CoChat
Sign Up
Cited by 6Social Media and PoliticsMisinformation and Its ImpactsPopulism, Right-Wing Movements

Few voices, strong echo: Measuring follower homogeneity of politicians’ Twitter accounts

Open access
Rusche, Felix·2024·New Media & Society
DOI 10.1177/14614448221099860
Open this paper in CoChat — free account

Abstract

Politicians have discovered Twitter as a tool for political communication. If information provided by politicians is circulated in ideologically segregated user networks, political polarization may be fostered. Using network information on all 1.78 million unique followers of German Members of Parliament by October 2018, follower homogeneity across politicians and parties is measured. While the overall homogeneity is low, politicians of the AfD—a right-wing populist party—stand out with very homogeneous follower networks. These are largely driven by a small group of strongly committed partisans that make up around 7% of the party’s but around 55–75% of the average AfD politician’s followers. The findings add to the literature by showing potentially unequal distributions of network segregation on Twitter. Furthermore, they suggest that small groups of active users can multiply their influence online, which has important implications for future research on echo chambers and other online phenomena.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/14614448221099860

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Highly active and ideologically driven users are often overrepresented in trace data, which can skew findings.

    “Highly active and ideologically driven users are often overrepresented in trace data, which can skew findings [110].”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [110]

    The Follower types section (p. 10) says profiles following multiple politicians receive more weight in its network measure, while studies weighting users equally may overlook their influence. The Conclusion (p. 21) similarly says a few partisan users can have disproportionate influence. The paper does not establish that highly active, ideologically driven users are often overrepresented in trace data or that such overrepresentation skews findings generally. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Machine learning-based bot detection offers a way to mitigate this issue.

    “Machine learning-based bot detection, as proposed by Rusche [110], and implemented by Choi et al. [100] and Gaumont et al. [120], offers a way to mitigate this issue.”

    From A systematic review of echo chamber research: comparative analysis of conceptualizations, operationalizations, and varying outcomes (2025) · cites this paper as [110]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 51

