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Cited by 25Hate Speech and Cyberbullying DetectionMisinformation and Its ImpactsSocial Media and Politics

Hatemongers ride on echo chambers to escalate hate speech diffusion

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Goel, Vasu, Sahnan, Dhruv, Dutta, Subhabrata, Bandhakavi, Anil, Chakraborty, Tanmoy·2023·PNAS Nexus
DOI 10.1093/pnasnexus/pgad041PMC PMC10011877
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Abstract

Recent years have witnessed a swelling rise of hateful and abusive content over online social networks. While detection and moderation of hate speech have been the early go-to countermeasures, the solution requires a deeper exploration of the dynamics of hate generation and propagation. We analyze more than 32 million posts from over 6.8 million users across three popular online social networks to investigate the interrelations between hateful behavior, information dissemination, and polarized organization mediated by echo chambers. We find that hatemongers play a more crucial role in governing the spread of information compared to singled-out hateful content. This observation holds for both the growth of information cascades as well as the conglomeration of hateful actors. Dissection of the core-wise distribution of these networks points towards the fact that hateful users acquire a more well-connected position in the social network and often flock together to build up information cascades. We observe that this cohesion is far from mere organized behavior; instead, in these networks, hatemongers dominate the echo chambers-groups of users actively align themselves to specific ideological positions. The observed dominance of hateful users to inflate information cascades is primarily via user interactions amplified within these echo chambers. We conclude our study with a cautionary note that popularity-based recommendation of content is susceptible to be exploited by hatemongers given their potential to escalate content popularity via echo-chambered interactions.

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  • La formación de cámaras de eco refuerza las dinámicas de agrupación de usuarios en comunidades que amplifican mutuamente sus perspectivas, normalizando y perpetuando el discurso de odio.

    “Además, la formación de cámaras de eco refuerza estas dinámicas, ya que los usuarios se agrupan en comunidades que amplifican mutuamente sus perspectivas, normalizando y perpetuando el discurso de odio (Goel et al., 2023).”

    From Redes de odio y contranarrativas en Twitter sobre el ataque a iglesias católicas en Algeciras: Dinámicas de conectividad, influencia y polarización (2026) · cites this paper as Goel et al., 2023

