundGenerative Social Science: Studies in Agent-Based Computational Modeling
Epstein, J. (2007) · Journal of Ecological Anthropology · doi:10.5038/2162-4593.11.1.8 · Cited by 858 · see citing works
The welfare effects of social media
Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020) · American Economic Review · doi:10.1257/aer.20190658 · Cited by 857 · see citing works
The evidence for motivated reasoning in climate change preference formation
Druckman, J. N., & McGrath, M. C. (2019) · Nature Climate Change · doi:10.1038/s41558-018-0360-1 · Cited by 733 · cited 2× · see citing works
“J. N. Druckman, M. C. McGrath, The evidence for motivated reasoning in climate change preference formation.”
BackgroundFacilitating Communication across Lines of Political Difference: The Role of Mass Media
Mutz, D. C. (2001) · American Political Science Review · doi:10.1017/s0003055401000223 · Cited by 700 · see citing works
Ideological Realignment in the U.S. Electorate
Abramowitz, A. I., & Saunders, K. L. (1998) · The Journal of Politics · doi:10.2307/2647642 · Cited by 681 · see citing works
Galston, W. A. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511613579 · Cited by 660 · see citing works
Dynamic debates: an analysis of group polarization over time on Twitter
Yardi, S., & Boyd, D. (2010) · Bulletin of Science, Technology & Society · doi:10.1177/0270467610380011 · Cited by 625 · see citing works
The Workplace as a Context for Cross-Cutting Political Discourse
Mutz, D. C., & Mondak, J. J. (2006) · The Journal of Politics · doi:10.1111/j.1468-2508.2006.00376.x · Cited by 612 · see citing works
The Increasingly United States: How and Why American Political Behavior Nationalized
Hopkins, D. J. (2018) · doi:10.7208/chicago/9780226530406.001.0001 · Cited by 587 · see citing works
The Hostile Audience: The Effect of Access to Broadband Internet on Partisan Affect
Lelkes, Y., Sood, G., & Iyengar, S. (2017) · American Journal of Political Science · doi:10.1111/ajps.12237 · Cited by 476 · cited 2× · see citing works
“However, while studies have identified a link between digital media and rising polarization (2, 5, 6), the causal mechanism at play has been subject to significant debate (7).”
BackgroundMethodThe Motivated Processing of Political Arguments
Taber, C. S., Cann, D. M., & Kucsova, S. (2009) · Political Behavior · doi:10.1007/s11109-008-9075-8 · Cited by 465 · see citing works
Dynamics of Political Polarization
Baldassarri, D., & Bearman, P. (2007) · American Sociological Review · doi:10.1177/000312240707200507 · Cited by 455 · see citing works
DellaPosta, D., Shi, Y., & Macy, M. (2015) · American Journal of Sociology · doi:10.1086/681254 · Cited by 440 · see citing works
Echo chambers and viral misinformation: Modeling fake news as complex contagion
Törnberg, P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203958 · Cited by 430 · see citing works
Self-citationMisconceptions, Misinformation, and the Logic of Identity-Protective Cognition
Kahan, D. M. (2017) · doi:10.2139/ssrn.2973067 · Cited by 423 · see citing works
A Cross-Cutting Calm: How Social Sorting Drives Affective Polarization
Mason, L. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw001 · Cited by 413 · see citing works
Does Counter-Attitudinal Information Cause Backlash? Results from Three Large Survey Experiments
Guess, A., & Coppock, A. (2020) · British Journal of Political Science · doi:10.1017/s0007123418000327 · Cited by 372 · see citing works
(2019) · Cited by 369 · see citing works
Lawrence, E., Sides, J., & Farrell, H. (2010) · Perspectives on Politics · doi:10.1017/s1537592709992714 · Cited by 352 · see citing works
The polarizing impact of science literacy and numeracy on perceived climate change risks
Kahan, D. M., Peters, E., Wittlin, M., Slovic, P., Ouellette, L. L., Braman, D., & Mandel, G. (2012) · Nature Climate Change · doi:10.1038/nclimate1547 · Cited by 352 · see citing works
Garrett, R. K., Gvirsman, S. D., Johnson, B. K., Tsfati, Y., Neo, R., & Dal, A. (2014) · Human Communication Research · doi:10.1111/hcre.12028 · Cited by 351 · cited 1× · see citing works
“(74) found survey evidence suggesting that exposure to out-party news sources will in fact intensify polarization.”
BackgroundKahan, D. M. (2016) · Emerging Trends in the Social and Behavioral Sciences · doi:10.1002/9781118900772.etrds0417 · Cited by 341 · see citing works
The measurement of partisan sorting for 180 million voters
Brown, J. R., & Enos, R. D. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01066-z · Cited by 330 · see citing works
Corner, A., Whitmarsh, L. E., & Xenias, D. (2012) · Climatic Change · doi:10.1007/s10584-012-0424-6 · Cited by 326 · see citing works
Communism, Conformity, and Civil Liberties: A Cross-Section of the Nation Speaks Its Mind
Schwartz, R. D., & Stouffer, S. A. (1956) · The Yale Law Journal · doi:10.2307/793991 · Cited by 314 · see citing works
(2019) · Project Muse (Johns Hopkins University) · Cited by 295 · see citing works
Culture and Competition: Homophily and Distancing Explanations for Cultural Niches
Mark, N. (2003) · American Sociological Review · doi:10.1177/000312240306800302 · Cited by 290
Stewardship of global collective behavior
Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025764118 · Cited by 289 · see citing works
Norris, P. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9780511790614 · Cited by 281 · see citing works
Investigating Political Polarization on Twitter: A Canadian Perspective
Gruzd, A., & Roy, J. (2014) · Policy & Internet · doi:10.1002/1944-2866.poi354 · Cited by 280 · see citing works
The Behavioral Approach in Political Science: Epitaph for a Monument to a Successful Protest
Dahl, R. A. (1961) · American Political Science Review · doi:10.1017/s0003055400125924 · Cited by 271
Culture and Competition: Homophily and Distancing Explanations for Cultural Niches
Mark, N. (2003) · American Sociological Review · doi:10.2307/1519727 · Cited by 251 · see citing works
Is There a Culture War? Conflicting Value Structures in American Public Opinion
JACOBY, W. G. (2014) · American Political Science Review · doi:10.1017/s0003055414000380 · Cited by 248 · see citing works
Unstable Majorities: Polarization, Party Sorting, and Political Stalemate
Grossmann, M. (2019) · Political Science Quarterly · doi:10.1002/polq.12880 · Cited by 246 · see citing works
Generating Random Regular Graphs Quickly
Steger, A., & Wormald, N. C. (1999) · Combinatorics Probability Computing · doi:10.1017/s0963548399003867 · Cited by 240 · cited 1× · see citing works
“(A) A random regular graph, generated through the algorithm described in Steger and Wormald (112).”