  1. Ausserhofer J and Maireder A (2013) National politics on Twitter. Information, Communication & Society 16(3): 291-314.
  2. Barberá P, Casas A, Nagler J, et al. (2019) Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data. American Political Science Review 113(4): 883-901.
  3. Barberá P, Jost JT, Nagler J, et al. (2015) Tweeting from left to right: is online political communication more than an echo chamber? Psychological Science 26(10): 1531-1542.
  4. Bond R and Messing S (2015) Quantifying social media's political space: estimating ideology from publicly revealed preferences on Facebook. American Political Science Review 109(1): 62-78.
  5. Boutyline A and Willer R (2017) The social structure of political echo chambers: variation in ideological homophily in online networks. Political Psychology 38(3): 551-569.
  6. Breiman L (2001) Random forests. Machine Learning 45(1): 5-32.
  7. Bright J (2018) Explaining the emergence of political fragmentation on social media: the role of ideology and extremism. Journal of Computer-Mediated Communication 23(1): 17-33.
  8. Broscheid A and Gschwend T (2005) Zur statistischen analyse von vollerhebungen. Politische Vierteljahresschrift 46(1): O16-O26.
  9. Chen L, Weber I and Okulicz-Kozaryn A (2014) U.S. religious landscape on Twitter. In: Aiello LM and McFarland D (eds) Social Informatics: 6th International Conference, Socinfo 2014 , Barcelona, Spain, November 11-13, 2014. Proceedings (Lecture Notes in Computer Science). Cham: Springer, pp. 544-560.
  10. Chiocchetti P (2016) The German radical left: a success story. In: The Radical Left Party Family in Western Europe, 1989-2015 . London: Routledge, pp. 81-122. https://www.taylorfrancis.com/ chapters/mono/10.4324/9781315622057-13/german-radical-left-success-story-paolo-chiocc hetti?context=ubx&refId=33659d1f-cfbf-4b89-ac93-5f43027843f2. · DOI
  11. Colleoni E, Rozza A and Arvidsson A (2014) Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication 64(2): 317-332.
  12. Dubois E and Blank G (2018) The echo chamber is overstated: the moderating effect of political interest and diverse media. Information, Communication & Society 21(5): 729-745.
  13. Dvir-Gvirsman S (2017) Media audience homophily: Partisan websites, audience identity and polarization processes. New Media & Society 19(7): 1072-1091.
  14. Eady G, Nagler J, Guess A, et al. (2019) How many people live in political bubbles on social media? Evidence from linked survey and Twitter data. SAGE Open 9(1): 1-21.
  15. Fawzi N, Obermaier M and Reinemann C (2017) Germany: is the populism laggard catching up? In: Aalberg T, Esser F, Reinemann C, et al. (eds) Populist Political Communication in Europe . New York: Taylor & Francis, pp. 111-126.
  16. Gallwitz F and Kreil M (2021) The rise and fall of 'social bot' research. SSRN scholarly paper, Social Science Research Network. Available at: https://papers.ssrn.com/abstract=2356199
  17. Gentzkow M and Shapiro JM (2011) Ideological segregation online and offline. The Quarterly Journal of Economics 126(4): 1799-1839.
  18. Grabowicz P, Babaei M, Kulshrestha J, et al. (2016) The road to popularity: the dilution of growing audience on Twitter. Proceedings of the International AAAI Conference on Web and Social Media 10: 567-570.
  19. Guerrero-Solé F (2018) Interactive behavior in political discussions on Twitter: politicians, media, and citizens' patterns of interaction in the 2015 and 2016 electoral campaigns in Spain. Social Media + Society 4(4): 1-16.
  20. Halberstam Y and Knight B (2016) Homophily, group size, and the diffusion of political information in social networks: evidence from Twitter. Journal of Public Economics 143: 73-88.
  21. Hu L, Kearney MW and Frisby CM (2021) Tweeting and retweeting: gender discrepancies in discursive political engagement and influence on Twitter. Journal of Gender Studies . Epub ahead of print 25 July. DOI:10.1080/09589236.2021.1995340. · DOI
  22. Johnson TJ, Bichard SL and Zhang W (2009) Communication communities or 'cyberghettos?' A path analysis model examining factors that explain selective exposure to blogs. Journal of Computer-Mediated Communication 15(1): 60-82.
  23. Jungherr A (2014) The logic of political coverage on Twitter: temporal dynamics and content. Journal of Communication 64(2): 239-259.
  24. Jungherr A (2016) Twitter use in election campaigns: a systematic literature review. Journal of Information Technology & Politics 13(1): 72-91.
  25. Jürgens P and Stark B (2022) Mapping exposure diversity: the divergent effects of algorithmic curation on news consumption. Journal of Communication . Epub ahead of print 16 March. DOI: 10.1093/joc/jqac009. · DOI
  26. Keller TR and Kleinen-von Königslöw K (2018) Followers, spread the message! Predicting the success of Swiss politicians on Facebook and Twitter. Social Media + Society 4(1): 1-11.
  27. Küpper B, Berghan W, Zick A, et al. (2020) Volkes Stimme-antidemokratische und populistische Einstellungen. In: Zick A and Küpper B (eds) Die Geforderte Mitte: Rechtsextreme Und Demokratiegefährdende Einstellungen in Deutschland , vol. 21. Bonn: Friedrich-EbertStiftung, pp. 43-74.
  28. Kwak H, Chun H and Moon S (2011) Fragile online relationship: a first look at unfollow dynamics in Twitter. In: Proceedings of the SIGCHI conference on human factors in computing systems , pp. 1091-1100. Available at: https://citeseerx.ist.psu.edu/viewdoc/download?doi=1 0.1.1.228.6826&rep=rep1&type=pdf
  29. Lawrence E, Sides J and Farrell H (2010) Self-segregation or deliberation? Blog readership, participation, and polarization in American politics. Perspectives on Politics 8(1): 141-157.
  30. Lee JK, Choi J, Kim C, et al. (2014) Social media, network heterogeneity, and opinion polarization. Journal of Communication 64(4): 702-722.
  31. Lewandowsky M (2015) Eine rechtspopulistische Protestpartei? Die AfD in der öffentlichen und politikwissenschaftlichen Debatte. Zeitschrift Für Politikwissenschaft 25(1): 119-134.
  32. Liaw A and Wiener M (2002) Classification and regression by randomforest. R News 2(3): 18-22.
  33. McPherson M, Smith-Lovin L and Cook JM (2001) Birds of a feather: homophily in social networks. Annual Review of Sociology 27(1): 415-444.
  34. Maity SK, Gajula R and Mukherjee A (2018) Why did they# unfollow me? Early detection of follower loss on Twitter. In: Proceedings of the 2018 ACM conference on supporting groupwork , pp. 127-131. Available at: https://arxiv.org/abs/1802.05091 · arXiv
  35. Mullainathan S and Shleifer A (2005) The market for news. American Economic Review 95(4): 1031-1053.
  36. Neudert L, Kollanyi B and Howard PN (2017) Junk News and Bots during the German Parliamentary Election: What are German Voters Sharing over Twitter? Data Memo 2017 . Oxford: Project on Computational Propaganda.
  37. Saalfeld T and Schoen H (2015) Party politics and electoral behaviour. In: Colvin S and Taplin M (eds) The Routledge Handbook of German Politics & Culture (Routledge Handbooks). New York: Routledge, pp. 105-118.
  38. Scharkow M, Mangold F, Stier S, et al. (2020) How social network sites and other online intermediaries increase exposure to news. Proceedings of the National Academy of Sciences 117(6): 2761-2763.
  39. Schmitt-Beck R, van Deth JW and Staudt A (2017) Die AfD nach der rechtspopulistischen Wende. Zeitschrift Für Politikwissenschaft 27(3): 273-303.
  40. Siegel AA, Nagler J, Bonneau R, et al. (2021) Tweeting beyond tahrir: ideological diversity and political intolerance in Egyptian Twitter networks. World Politics 73(2): 243-274.
  41. Silva BC and Proksch SO (2021) Fake it 'til you make it: a natural experiment to identify European politicians' benefit from Twitter bots. American Political Science Review 115(1): 316-322.
  42. Stier S, Bleier A, Lietz H, et al. (2018) Election campaigning on social media: politicians, audiences, and the mediation of political communication on Facebook and Twitter. Political Communication 35(1): 50-74.
  43. Stroud NJ (2018) Selective exposure theories. In: Kenski K and Hall Jamieson K (eds) The Oxford Handbook of Political Communication . Oxford: Oxford University Press, pp. 1-19.
  44. Sunstein CR (2001) Republic.com . Princeton, NJ: Princeton University Press.
  45. Takhteyev Y, Gruzd A and Wellman B (2012) Geography of Twitter networks. Social Networks 34(1): 73-81.
  46. Vaccari C, Valeriani A, Barberá P, et al. (2016) Of echo chambers and contrarian clubs: exposure to political disagreement among German and Italian users of Twitter. Social Media + Society 2(3): 1-24.
  47. Van Alstyne M and Brynjolfsson E (2005) Global Village or Cyber-Balkans? Modeling and measuring the integration of electronic communities. Management Science 51(6): 851-868.
  48. Van Vliet L, Törnberg P and Uitermark J (2021) Political systems and political networks: the structure of parliamentarians' retweet networks in 19 countries. International Journal of Communication 15: 21.
  49. Webster JG and Ksiazek TB (2012) The dynamics of audience fragmentation: public attention in an age of digital media. Journal of Communication 62(1): 39-56.
  50. Yardi S and Boyd D (2010) Dynamic debates: an analysis of group polarization over time on Twitter. Bulletin of Science, Technology & Society 30(5): 316-327.
  51. Zittel T and Gschwend T (2008) Individualised constituency campaigns in mixed-member electoral systems: candidates in the 2005 German elections. West European Politics 31(5): 9781003.

