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Cited by 60Climate Change Communication and PerceptionMisinformation and Its ImpactsOpinion Dynamics and Social Influence

Credibility of climate change denial in social media

Open access
Samantray, Abhishek, Pin, Paolo·2019·Palgrave Communications
DOI 10.1057/s41599-019-0344-4
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Abstract

Public perception about the reality of climate change has remained polarized and propagation of fake information on social media can be a potential cause. Homophily in communication, the tendency of people to communicate with others having similar beliefs, is understood to lead to the formation of echo chambers which reinforce individual beliefs and fuel further increase in polarization. Quite surprisingly, in an empirical analysis of the effect of homophily in communication on the level of polarization using evidence from Twitter conversations on the climate change topic during 2007–2017, we find that evolution of homophily over time negatively affects the evolution of polarization in the long run. Among various information about climate change to which people are exposed to, they are more likely to be influenced by information that have higher credibility. Therefore, we study a model of polarization of beliefs in social networks that accounts for credibility of propagating information in addition to homophily in communication. We find that polarization can not increase with increase in homophily in communication unless information propagating fake beliefs has minimal credibility. We therefore infer from the empirical results that anti-climate change tweets are largely not credible.

AI snapshot

Written from the paper’s full text.

TL;DR

Using Twitter climate-change conversations from 2007–2017 and a belief-updating model, the paper finds that homophily in communication negatively affects polarization in the long run. The authors infer that anti-climate-change tweets are largely not credible because polarization would not decrease with homophily unless fake information has minimal credibility.

Key findings

  • In monthly Twitter data, homophily Granger-causes polarization with a negative effect (test statistic 12.3, p < 0.01), while polarization does not Granger-cause homophily (test statistic 4.0, not significant at conventional levels).
  • The model predicts that polarization cannot increase with homophily unless information propagating fake beliefs has minimal credibility; if fake information has zero credibility, homophily can negatively affect polarization.
  • The authors conclude from the empirical negative effect that anti-climate-change tweets are “largely not credible” to the broader society.
  • The dataset contains 14,353,859 unique tweets and 3,595,205 unique users; only 42 users were classified as having no opinion and were dropped.

Methods

  • Empirical design: collected tweets from 2007–2017 containing “climate change,” “#climatechange,” “global warming,” or “#globalwarming” via Twitter’s public advanced search; retweets were excluded, and communication was measured through mentions/replies.
  • Opinion and sentiment: VADER scored tweet sentiment from −1 to +1; a support vector classifier trained on manually annotated stance data predicted whether tweets were in favor of or against “climate change is a real concern,” with about 72% tweet-level accuracy.
  • Analysis: monthly homophily and polarization time series were tested for cointegration, modeled with a vector error correction model, and assessed with Toda–Yamamoto-modified Granger causality tests; a separate Bayesian belief-updating model jointly analyzed homophily and information credibility.

Limitations

  • The paper states that Granger causality does not fully establish causality and does not exclude an unobserved variable driving both homophily and polarization.
  • The paper states that the model assumes rational Bayesian updating and notes that alternative behavioral assumptions could also explain a negative effect of homophily on polarization.
  • Evident from the text: tweet-level opinion classification accuracy is about 72%, and homophily is inferred from mentioning patterns as a proxy for retweeting/following patterns because retweet/follow data were not available.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1057/s41599-019-0344-4

Cites

70 of 70 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

10 of 39 cited sources checked · 2 supported · 2 flagged · accuracy not yet measured

  • Premisecitation

    Homophily among liberals and conservatives in political blogs links leads to echo chambers.

    “For example, homophily among liberals and conservatives in political blogs links (Adamic and Glance, 2005), i.e., the tendency of liberal and conservative blogs to link primarily within their separate communities, leads to echo chambers (Jasny et al., 2015;Garrett, 2009;Flaxman et al., 2016), and hence beliefs can be polarized due to such exposure to selective information (Bakshy et al., 2015).”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites Adamic and Glance, 2005 source
    • cites Jasny et al., 2015 source
    • cites Garrett, 2009 source

      The Discussion (page unavailable) says there is little evidence that people use the Internet to create echo chambers devoid of other viewpoints, contrary to the claim that selective linking leads to echo chambers. Garrett studies online news choices, not links between political blogs; the Discussion treats politically segmented services as a possible future threat, not an established outcome. (LLM, conf=0.82)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
  • Premisecitation

    Homophily in communication is a key tendency of users in information propagation media like Twitter and other platforms: people tend to communicate with others who hold similar beliefs.