MethodSmall Worlds and Cultural Polarization
FLACHE, A., & MACY, M. W. (2011) · The Journal of Mathematical Sociology · doi:10.1080/0022250x.2010.532261 · Cited by 237 · cited 1× · see citing works
“Focusing on polarization as the divergence over single attributes, models found that negative influence can lead to bipolarization—that is, division into two divergent groups—even when there are initially no agents with extreme opinions (86, 108, 109).”
BackgroundHorizontal Inequality, Crosscutting Cleavages, and Civil War
Gubler, J. R., & Selway, J. S. (2012) · Journal of Conflict Resolution · doi:10.1177/0022002711431416 · Cited by 227 · see citing works
Mäs, M., & Flache, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074516 · Cited by 226 · see citing works
Real and perceived attitude agreement in social networks.
Goel, S., Mason, W., & Watts, D. J. (2010) · Journal of Personality and Social Psychology · doi:10.1037/a0020697 · Cited by 219 · see citing works
Pluralistic Collapse: The “Oil Spill” Model of Mass Opinion Polarization
DellaPosta, D. (2020) · American Sociological Review · doi:10.1177/0003122420922989 · Cited by 219 · cited 3× · see citing works
“cal positions, values, and cultural preferences (21), the result is”
BackgroundThe consequences of online partisan media
Guess, A. M., Barberá, P., Munzert, S., & Yang, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013464118 · Cited by 189 · cited 1× · see citing works
“That psychological mechanisms such as selective processing may be at play when we interact with opposing messages on digital media finds some support in empirical studies showing that while media consumption may be largely bipartisan, media trust is highly polarized (96, 97).”
BackgroundThe Behavioral Approach in Political Science: Epitaph for a Monument to a Successful Protest
Dahl, R. A. (1961) · American Political Science Review · doi:10.2307/1952525 · Cited by 189 · see citing works
Vaccari, C., Valeriani, A., Barberá, P., Jost, J. T., Nagler, J., & Tucker, J. A. (2016) · Social Media + Society · doi:10.1177/2056305116664221 · Cited by 188 · see citing works
Breaking the social media prism: how to make our platforms less polarizing
Sufi, M. (2023) · Information Communication & Society · doi:10.1080/1369118x.2023.2258608 · Cited by 187 · see citing works
Prius or Pickup? How the Answers to Four Simple Questions Explain America’s Great Divide
Federico, C. M. (2019) · Political Science Quarterly · doi:10.1002/polq.12936 · Cited by 182 · see citing works
Partisans Without Constraint: Political Polarization and Trends in American Public OpinionPreprint
Baldassarri, D., & Gelman, A. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.1010098 · Cited by 176 · see citing works
Learning Together Slowly: Bayesian Learning about Political Facts
Hill, S. J. (2017) · The Journal of Politics · doi:10.1086/692739 · Cited by 159 · see citing works
Echo chamber and trench warfare dynamics in online debates
Karlsen, R., Steen-Johnsen, K., Wollebæk, D., & Enjolras, B. (2017) · European Journal of Communication · doi:10.1177/0267323117695734 · Cited by 154 · see citing works
Currid‐Halkett, E. (2017) · Princeton University Press eBooks · doi:10.1515/9781400884698 · Cited by 152 · see citing works
The Value Patterns of Democracy: A Case Study in Comparative Analysis
Lipset, S. M. (1963) · American Sociological Review · doi:10.2307/2090068 · Cited by 140 · see citing works
C-Escalation and D-Escalation: A Theory of the Time-Dynamics of Conflict
Collins, R. (2012) · American Sociological Review · doi:10.1177/0003122411428221 · Cited by 139 · see citing works
Opinion polarization by learning from social feedback
Banisch, S., & Olbrich, E. (2018) · Journal of Mathematical Sociology · doi:10.1080/0022250x.2018.1517761 · Cited by 125 · cited 3× · see citing works
“Banisch and Olbrich (110) and M€as and Flache (92) provide attempts at identifying such conditions, by assuming that individuals do not necessarily converge but can become more extreme when interacting with likeminded others.”