Cited by

Sorted by citations

Authors
Rusche, F.
Affiliations
University of Mannheim (DE)
Corresponding author
Rusche, Felix
ORCID
Rusche, Felix
Venue
New Media & Society
Publisher
SAGE Publications
Published
2022-06-20
Volume
26
Issue
6
Pages
3514-3540
ISSN
  • 1461-4448 (ISSN-L)
  • 1461-7315
DOI
10.1177/14614448221099860
OpenAlex
W4283156682
Cited by
6
References
7
Citations by year
2026: 1 · 2025: 4 · 2023: 1
FWCI
1.66
Type
journal article
Language
en
Open access
Licence
Locations
  • New Media & Society (publishedVersion, cc-by)
  • MADOC (University of Mannheim) (submittedVersion, cc-by)
Funders
  • Deutsche Forschungsgemeinschaft
  • Studienstiftung des Deutschen Volkes
  • Universität Mannheim
  • Leibniz-Gemeinschaft
Topics
  • Social Sciences › Communication › Social Media and Politics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Political Science and International Relations › Populism, Right-Wing Movements
Keywords
Twitter, politicians, political polarization, echo chambers, network segregation, right-wing populism, Alternative für Deutschland, partisans, political communication, internet influence

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.

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 ›

Are you an author, or do you see a problem with this page? Request a review