    “Homophily in communication is a key tendency of users in information propagation media like Twitter and other platforms: people tend to communicate with others who hold similar beliefs (Barberá, 2015;Conover et al., 2011).”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Barberá, 2015 source
    • cites Conover et al., 2011 source
  • Interpretationcitation

    The negative effects of echo chambers in social networks might be overstated.

    “This also reaffirms the fact that the negative effects of echo chambers in social networks, which are known to arise due to high levels of homophily in communication, might be overstated”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites Warner, 2010 source
    • cites Bail et al., 2018 source

      The Abstract reports that exposure to opposing views increased polarization among Republican Twitter users and did not significantly reduce it among Democrats. The Discussion and Conclusion says such interventions might be ineffective or counterproductive, which supports the possibility that the harms attributed to echo chambers are overstated. But the study did not directly measure the effects of echo chambers, and its authors caution against generalizing beyond frequent, party-identified Twitter users. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites Flaxman et al., 2016 source
  • Premisecitation

    Echo chambers have been observed for climate change discussions on social media.

    “Previously echo chambers have been observed for climate change discussions on social media (Williams et al., 2015;McCright and Dunlap, 2010).”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites Williams et al., 2015 source
    • cites McCright and Dunlap, 2010 source
  • Findingown data

    Augmented Dickey-Fuller test and Phillips-Ouliaris test suggest that polarization and homophily are cointegrated, i.e., they are bound in a long term equilibrium relationship such that they mean-revert whenever there are deviations away from the equilibrium.

    “Augmented Dickey-Fuller test and Phillips-Ouliaris test suggest that polarization and homophily are cointegrated, i.e., they are bound in a long term equilibrium relationship such that they mean-revert whenever there are deviations away from the equilibrium.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Homophily in mentioning patterns reveals homophily in retweeting and following patterns, for which the paper does not have data.
  • Classified as probably not a substantial claim. 27
  • May combine two claims. 20

  • Classified as probably not a substantial claim.

    Premisecitation

    Homophily in communication is understood to lead to the formation of echo chambers which reinforce individual beliefs and fuel further increase in polarization.

    “Homophily in communication, the tendency of people to communicate with others having similar beliefs, is understood to lead to the formation of echo chambers which reinforce individual beliefs and fuel further increase in polarization.”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites Bessi et al., 2016

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Jasny et al., 2015 source
    • cites Garrett, 2009 source

      The Discussion says people prefer opinion-reinforcing news, but also says there is little evidence that they use the Internet to create echo chambers devoid of other viewpoints. The Conclusion says concern about increasing fragmentation appears overstated. The paper therefore does not support the claimed progression from like-minded communication to echo chambers and greater polarization. (LLM, conf=0.88)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Communicating only with people who share similar ideology or opinion restricts beliefs and prevents learning the truth.

    “Communicating only with people who share similar ideology or opinion restricts beliefs and prevents learning the truth”
    Quote in the paperSubstantial claim1 of 3 sources checked
    • cites Madsen et al., 2018 source

      The Introduction defines echo chambers as communication among like-minded people that reinforces pre-existing beliefs. The Simulations and Results section reports that simulated agents in echo chambers can maintain objectively mistaken beliefs, but this is a model finding, not a demonstration that communicating with like-minded people invariably prevents real people from learning the truth. No page numbers were provided in our copy. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites Acemoglu et al., 2011 source
    • cites Acemoglu et al., 2010 source

  • May combine two claims.

    Premisecitation

    Previous studies reveal that homophily of the aggregate network measured using retweeting and mentioning tend to be the same, although mentioning is more volatile at the group levels.