BackgroundInfluentialThe dynamics of political polarization
Levin, S. A., Milner, H. V., & Perrings, C. A. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116950118 · Cited by 121 · see citing works
Modeling the emergence of affective polarization in the social media society
Törnberg, P., Andersson, C., Lindgren, K., & Banisch, S. (2021) · PLOS ONE · doi:10.1371/journal.pone.0258259 · Cited by 118 · see citing works
Self-citationEcho Chambers in Parliamentary Twitter Networks: The Catalan Case
Valle, M. E. D., & Borge, R. (2018) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 111 · see citing works
Opinion polarization by learning from social feedback
Banisch, S., & Olbrich, E. (2019) · MPG.PuRe (Max Planck Society) · Cited by 107 · see citing works
The nonlinear feedback dynamics of asymmetric political polarization
Leonard, N. E., Lipsitz, K., Bizyaeva, A., Franci, A., & Yphtach Lelkes, B. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102149118 · Cited by 107 · see citing works
No echo in the chambers of political interactions on Reddit
De Francisci Morales, G., Monti, C., & Starnini, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-81531-x · Cited by 98 · see citing works
Discrepancy and Disliking Do Not Induce Negative Opinion Shifts
Takács, K., Flache, A., & Mäs, M. (2016) · PLOS ONE · doi:10.1371/journal.pone.0157948 · Cited by 97 · see citing works
Cross-Cuttingness, Cleavage Structures and Civil War Onset
Selway, J. (2010) · British Journal of Political Science · doi:10.1017/s0007123410000311 · Cited by 94 · see citing works
Polarized information ecosystems can reorganize social networks via information cascades
Tokita, C. K., Guess, A. M., & Tarnita, C. E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102147118 · Cited by 92 · see citing works
Persuasion in Parallel: How Information Changes Minds about Politics
Coppock, A. (2022) · doi:10.7208/chicago/9780226821832.001.0001 · Cited by 91 · see citing works
Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing
Bail, C. (2021) · doi:10.1515/9780691216508 · Cited by 86 · see citing works
Hate in a Tweet: Exploring Internet-Based Islamophobic Discourses
Evolvi, G. (2018) · Religions · doi:10.3390/rel9100307 · Cited by 83 · see citing works
The Welfare Effects of Social Media
Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2019) · doi:10.3386/w25514 · Cited by 81 · see citing works
Hot weather, hot topic. Polarization and sceptical framing in the climate debate on Twitter
Moernaut, R., Mast, J., Temmerman, M., & Broersma, M. (2022) · Information, Communication & Society · doi:10.1080/1369118x.2020.1834600 · Cited by 65 · see citing works
Introduction to Mathematical Sociology
Bonacich, P., & Lu, P. (2012) · Princeton University Press eBooks · doi:10.1515/9781400842452 · Cited by 61 · see citing works
Partisan Polarization and the Rise of the Tea Party MovementPreprint
Abramowitz, A. I. (2011) · SSRN Electronic Journal · Cited by 51 · see citing works
Ethnicity, class, and civil war: the role of hierarchy, segmentation, and cross-cutting cleavages
Siroky, D. S., & Hechter, M. (2016) · Civil Wars · doi:10.1080/13698249.2016.1145178 · Cited by 41 · see citing works
Group polarization and choice‐dilemmas: how important is self‐categorization?
Križan, Z., & Baron, R. S. (2006) · European Journal of Social Psychology · doi:10.1002/ejsp.345 · Cited by 35 · see citing works
Polarization, diversity, and democratic robustness
Bednar, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113843118 · Cited by 34 · cited 3× · see citing works
“As Bednar (114) argues, such federalism can effectively provide a source for cross-cutting cleavages, thus functioning as a safeguard and counterweight to the national government.”
BackgroundInfluentialTweeting ourselves to death: the cultural logic of digital capitalism
Törnberg, P., & Uitermark, J. (2022) · Media, Culture & Society · doi:10.1177/01634437211053766 · Cited by 33 · see citing works
Self-citationAffective polarization in the American public
Druckman, J. N., & Levy, J. (2022) · Edward Elgar Publishing eBooks · doi:10.4337/9781800379619.00030 · Cited by 29 · see citing works
What sustains cultural diversity and what undermines it? Axelrod and beyondPreprint
Flache, A., & Macy, M. W. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.physics/0604201 · Cited by 29 · see citing works
Keuchenius, A., Törnberg, P., & Uitermark, J. (2021) · PLOS ONE · doi:10.1371/journal.pone.0256696 · Cited by 27 · see citing works
Self-citationKawakatsu, M., Yphtach Lelkes, B., Levin, S. A., & Tarnita, C. E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102148118 · Cited by 27 · cited 2× · see citing works
“The effects are a dimensionality reduction, in which conflicts align along a single partisan divide (23).”
BackgroundWhat’s Not to Like? Facebook Page Likes Reveal Limited Polarization in Lifestyle Preferences
Praet, S., Guess, A. M., Tucker, J. A., Bonneau, R., & Nagler, J. (2022) · Political Communication · doi:10.1080/10584609.2021.1994066 · Cited by 22 · see citing works
WHEN WILL THEY EVER MAKE UP THEIR MINDS? THE SOCIAL STRUCTURE OF UNSTABLE DECISION MAKING
Flache, A., & Torenvlied, R. (2004) · Journal of Mathematical Sociology · doi:10.1080/00222500490480257 · Cited by 12 · see citing works
Wintemute, G. J., Robinson, S., Crawford, A., et al. (2022) · medRxiv · doi:10.1101/2022.07.15.22277693 · Cited by 12 · see citing works
Wintemute, G. J., Robinson, S. L., & Tomsich, E. A. (2022) · doi:10.31235/osf.io/q9ect · Cited by 9 · see citing works
CAROTHERS, T., & O’DONOHUE, A. (2019) · Democracies Divided · doi:10.5040/9780815750819.0003 · Cited by 2
Replication Data for: Learning Together Slowly: Bayesian Learning About Political Facts
Hill, S. J. (2016) · Harvard Dataverse · doi:10.7910/dvn/lgdizd
Currid-Halkett, E. (2017) · doi:10.2307/j.ctvc77hb4
Cleavages, parties and democracy
Lipset, S. M. (n.d.) · Party Systems and Voter Alignments Revisited · doi:10.4324/9780203469323_chapter_1
The Motivated Processing of Political Arguments
Taber, C. S., Cann, D. M., & Kucsova, S. (2008) · doi:10.2139/ssrn.1274028
Flagged first, then by citations
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Cited by
Sorted by citations
Social Drivers and Algorithmic Mechanisms on Digital Media.
Metzler, H., & Garcia, D. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231185057 · Cited by 204 · see citing works
A review and provocation: On polarization and platforms
Kreiss, D., & McGregor, S. C. (2024) · New Media & Society · doi:10.1177/14614448231161880 · Cited by 151 · see citing works
“…argue that the causes of this sectarian polarization are both political—such as the sorting of racial and other identities into distinct parties and the embrace of sectarianism by elites—and tied to broader shifts in media, including the rise of social media and platforms (e.g. Törnberg, 2022).”
BackgroundAlgorithm-mediated social learning in online social networks
Brady, W. J., Jackson, J. C., Lindström, B., & Crockett, M. J. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.06.008 · Cited by 132 · see citing works
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis
Stray, J., Halevy, A. Y., Assar, P., Hadfield-Menell, D., Boutilier, C. E., Ashar, A., Bakalar, C., Beattie, L., Ekstrand, M. D., Leibowicz, C., et al. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3632297 · Cited by 145 · see citing works
Van Bavel, J. J., Robertson, C. E., del Rosario, K., Rasmussen, J., & Rathje, S. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-022123-110258 · Cited by 121 · see citing works
Do social media undermine social cohesion? A critical review
González-Bailón, S., & Lelkes, Y. (2022) · Social Issues and Policy Review · doi:10.1111/sipr.12091 · Cited by 110 · see citing works
Vasist, P. N., Chatterjee, D., & Krishnan, S. (2023) · Information Systems Frontiers · doi:10.1007/s10796-023-10390-w · Cited by 107 · see citing works
“…ICTs also constitute a threat to society (Ahmed et al., 2022), and digital spaces have been accused of providing a fertile ground for fake news campaigns and online hate speech (Aanestad et al., 2021; Castaño-Pulgarín et al., 2021), while exacerbating societal polarization (Törnberg, 2022).”
Haroon, M., Wojcieszak, M., Chhabra, A., Liu, X., Mohapatra, P., & Shafiq, Z. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2213020120 · Cited by 64 · see citing works
Brüggemann, M., & Meyer, H. (2023) · Communication Theory · doi:10.1093/ct/qtad012 · Cited by 60 · see citing works
Schneider, N. (2024) · University of California Press eBooks · doi:10.1525/9780520393950 · Cited by 41 · see citing works
Network segregation and the propagation of misinformation
Stein, J., Keuschnigg, M., & van de Rijt, A. (2023) · Scientific Reports · doi:10.1038/s41598-022-26913-5 · Cited by 38 · see citing works
“…platform’s act of exposing individuals to ideologi-cally opposed alters by diversifying users’ networks could backfire at a psychological level—e.g., inciting further polarization of attitudes 10,37,38 —our result suggests that it would lower the prevalence of misinformation in the system overall.”
BackgroundAlgorithmic media use and algorithm literacy: An integrative literature review
Gagrčin, E., Naab, T. K., & Grub, M. F. (2024) · New Media & Society · doi:10.1177/14614448241291137 · Cited by 67 · see citing works
Esau, K., Choucair, T. D. S., Vilkins, S., Svegaard, S. F. K., Bruns, A., O’Connor-Farfan, K. S., & Lubicz-Zaorski, C. (2024) · Information Communication & Society · doi:10.1080/1369118x.2024.2413127 · Cited by 45 · see citing works
“…the lack of a direct linkage between selective exposure and selective avoidance (Weeks et al., 2016), and incidental exposure to a more diverse range of news sources for active social media users (Fletcher & Nielsen, 2018) – though such increased exposure can itself be problematic (Törnberg, 2022).”
Falkenberg, M., Zollo, F., Quattrociocchi, W., Pfeffer, J., & Baronchelli, A. (2024) · Nature Communications · doi:10.1038/s41467-024-53868-0 · Cited by 43 · see citing works
“However, the richness and availability of social media data has made it an invaluable forum for studying the mechanisms of polarization 15,29 – 31 , depolarization 32,33 , its evolution over time 16,34 , and for testing potential interventions and countermeasures 18 .”
The WEIRD governance of fact-checking and the politics of content moderation
Vinhas, O., & Bastos, M. (2023) · New Media & Society · doi:10.1177/14614448231213942 · Cited by 32 · see citing works
“As such, rather than offering opportunity for common ground, successful cross-cutting fact-checking posts are likely to fuel affective polarization between opposing groups (Tornberg, 2022) and be weaponized through the selective and partisan sharing of fact-checks (Shin and Thorson, 2017).”
Embedding Democratic Values into Social Media AIs via Societal Objective Functions
Jia, C., Lam, M. S., Mai, M. C., Hancock, J. T., & Bernstein, M. S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3641002 · Cited by 31 · see citing works
“by repeated exposure to individuals outside of local networks, which might cause local conflicts to align on global partisan lines [84].”
BackgroundInfluentialKnowledge through social networks: Accuracy, error, and polarisation
Hahn, U., Merdes, C. J., & von Sydow, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0294815 · Cited by 10 · see citing works
The Algorithmic Management of Polarization and Violence on Social MediaPreprint
Stray, J., Iyer, R., & Larrauri, H. P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4429558 · Cited by 44 · see citing works
Understanding anti-immigration sentiment spreading on Twitter
Nasuto, A., & Rowe, F. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307917 · Cited by 30 · see citing works
Li, M., Suk, J., Zhang, Y., Pevehouse, J. C. W., Sun, Y., Kwon, H., Lian, R., Wang, R., Dong, X., & Shah, D. V. (2024) · New Media & Society · doi:10.1177/14614448241285562 · Cited by 24 · see citing works
“Twitter demonstrates homophilic tendencies in user interactions (Cinelli et al., 2021; Yarchi et al., 2021; Zhang et al., 2022a) through partisan sorting to create like-minded networks (Törnberg, 2022).”
Keepin' it Reel: Investigating how Short Videos on TikTok and Instagram Reels Influence View Change
Molem, A., Makri, S., & McKay, D. (2024) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3627508.3638341 · Cited by 31 · see citing works
Data in movement: the social movement society in the age of datafication
Milan, S., & Beraldo, D. (2024) · Social movement studies · doi:10.1080/14742837.2024.2331550 · Cited by 22 · see citing works
“…and re-arrange information for users, allegedly augmenting political polarization, either by trapping users in echo chambers of homogeneous content, or by exposing them to positions outside of their bubble and reinforcing partisanship as the result of group interaction dynamics (Törnberg, 2022).”