    “Previous studies (Lietz et al., 2014;Aragón et al., 2013) reveal that homophily of the aggregate network measured using retweeting and mentioning tend to be same, although mentioning is more volatile at the group (sub network with a particular ideology) levels.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Lietz et al., 2014 source

      The Introduction (page unavailable) says that homophily is weakest for mentioning and that its variance across parties is largest, supporting the claim about greater group-level variation. It does not establish that aggregate homophily is the same for retweeting and mentioning; its comparison instead emphasizes a difference between them. (LLM, conf=0.86)

      Experimental — accuracy not yet measured
    • cites Aragón et al., 2013 source

  • May combine two claims.

    Premisecitation

    Individual rumors are debunked in due time by fact-checking measures, but public perception about anthropogenic climate change has remained controversial for a long time.

    “Individual rumors are being debunked in due time by fact checking measures (Zhao et al., 2015;Ciampaglia et al., 2015), however, public perception about some issues like human induced (anthropogenic) climate change have remained controversial for a long time (Tvinnereim et al., 2017).”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites Zhao et al., 2015 source
    • cites Ciampaglia et al., 2015 source

      The Introduction says that vandalism, distortions, and outright lies on Wikipedia are frequently repaired within minutes. That supports timely correction of some false information, but not the broader claim that individual rumors are being debunked by fact-checking measures. The Discussion describes computational fact-checking as a potential way to mitigate misinformation, not an established record of rumor debunking. The climate-change clause is attributed to a different citation. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
    • cites Tvinnereim et al., 2017 source

      The Introduction (page unavailable) reports differing views on whether climate change is mostly human-caused or natural, supporting the point that public perceptions of anthropogenic climate change are divided. It does not establish that this division has persisted for a long time. The fact-checking clause is attributed to other citations, not to this paper. (LLM, conf=0.78)

      Experimental — accuracy not yet measured

  • Classified as probably not a substantial claim.

    Premisecitation

    Sentiment and opinion contained in a message are two different characteristics of the message.

    “Therefore sentiment and opinion contained in a message are two different characteristics of the message.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites Sobhani et al., 2016 source

      Section 3 labels tweets for sentiment separately from the paper’s annotation of whether an opinion concerns the target, another entity, or no entity. This supports distinguishing sentiment from the target or presence of an opinion, but the paper does not establish the broader claim that sentiment and opinion themselves are two distinct characteristics. (LLM, conf=0.79)

      Experimental — accuracy not yet measured
    • cites Mohammad et al., 2017 source

  • May combine two claims.

    Findingreasoning

    The belief updating model predicts that a marginal increase in homophily always leads to an increase in polarization, except when fake information has no credibility.

    “The model predicts that marginal increase in homophily always leads to increase in polarization expect for the only case when fake information has no credibility.”
    • Also stated in: § Discussion · plain
      “There is zero probability for such an increase if fake information that propagates in the social network has zero credibility”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Agents in the model are rational and rely solely on previously held beliefs and beliefs expressed by neighbors in their immediate social networks to update beliefs.

  • May combine two claims.

    Findingcitation

    A large number of tweets have neutral or close to neutral sentiment, thereby increasing the mass around 0 and decreasing the intensity of bimodality in the distribution of EAB.

    “A large number of tweets have neutral or close to neutral sentiment, thereby increasing the mass around 0 and decreasing the intensity of bimodality in the distribution of EAB.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The evolutions of the two curves are not independent, and polarization tends to decrease at times when homophily increases.

    “It appears that the evolutions of the two curves are not independent, and polarization tends to decrease at times when homophily increases.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Homophily in mentioning patterns reveals homophily in retweeting and following patterns, for which the paper does not have data.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    There are 14,353,859 unique tweets and 3,595,205 unique users in the dataset.

    “There are a total of 14,353,859 unique tweets (without counting retweets values) and 3,595,205 unique users in the dataset.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    Only 42 users were classified as having no opinion, and were dropped from the dataset.

    “Only 42 users were classified as having no opinion, and were dropped from the dataset.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Only homophily Granger-causes polarization with a negative effect, and the causality in the other direction is absent, i.e., polarization does not Granger cause homophily.

    “The conclusion that emerges is that only homophily Granger-causes polarization with a negative effect, and the causality in the other direction is absent, i.e., polarization does not Granger cause homophily.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Homophily in mentioning patterns reveals homophily in retweeting and following patterns, for which the paper does not have data.