BackgroundA theoretical framework for polarization as the gradual fragmentation of a divided society
Bliuc, A., Betts, J. M., Vergani, M., Bouguettaya, A., & Cristea, M. (2024) · Communications Psychology · doi:10.1038/s44271-024-00125-1 · Cited by 32 · see citing works
“Differences in ideological position become the driver of the process of sorting the public into opposing sides along the lines of con fl icting multiple social identities; a process further enhanced by difference, distrust, and disdain for the opposing group 24,25 .”
BackgroundMethodInfluentialShockwavelike Behavior across Social Media
Manrique, P. D., Huo, F. Y., Oud, S. E., et al. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.237401 · Cited by 19 · see citing works
“Following prior social science and physics studies [11 – 14 ,16 ,17 , 32, 33 ], each individual i Master equations for the number n s ð t Þ of aggregates of size s , are for s > 1 and s ¼ 1 , respectively, where the first term(s) on each right-hand side are fusion, the next are total fission, and…”
MethodThe Origins of Elected Strongmen
Frantz, E., Kendall-Taylor, A., & Wright, J. (2024) · doi:10.1093/oso/9780198888079.001.0001 · Cited by 20 · see citing works
Motivated information acquisition and social norm formation
Dimant, E., Galeotti, F., & Villeval, M. C. (2024) · European Economic Review · doi:10.1016/j.euroecorev.2024.104778 · Cited by 18 · see citing works
Unraveling polarization: Insights into individual and collective dynamics
Bar-On, K. K., Dimant, E., Yphtach Lelkes, B., & Rand, D. G. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae426 · Cited by 19 · see citing works
Stray, J. (2023) · Analytics · doi:10.3390/analytics2020020 · Cited by 17 · see citing works
Threatening experts: Correlates of viewing scientists as a social threat
Chinn, S., Hasell, A., Roden, J., & Zichettella, B. (2023) · Public Understanding of Science · doi:10.1177/09636625231183115 · Cited by 18 · see citing works
“Individuals who seek information about science in social media are likely exposed to diverse political discourses and may rely on partisan group cues to evaluate and form attitudes toward scientists (Törnberg, 2022).”
BackgroundChavalarias, D., Bouchaud, P., & Panahi, M. (2024) · Journal of Artificial Societies and Social Simulation · doi:10.18564/jasss.5203 · Cited by 17 · see citing works
Algorithm-Mediated Social Learning in Online Social NetworksPreprint
Brady, W. J., Jackson, J. C., Lindström, B., & Crockett, M. J. (2023) · doi:10.31219/osf.io/yw5ah · Cited by 18 · see citing works
Internet-based micro-identities as a driver of societal disintegration
Kossowska, M., Kłodkowski, P., Siewierska-Chmaj, A., Guinote, A., Kessels, U., Moyano, M., & Strömbäck, J. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02441-z · Cited by 22 · see citing works
Multispecies Cohesion: Humans, Machinery, AI, and Beyond
Huo, F. Y., Manrique, P. D., & Johnson, N. F. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.247401 · Cited by 16 · see citing works
CSR Communication and the Polarization of Public Discourses: Introduction to the Special Issue
Schoeneborn, D., Golob, U., Trittin‐Ulbrich, H., Wenzel, M., & O’Connor, A. (2024) · Management Communication Quarterly · doi:10.1177/08933189241268503 · Cited by 16 · see citing works
“Especially Especially the latter observation seems characteristic for communication on and through social media: It is the affective side of such communication that seems to contribute to the emergence of diametrically opposed positions (T¨ornberg, 2022).”
Contextures of hate: Towards a systems theory of hate communication on social media platforms
Barth, N., Wagner, E., Raab, P., & Wiegärtner, B. (2023) · The Communication Review · doi:10.1080/10714421.2023.2208513 · Cited by 20 · see citing works
“Media scholars have not only criticized the echo chamber thesis that Habermas is referring to (Bruns, 2019a, 2019b; Törnberg, 2022) but have also argued for a more differentiated concept of digital publics.”
BackgroundGovernable Spaces: Democratic Design for Online Life
Schneider, N. (2023) · University of California Press eBooks · doi:10.1525/luminos.181 · Cited by 15 · see citing works
The Echo chamber-driven Polarization on Social Media
Kim, L. (2023) · Journal of Student Research · doi:10.47611/jsr.v12i4.2274 · Cited by 15 · see citing works
The Plot Thickens: A Sociology of Conspiracy Theories
Rao, H., & Greve, H. R. (2024) · Annual Review of Sociology · doi:10.1146/annurev-soc-030222-031142 · Cited by 16 · see citing works
Othering in politics: how affective polarization undermines democratic philia?
Erdoğan, E., & Uyan-Semerci, P. (2025) · Frontiers in Political Science · doi:10.3389/fpos.2025.1553889 · Cited by 18 · see citing works
“Emotional responses to political content on social media, such as anger and disgust, reinforce affective polarization, particularly in polarized digital environments (Druckman et al., 2021; Iyengar, 2019; Törnberg, 2022).”
AI and human autonomy: a literature review
Catena, E. (2026) · AI and Ethics · doi:10.1007/s43681-025-00958-4 · Cited by 20 · see citing works
“…media platforms are in line with debates on the socio-political implications of cases like Facebook emotional contagion experiment, the Cambridge Analytica scandal, and related phenomena such as filter bubbles and eco-chambers (although the latter have been recently questioned; see e.g., [122]).”
de Jong, J. F. (2024) · Communications Psychology · doi:10.1038/s44271-023-00051-8 · Cited by 14 · see citing works
Wuestenenk, N., van Tubergen, F., & Stark, T. (2023) · New Media & Society · doi:10.1177/14614448231172966 · Cited by 14 · see citing works
“This, in turn, leads to a greater difference in opinions between groups along political lines ( ideological polarization ) (Sunstein, 2018; Terren et al., 2021) and more hostile intergroup relations ( affective polarization ) (Törnberg, 2022).”
Countering the “Climate Cult” – Framing Cascades in Far-Right Digital Networks
Knüpfer, C. B., & Hoffmann, M. (2024) · Political Communication · doi:10.1080/10584609.2024.2332762 · Cited by 12 · see citing works
“This enables forms of ideological sorting around shared perceptions and monitoring of opposing out-groups (Törnberg, 2022).”
White supremacists anonymous: how digital media emotionally energize far-right movements
Törnberg, A., & Törnberg, P. (2023) · Journal of Information Technology & Politics · doi:10.1080/19331681.2023.2262459 · Cited by 12 · see citing works
“But social media not only offer tactical tools for these movements; they also serve as communitarian space for negotiations and identity-formation within movements (Törnberg & Törnberg, 2022).”
Self-citationManhart, S. (2023) · Springer eBooks · doi:10.1007/978-3-658-41104-6_12 · Cited by 12 · see citing works
Zhu, Z., Zhang, N., Ding, M., & Chen, L. (2023) · Information Systems Journal · doi:10.1111/isj.12486 · Cited by 10 · see citing works
“On the other hand, some studies suggest that algorithms that avoid exposure to opposite ideas could reduce SMIP because digital media may drive affective SMIP by connecting us to views and positions outside our local bubble (Törnberg, 2022).”
BackgroundStitch incoming: political engagement and aggression on TikTok
McLaughlin, B., Cloudy, J., Hunter, J. A., & Potter, B. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2354436 · Cited by 12 · see citing works
“For example, in face-to-face communities, where people know each other well, individuals are more likely to overcome political di ff erences and fi nd ways to work together and co-exist (Törnberg 2022).”
Emotion processes in social and political contexts: the case of affective polarisation
Scherer, K. R. (2024) · Cognition & Emotion · doi:10.1080/02699931.2024.2361754 · Cited by 10 · see citing works
“Interdisciplinary theorising and research on a ff ective polarisation is all the more urgent and important given the increasing role of digital social media in the process of social organisation and political behaviour (Harel et al., 2020; Törnberg, 2022; Yarchi et al., 2021).”
BackgroundAffective polarization is uniformly distributed across American States
Holliday, D. E., Yphtach Lelkes, B., & Westwood, S. J. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae310 · Cited by 10 · see citing works
Conceptualizing new forms of volunteering in urban governance
Fenger, M., Karré, P. M., & Blok, S. N. (2023) · Urban Governance · doi:10.1016/j.ugj.2023.09.002 · Cited by 9 · see citing works
Neyazi, T. A., Kuru, O., & Mukerjee, S. (2024) · The International Journal of Press/Politics · doi:10.1177/19401612241242431 · Cited by 10 · see citing works
“Research has shown that digital platforms can amplify partisan sentiments, potentially leading to heightened polarization (Törnberg 2022).”
Agents of Discord: Modeling the Impact of Political Bots on Opinion Polarization in Social Networks
Lu, H.-C., & Lee, H. (2024) · Social Science Computer Review · doi:10.1177/08944393241270382 · Cited by 9 · see citing works
“This observation aligns with previous research suggesting that microconvergence can lead to macropolarization (T¨ornberg, 2022).”
Charting multidimensional ideological polarization across demographic groups in the USA
Ojer, J., Carcamo, D. P., Pastor‐Satorras, R., & Starnini, M. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02251-0 · Cited by 14 · see citing works
“Interestingly, online interactions on social media are segregated by demographic factors 31 and contribute to the rise of affective polarization through partisan sorting 32 .”
Zhang, Y., Yue, Z., Yang, X., Chen, F., & Kwak, N. (2022) · New Media & Society · doi:10.1177/14614448221137324 · Cited by 9 · see citing works
“…shows that QAnon accounts promote shared values and beliefs to bond with in-group members, speaking of how social media provide a hotbed for members of a group, marginal or not, to network with each other, seek gratification, and build group cohesion (Benkler, 2006; Castells, 2007; Törnberg, 2022).”
Tweet Trajectory and AMPS-based Contextual Cues can Help Users Identify Misinformation
Zade, H., Woodruff, M., Johnson, E., Stanley, M., Zhou, Z., Huynh, M. T., Acheson, A. E., Hsieh, G., & Starbird, K. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579536 · Cited by 9 · see citing works
De-sounding echo chambers: Simulation-based analysis of polarization dynamics in social networks
Donkers, T., & Ziegler, J. (2023) · Online Social Networks and Media · doi:10.1016/j.osnem.2023.100275 · Cited by 14 · see citing works
Su, Y., Luo, C., & Borah, P. (2024) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmae010 · Cited by 10 · see citing works
Política, uso de medios y polarización afectiva en España
Crespo-Martínez, I., Melero-López, I., Mora-Rodríguez, A., & Rojo-Martínez, J.-M. (2024) · Revista Mediterránea de Comunicación · doi:10.14198/medcom.26681 · Cited by 8 · see citing works
Modeling echo chamber effects in signed networks
Vendeville, A., & Diaz-Diaz, F. (2025) · Physical review. E · doi:10.1103/physreve.111.024302 · Cited by 8 · see citing works
How out-group animosity can shape partisan divisions: A model of affective polarization
Nettasinghe, B., Percus, A. G., & Lerman, K. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf082 · Cited by 9 · see citing works
“…as one possible explanation of why even traits that are historically nonpartisan, such as the preferred choice of beverage, leisure activity, vocabulary, etc., can start to diverge along party lines when the prevalence of that trait in the opposite party is emphasized in the digital news (13).”
Gagrčin, E., Ohme, J., Buttgereit, L., & Grünewald, F. (2023) · Media and Communication · doi:10.17645/mac.v11i3.6641 · Cited by 9 · see citing works
“, 2018), leading to an increase in the number of topics linked to politics but a decrease in their diversity (Törnberg, 2022).”
BackgroundInfluentialMcKay, D., Makri, S., Lopez, M. G., Porlezza, C., MacFarlane, A., Cooper, G., & Missaoui, S. (2024) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24885 · Cited by 8 · see citing works
“Although evidence of filter bubbles is lacking and echo chambers limited, polarization remains a perennial problem, which may have an alternative causal mechanism (Törnberg, 2022).”