  • May combine two claims.

    Findingown data

    In the VEC model, the lagged homophily covariates negatively affect polarization in the long term with high statistical significance and the lagged effects of polarization on homophily are not significant.

    “Supplementary Table 4 contains details of the parameter estimates of VEC model: the lagged homophily covariates negatively affect polarization in the long term with high statistical significance and the lagged effects of polarization on homophily are not significant.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Homophily in mentioning patterns reveals homophily in retweeting and following patterns, for which the paper does not have data.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    Beliefs can be emotion-adjusted as done in the empirical analysis to include the emotional content of the belief; this does not change the nature of predictions of the model.

    “The beliefs can be emotion-adjusted as well, as is done in the empirical analysis (see Methods section), to include the emotional content of the belief; this does not change the nature of predictions of our model.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    These estimates do not form a conclusion about the direction of causality between homophily and polarization.

    “These estimates however do not form a conclusion about the direction of causality between homophily and polarization.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationown data

    This empirical result that homophily negatively affects polarization is counterintuitive to the discussions made previously about the nature of homophily and polarization.

    “This empirical result that homophily negatively affects polarization is counterintuitive to the discussions made previously about the nature of homophily and polarization.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Public perception and understanding of climate change has remained polarized.

    “Public perception about the reality of climate change has remained polarized”
    • Also stated in: § Abstract · plain
      “Public understanding of climate change has remained polarized (Moore et al., 2019;McCright et al., 2014;Hamilton, 2011).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Moore et al., 2019 source
    • cites McCright et al., 2014 source
    • cites Hamilton, 2011 source

  • Classified as probably not a substantial claim.

    Premisenone given

    In social networks, polarization of beliefs and homophily in communication tend to be highly correlated.

    “In social networks, polarization of beliefs (existence of large groups of people with opposing beliefs) and homophily in communication (communication among people having same beliefs) tend to be highly correlated.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Premisereasoning

    Credibility is a dimension of information independent of the veracity of the information.

    “Credibility is a dimension of information which is independent of veracity of the information.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Exposure to different kinds of information on media, politicization of climate change, and exposure to fake information on social media are potential reasons that can contribute to polarization of beliefs about the reality of climate change.

    “Various factors like exposure to different kind of information on the media (Iyengar and Massey, 2019;Williams, 2016), politicization of climate change (McCright and Dunlap, 2011), and exposure to fake information on social media platforms are potential reasons that can contribute to polarization of beliefs about the reality of climate change.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Iyengar and Massey, 2019 source
    • cites Williams, 2016 source
    • cites McCright and Dunlap, 2011 source

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    The alternative behavioral assumption that agents gain utility from knowing the opposite party is misguided may not scale to a wider population.

    “Although such a behavioral assumption can lead to an alternative way to explain negative effect of homophily on polarization, we believe that such assumption may not scale to a wider population.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisecitation

    Any reasonable measure of polarization must be global in nature.

    “This is in line with Esteban and Ray (1994), according to whom any reasonable measure of polarization must be global in nature.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Esteban and Ray, 1994 source

  • May combine two claims.

    Interpretationreasoning

    Social media messages on platforms like Twitter are not a potential concern because, although many sources propagate fake beliefs about climate change, the collective credibility of such sources is negligible.

    “In this study, we showed that social media messages on platform like Twitter is not a potential concern because although there are many sources propagating fake beliefs regarding climate change, the collective credibility of such sources is negligible.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    Credibility of communicated information is independent from the preciseness with which the information source believes the information.

    “Lastly, credibility of a communicated information is independent from the preciseness with which the information source believes the information.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Discussions and information propagation on Twitter has potential to shape beliefs at a large scale.

    “We believe discussions and information propagation that happens on such a platform has potential to shape beliefs at a large scale.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    When homophily in communication occurs in two large sections of society with differing beliefs, it enhances polarization.

    “When such homophily in communication happens in two large sections of the society with differing beliefs, it enhances polarization (Esteban and Ray, 1994).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Esteban and Ray, 1994 source

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    In a Bayesian update of beliefs, how a communicated message affects the beliefs of listeners depends on the belief expressed in the communicated message, the precision or credibility of the message, prior beliefs held by individual listeners and the precision of such prior beliefs of listeners.