Background“It Became No Man’s Land”: The Burden of Moderating Online Harassment in Newswork
Dodds, T., Geboers, M., & Boukes, M. (2026) · Journalism Practice · doi:10.1080/17512786.2024.2387664 · Cited by 8 · see citing works
“We know that social media are not “merely arenas for rational deliberation” but rather boast a space for social identity formation and “symbolic displays of solidarity with allies and difference from outgroups” (Törnberg 2022, 10).”
Media anti-populism and political parallelism in Latin America
Hallin, D. C., Kitzberger, P., & Pons, M. P. (2025) · Annals of the International Communication Association · doi:10.1093/anncom/wlaf003 · Cited by 10 · see citing works
Sun, M., Gao, X., Jing, X., et al. (2025) · Energy · doi:10.1016/j.energy.2025.136975 · Cited by 7 · see citing works
Lee, Y., Min, B., & Han, S. (2026) · Cities · doi:10.1016/j.cities.2025.106766 · Cited by 7 · see citing works
Individuals, Collectives, and Individuals in Collectives: The Ineliminable Role of Dependence
Hahn, U. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231198479 · Cited by 7 · see citing works
“…Glance, 2005; Bail et al., 2018; Bakshy et al., 2015; Barberá et al., 2015; Del Valle & Borge Bravo, 2018; Himelboim et al., 2013; Lee et al., 2014; Törnberg, 2022; Tucker et al., 2017), conspiracy theories, and fake news (Del Vicario et al., 2016; Lazer et al., 2018; Scheufele & Krause, 2019;…”
BackgroundSakieh, Y. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01175-9 · Cited by 7 · see citing works
“In the case of climate change, recommendation systems have a major impact on formation of attitudes in society toward the issue and climate change itself has fundamental implications in economic and political domains (Törnberg 2022).”
Affective and interactional polarization align across countriesPreprint
Falkenberg, M., Zollo, F., Quattrociocchi, W., Pfeffer, J., & Baronchelli, A. (2023) · doi:10.31234/osf.io/ngsb6 · Cited by 11 · see citing works
Martínez‐España, R., Fernández-Pedauyé, J., de Lucía, J. G. P., Martínez, J. M. R., Bakdid, K., & Escribano, J. J. G. (2024) · IEEE Access · doi:10.1109/access.2024.3431999 · Cited by 6 · see citing works
“Contrary to expectations, what social networks promote is not isolation, but interaction outside the local bubble which tempers partisan classification, generating an adverse emotional reaction that reinforces our identity [25].”
Background(De)Polarization Entrepreneurs? Think Tanks and Pernicious Polarization in Central Europe
Jezierska, K., Krizsán, A., & Sörbom, A. (2024) · Perspectives on Politics · doi:10.1017/s1537592724001397 · Cited by 6 · see citing works
“…with signi fi cant resources for employing polarizing strategies, are political leaders (Enyedi 2016; Lorch 2021; McCoy, Rahman, and Somer 2018; Mietzner 2021; Rahman 2019; Somer, McCoy, and Luke 2021; Svolik 2019; Tworzecki 2019; Vachu-dova 2019) and media outlets (e.g., Prior 2013; Törnberg 2022).”
How social media users adopt the toxic behaviors of ingroup and outgroup accounts
Zoizner, A., & Levy, A. (2025) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmaf018 · Cited by 7 · see citing works
Distorted flesh – Towards a non-speculative concept of social pathology
Sík, D. (2024) · Philosophy & Social Criticism · doi:10.1177/01914537241244820 · Cited by 6 · see citing works
“Besides the actors ’ discursive efforts, algorithms are also responsible for amplifying the dominant expressions, which often results in polarization and the undermining of meaningful discussions (T¨ornberg 2022).”
From Echo Chambers to Digital Campfires
Törnberg, A., & Törnberg, P. (2024) · Routledge eBooks · doi:10.4324/9781003472148-5 · Cited by 6 · see citing works
Self-citationPoliticized and Paranoid? Assessing Attitudinal Predictors of Alternative News Consumption
Puschmann, C., Stier, S., Zerrer, P., & Rauxloh, H. (2024) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2024.2383411 · Cited by 6 · see citing works
“…is debated intensely within political communication what consequences, if any, such a shift may have on political polarization, the fragmentation of public opinion, and the role of media in shaping public discourse (Nelson & Webster, 2017; Steppat et al., 2022; Stier et al., 2023; Törnberg, 2022).”
Raemdonck, N. V., Picone, I., & Pierson, J. (2025) · Social Media + Society · doi:10.1177/20563051251319066 · Cited by 12 · see citing works
“We know that exposure to dissenting views can increase “partisan sorting,” forcing people to choose a side (Bail et al., 2018; Törnberg, 2022).”
Nettasinghe, B., Rao, A., Jiang, B., Percus, A. G., & Lerman, K. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714935 · Cited by 6 · see citing works
Bassi, D., Maggini, M. J., Vieira, R. L. R., & Pereira-Fariña, M. (2024) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams64316.2024.10883781 · Cited by 5 · see citing works
Aytaç, S. E., Irmak, S., & Söylemez-Karakoç, B. (2025) · South European Society & Politics · doi:10.1080/13608746.2025.2597287 · Cited by 8 · see citing works
Chinn, S., & Hasell, A. (2023) · Health Communication · doi:10.1080/10410236.2023.2207284 · Cited by 7 · see citing works
“In sum, individuals using social media to seek information about health, science, and news may be exposed to diverse information from experts and political elites, but that information may not be accurate and is likely to activate partisan norms and identities (Törnberg, 2022).”