    “In a Bayesian update of beliefs, how a communicated message affects the beliefs of listeners of the message depends on the belief expressed in the communicated message, the precision (or credibility) of the message, prior beliefs held by individual listeners and the precision of such prior beliefs of listeners.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    Human behavior is highly heterogeneous and there may exist different ways in which people update beliefs in the real world.

    “Human behavior, in general, is highly heterogeneous and there may exist different ways in which people update beliefs in real world.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Negative consequences that may arise due to the spread of fake information in social networks depend on information credibility, thereby making it a factor that can induce polarization.

    “Hence, negative consequences that may arise due to the spread of fake information in social networks depend on information credibility, thereby making it a factor that can induce polarization.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    If groups are of insignificant size, such as isolated individuals, they do not contribute to polarization.

    “If the groups are of insignificant size (e.g., isolated individuals), they do not contribute to polarization.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisereasoning

    Polarization of beliefs can be affected not only by homophily in communication but also by the credibility of information that propagates on Twitter.

    “Polarization of beliefs can be affected not only by homophily in communication among people, but also by the credibility of information that propagates on Twitter.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    Granger causation of homophily on polarization does not fully establish its causality.

    “It is important to note that Granger causation of homophily on polarization does not fully establish its causality.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Because the empirical analysis of climate change discussions on Twitter found a negative effect of homophily on polarization, tweets expressing anti-climate change beliefs are largely not credible to the broader society.

    “Since we observed a negative effect of homophily on polarization in the empirical analysis of climate change discussions on the Twitter platform, we conclude that tweets expressing anti-climate change beliefs are largely not credible to the broader society.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Agents in the model are rational and rely solely on previously held beliefs and beliefs expressed by neighbors in their immediate social networks to update beliefs.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given

    The collection of tweets does not violate any ethical standards and consists of only publicly available data.

    “This collection of tweets does not violate any ethical standards and consists of only publicly available data.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    The polarization measure describes a different phenomenon than the homophily measure.

    “It is easy to see that the polarization measure describes a different phenomenon than the homophily measure.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductnone given

    The presence of an unobserved variable driving both homophily and polarization is highly unlikely within the framework of the model and the variables used for empirical analysis.

    “Although the presence of such an unobserved variable is highly unlikely within the framework of our model and the variables used for empirical analysis”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The data is complete in the sense that it contains all original tweets that fulfill the search criteria.

    “The data is complete in the sense that it contains all original tweets that fulfill the search criteria.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisereasoning

    Retweets do not contain new opinions and are mere repetitions or broadcast of opinions expressed in original tweets, whereas mentions contain explicit referencing of other users and hence convey exchange of opinions targeted towards users being mentioned.

    “This is because retweets do not contain new opinions and are mere repetitions or broadcast of opinions expressed in original tweets, whereas mentions contain explicit referencing of other users and hence convey exchange of opinions targeted towards users being mentioned.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Fake information regarding climate change can pose a collective hurdle for society at large if such information becomes highly credible among users of the platform.

    “Fake information regarding such an important issue as climate change can pose a collective hurdle for the society at large if such information becomes highly credible among users of the platform.”
    Quote in the paperNot a substantial claim
38 of 38

  • The authors state this as an assumption.

    Homophily in mentioning patterns reveals homophily in retweeting and following patterns, for which the paper does not have data.

    “Hence, based on such studies, we assume that homophily in mentioning patterns reveals the homophily in retweeting (and following) patterns, for which we do not have the data.”

  • The authors state this as an assumption.

    Each agent receives information about a topic from the same number k of speakers.

    “In our model, we consider a social network with each agent receiving information about a topic (e.g., the reality of climate change) from the same number, k, of speakers (for simplicity).”

  • The authors state this as an assumption.

    At a given time there are two types of agents, informed and misinformed, and the social network has a homophily coefficient hk with respect to this property.

    “At a given time t, we assume there are two types of agents-θ l , θ rinformed and misinformed, and the social network has a homophily coefficient of h k with respect to this property: h speakers of an agent of type θ i , i ∈ {l, r}, are of the same θ i type.”