BackgroundHow to divide people with things: division entrepreneurs, wedges, and the Delta Smelt controversy
Scoville, C. (2024) · American Journal of Cultural Sociology · doi:10.1057/s41290-024-00212-0 · Cited by 5 · see citing works
Partisan winds: Group-level polarization and issue-framing propel attitudes about local wind farms
Marcos, A., Hartmann, P., Bessette, D., & Árvai, J. (2024) · Energy Research & Social Science · doi:10.1016/j.erss.2024.103848 · Cited by 6 · see citing works
Neumann, R., Pfetsch, B., Hutter, S., Koschut, S., Schieferdecker, D., & Specht, J. (2023) · Social Indicators Research · doi:10.1007/s11205-023-03163-0 · Cited by 5 · see citing works
You Are a Bot! – Studying the Development of Bot Accusations on Twitter
Assenmacher, D., Fröhling, L., & Wagner, C. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31301 · Cited by 5 · see citing works
Depolarizing within the Comfort of Your Party: Experimental Evidence from Online Workshops
Blair, R., Gottlieb, J., Schenk, M., & Woods, C. (2025) · Political Communication · doi:10.1080/10584609.2025.2469097 · Cited by 6 · see citing works
“Perhaps counterintuitively, Törnberg (2022) argues that the fact that people increasingly interact online only serves to exacerbate polarization induced by this physical sorting: this is because people who are physically proximate tend to have more cross-cutting attributes than groups who choose to…”
Neyazi, T. A., Ee, T. K., & Kuru, O. (2025) · Social Science Computer Review · doi:10.1177/08944393251362247 · Cited by 4 · see citing works
“…as social media algorithms prioritize negative and polarized content that is more likely to create engagement, this can further magnify partisan divides as voters become more entrenched in their views and less tolerant of opposing perspectives (Iyengar & Krupenkin, 2018; T¨ornberg, 2022).”
Social Cohesion in (Post‐)Conflict and Divided Societies: Recent Advances and Future Directions
Figueiredo, A., Baysu, G., Uluğ, Ö. M., et al. (2025) · European Journal of Social Psychology · doi:10.1002/ejsp.70024 · Cited by 4 · see citing works
Primig, F. (2025) · Publizistik · doi:10.1007/s11616-025-00887-6 · Cited by 5 · see citing works
“Digital-ization, however, has introduced new potential for reshuffling and contesting these norms, and it has increased the instances in which alternatives collide; as some argue a key driver for societal polarization (Bor and Petersen 2021; Rosa 2013; Törnberg 2022).”
The Creative and Cultural Industries towards Sustainability and Recovery
Komorowski, M., & Lewis, J. (2023) · MDPI eBooks · doi:10.3390/books978-3-0365-8133-0 · Cited by 4 · see citing works
Evangelista, R. (2023) · Aurora Revista de Arte Mídia e Política · doi:10.23925/1982-6672.2022v15i47p112-133 · Cited by 5 · see citing works
Macková, A., Novotná, M., Čejková, L., & Hrbková, L. (2023) · Journal of Information Technology & Politics · doi:10.1080/19331681.2023.2202650 · Cited by 7 · see citing works
“This leads to more polarization (Törnberg, 2022) because opinions on SNS become less important as opinions and become markers of identity (Törnberg, Andersson, Lindgren, Banisch, & Mahmoud, 2021).”
BackgroundCultural Evolution, Disinformation, and Social Division
Bentley, R. A., Horne, B. D., Borycz, J., Carrignon, S., Shteynberg, G., Vidiella, B., Valverde, S., & O’Brien, M. J. (2023) · Adaptive Behavior · doi:10.1177/10597123231186432 · Cited by 6 · see citing works
Castaños, S. P., & Núñez, J. M. R. (2023) · Social Sciences · doi:10.3390/socsci12090504 · Cited by 4 · see citing works
Klein, O., Trenz, H.-J., & Hesse, N. (2024) · Journal of Information Technology & Politics · doi:10.1080/19331681.2024.2308886 · Cited by 4 · see citing works
“Applied to the new field of social media conflict research, we can extrapolate the general expectation that conflict increases in terms of polarization through the selective exposure to content combined with the fragmentation of relatively homogenous user communities (Törnberg, 2022).”
BackgroundOnline-Informationen: Individuelle und gesellschaftliche Effekte
Schweiger, W. (2025) · Springer eBooks · doi:10.1007/978-3-658-18017-1_41-1 · Cited by 3 · see citing works
Civil society IV: Democratic solidarity and the non-civil scaffolding of the civil sphere
Watts, G., & Horgan, M. (2025) · Filozofija i drustvo · doi:10.2298/fid2501011w · Cited by 3 · see citing works
Flood the Zone with Shit: Algorithmic Domination in the Modern Republic
Maynor, J. W. (2025) · Social Sciences · doi:10.3390/socsci14060391 · Cited by 3 · see citing works
From activism to polarization: temporal dynamics of #MeToo politicization on Twitter/X
Suk, J., Zhang, Y., Wang, R., Yang, D., Levin, S. H., Seo, J. M., & Dong, X. (2025) · Information Communication & Society · doi:10.1080/1369118x.2025.2581014 · Cited by 4 · see citing works
“Platforms promote ideological sorting by letting users choose the ‘right’ side (Törnberg, 2022).”
Hakkarainen, J., & Savolainen, L. (2025) · Information Communication & Society · doi:10.1080/1369118x.2025.2595663 · Cited by 3 · see citing works
“It should be noted that the ‘filter bubble’ effect is debated in the literature (Törnberg, 2022).”
Derivative Media: How Wall Street Devours Culture
deWaard, A. (2024) · University of California Press eBooks · doi:10.1525/luminos.197 · Cited by 3 · see citing works
- Authors
- Törnberg, P.
- Affiliations
- University of Neuchâtel (CH)
- Corresponding author
- Törnberg, Petter
- ORCID
- Törnberg, Petter
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2022-10-10
- Volume
- 119
- Issue
- 42
- Pages
- e2207159119-e2207159119
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC9586282
- OpenAlex
- W4304098849
- Semantic Scholar
- ab19b3fdc1f28d16e01c7ca9fdd1ba217428a298
- Cited by
- 288
- References
- 110
- Citations by year
- 2026: 76 · 2025: 75 · 2024: 79 · 2023: 52 · 2022: 6
- FWCI
- 81.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.201S.006)
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Keywords
- affective polarization, digital media, partisan sorting, opinion dynamics, nonlocal interactions, local interactions, political polarization, social media
- MeSH terms
- Attitude, Humans, Politics, Internet
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