  • The authors state this as an assumption.

    Prior beliefs of informed and misinformed agents derive from normal distributions N(θ0, δl^-1) and N(θ0+ξ, δr^-1), with ξ strictly positive, ensuring that the misinformed type are on average farther from truth than the informed type.

    “We assume that the prior beliefs of informed and misinformed agents derive from distributions N ðθ 0 ; δ À1 l Þ and N ðθ 0 þ ξ; δ À1 r Þ, respectively, with a strictly positive value for ξ, in order to ensure that the misinformed type are, on average, farther away from truth compared to the informed type.”

  • The authors state this as an assumption.

    Each agent communicates a realization of his belief using a message during the period between t and t+1, and all agents update their prior beliefs at t+1 after incorporating beliefs expressed in their speakers' messages.

    “We assume each agent communicates a particular realization of his belief using a message (e.g., via a tweet) during the period between t and a future time t + 1, and all agents update their prior beliefs at t + 1 after incorporating beliefs about the fundamental expressed in their speakers' messages.”

  • The authors state this as an assumption.

    A message from a speaker is an independent noisy signal about the speaker's realized belief, with the degree of noise conditional on the type of speaker.

    “Mathematically, a message from a speaker is modeled as an independent noisy signal about the speaker's realized belief, with the degree of the noise (or uncertainty) being conditional on the type of speaker.”

  • The authors state this as an assumption.

    Messages from informed agents carry a positive credibility.

    “We assume that the messages from informed agents carry a positive credibility.(In our context of the reality about climate change, this is a reasonable assumption).”

  • The authors state this as an assumption.

    True and fake information about reality, arising from informed and misinformed speakers respectively, propagate with different credibilities in the social network.

    “Since credibility of a communicated message is the precision of belief expressed in the message, in our model, true and fake information about the reality, arising from informed and misinformed speakers, respectively, propagate with different credibilities in the social network.”

  • The authors state this as an assumption.

    Agents in the model are rational and rely solely on previously held beliefs and beliefs expressed by neighbors in their immediate social networks to update beliefs.

    “Agents in the model are rational (Madsen et al., 2018) and purely rely on previously held beliefs and beliefs expressed by neighbors in their immediate social networks to arrive at new beliefs.”

  • The paper relies on this without support.

    The baseline model with Bayesian updating is ideally representative of the behavior of a larger population.

    “In this context, we believe that by imposing a Bayesian updating criteria we have studied the expected outcomes in a baseline model, and is (ideally) representative of the behavior of a larger population.”

  • The paper relies on this without support.

    Information from a source that is not credible is least likely to affect or change an individual's belief.

    “It is intuitive since information from a source which is not credible is naturally least likely to affect or change the belief of an individual.”

  • The paper relies on this without support.

    Credibility is a dimension of information independent of the veracity of information.

    “Credibility is a dimension of information independent of the veracity of information.”
12 of 12

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.

  • All users' tweets versus most users' tweets in one category

    Minor

    The paper says each user's tweets were predicted into a single category, but then says this was true for only most users. These statements disagree about whether any users had tweets in multiple categories.

1 of 1

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Authors
Samantray, A., & Pin, P.
Affiliations
  • IMT School for Advanced Studies Lucca (IT)
  • University of Siena (IT)
Corresponding author
Samantray, Abhishek
ORCID
Venue
Palgrave Communications
Publisher
Springer Science and Business Media LLC
Published
2019-10-29
Volume
5
Issue
1
ISSN
2055-1045 (ISSN-L)
DOI
10.1057/s41599-019-0344-4
OpenAlex
W2982537810
Cited by
60
References
63
Citations by year
2026: 3 · 2025: 13 · 2024: 12 · 2023: 8 · 2022: 12 · 2021: 7 · 2020: 5
FWCI
12.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Humanities and Social Sciences Communications (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Use Siena air (University of Siena) (submittedVersion, cc-by-nc)
Topics
  • Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
Keywords
climate change denial, social media, polarization, echo chambers, fake news, information credibility, belief propagation, Twitter

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-11-02.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-11-02.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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