    The Introduction (page unavailable) describes echo chambers as groups of ideologically aligned users and reports that membership amplifies the reach of highly hateful users. The paper does not establish that this process normalizes hate speech; the Conclusion (page unavailable) also cautions that the observed relationships do not establish causality. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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  1. [1]Waheed H, Anjum M, Rehman M, Khawaja A. 2017. Investigation of user behavior on social networking sites. PLoS ONE. 12(2): e0169693. 28151963 PMC5289435
  2. [2]Simchon A, Brady WJ, Van Bavel JJ. 2022. Troll and divide: the language of online polarization. PNAS Nexus. 1(1): pgac019. 36712799 PMC9802075
  3. [4]Asimovic N, Nagler J, Bonneau R, Tucker JA. 2021. Testing the effects of facebook usage in an ethnically polarized setting. Proc Natl Acad Sci USA. 118(25): e2022819118. 10.1073/pnas.2022819118 PMC8237683 34131075 · DOI · Paper page
  4. [5]Guess AM, Barberá P, Munzert S, Yang J. 2021. The consequences of online partisan media. Proc Natl Acad Sci USA. 118(14): e2013464118. 10.1073/pnas.2013464118 PMC8040813 33782116 · DOI · Paper page
  5. [7]Awan I, Zempi I. 2016. The affinity between online and offline anti-muslim hate crime: dynamics and impacts. Aggress Violent Behav. 27: 1– 8. 10.1016/j.avb.2016.02.001 · DOI · Paper page
  6. [8]Bilewicz M, Soral W. 2020. Hate speech epidemic. the dynamic effects of derogatory language on intergroup relations and political radicalization. Polit Psychol. 41(S1): 3– 33. 10.1111/pops.12670 · DOI · Paper page
  7. [10]Waqas A, Salminen J, Jung SG, Almerekhi H, Jansen BJ. 2019. Mapping online hate: a scientometric analysis on research trends and hotspots in research on online hate. PLoS ONE. 14(9): e0222194– e0222194. 31557227 PMC6763199
  8. [11]2020. A systematized review. Sage Open. 10(4): 2158244020973022.
  9. [12]Cinelli M, et al 2021. Dynamics of online hate and misinformation. Sci Rep. 11(1): 22083. 34764344 PMC8585974
  10. [13]Velásquez N, et al 2021. Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms. Sci Rep. 11(1): 11549. 34131158 PMC8206165
  11. [14]Uyheng J, Carley KM. 2021. Characterizing network dynamics of online hate communities around the COVID-19 pandemic. Appl Netw Sci. 6(1): 20. 33718589 PMC7934993
  12. [15]Masud S, et al 2021. Hate is the new infodemic: a topic-aware modeling of hate speech diffusion on twitter. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). Chania, Greece: IEEE. p. 504–515.
  13. [16]Johnson NF, et al 2019. Hidden resilience and adaptive dynamics of the global online hate ecology. Nature. 573(7773): 261– 265. 31435010
  14. [17]Banks J. 2010. Regulating hate speech online. Int Rev Law Comput Technol. 24(3): 233– 239. 10.1080/13600869.2010.522323 · DOI
  15. [18]Reynolds L. 2018. Mainstreamed online extremism demands a radical new response. Nat Hum Behav. 2(4): 237– 238. 30936542
  16. [19]Rahmawan D, Wibowo KA. 2021. A meta-analysis of hate speech in Indonesia: the yielding of academic discourse to the discourse of authority. Pac Journal Rev. 27(1/2): 251– 267.
  17. [20]Hatzipanagos R. 2018. How online hate speech is fueling real life violence. The Washington Post. www.washingtonpost.com/nation/2018/11/30/how-online-hate-speech-is-fueling-real-life-violence/. 29573165
  18. [21]Winter A. 2019. Online hate: from the far-right to the ‘alt-right’and from the margins to the mainstream. In Online othering: Exploring digital violence and discrimination on the web. Springer. p. 39–63.
  19. [22]Peters MA. 2020. Limiting the capacity for hate: hate speech, hate groups and the philosophy of hate. Educ Philos Theory. 54(4): 1– 6.
  20. [23]Huszár F, et al 2022. Algorithmic amplification of politics on twitter. Proc Natl Acad Sci USA. 119(1): e2025334119. PMC8740571 34934011
  21. [24]Youngblood M. 2020. Extremist ideology as a complex contagion: the spread of far-right radicalization in the united states between 2005 and 2017. Hum Soc Sci Commun. 7(1): 1– 10.
  22. [25]Habib H, Srinivasan P, Nithyanand R. 2022. Making a radical misogynist: how online social engagement with the manosphere influences traits of radicalization, arXiv, arXiv:2202.08805, preprint: not peer reviewed. · arXiv
  23. [27]Artime O, d’Andrea V, Gallotti R, Sacco PL, De Domenico M. 2020. Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms. Sci Rep. 10(1): 14392. 32873821 PMC7462854
  24. [28]Jhaver S, Bruckman A, Gilbert E. 2019. Does transparency in moderation really matter?: user behavior after content removal explanations on reddit. Proc ACM Hum-Comput Interact. 3: 1– 27.34322658
  25. [29]Gillani N, Yuan A, Saveski M, Vosoughi S, Roy D. 2018. Me, my echo chamber, and I: introspection on social media polarization. In Proceedings of the 2018 World Wide Web Conference. Palais des congrès de Lyon: ACM. p. 823–831.
  26. [30]Quattrociocchi W. 2017. Inside the echo chamber. Sci Am. 316(4): 60– 63. 28296846
  27. [31]Garimella K, De Francisci Morales G, Gionis A, Mathioudakis M. 2018. Political discourse on social media: echo chambers, gatekeepers, and the price of bipartisanship. In: Proceedings of the 2018 World Wide Web Conference. Palais des congrès de Lyon: ACM. p. 913–922.
  28. [33]Sasahara K, et al 2019. On the inevitability of online echo chambers, arXiv, arXiv:1905.03919, preprint: not peer reviewed. · arXiv
  29. [34]Choi D, Chun S, Oh H, Han J, Kwon TT. 2020. Rumor propagation is amplified by echo chambers in social media. Sci Rep. 10(1): 310. 31941980 PMC6962360
  30. [36]Zannettou S, et al 2018. What is gab: a bastion of free speech or an alt-right echo chamber. Companion Proceedings of the The Web Conference 2018. Palais des congrès de Lyon: ACM.
  31. [37]Davidson T, Warmsley D, Macy M, Weber I. 2017. Automated hate speech detection and the problem of offensive language. In: ICWSM. Vol. 11. Montreal, Canada: AAAI.
  32. [38]Qian J, Wang H, ElSherief M, Yan X. 2021. Lifelong learning of hate speech classification on social media. In: NAACL. ACL. p. 2304–2314.
  33. [39]Waseem Z, Hovy D. 2016. Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: Proceedings of the NAACL Student Research Workshop. San Diego, CA: ACL. p. 88–93.
  34. [40]Founta AM, et al 2019. A unified deep learning architecture for abuse detection. In: WebSci. Boston, MA: ACM. p. 105–114.
  35. [41]Mathew B, Dutt R, Goyal P, Mukherjee A. 2019. Spread of hate speech in online social media. In: Proceedings of the 10th ACM Conference on Web Science, WebSci ’19; New York, NY, USA. Association for Computing Machinery. 10.1145/3292522.3326034. · DOI
  36. [42]Shin J, Jian L, Driscoll K, Bar F. 2017. Political rumoring on twitter during the 2012 US presidential election: rumor diffusion and correction. New Media Soc. 19(8): 1214– 1235.
  37. [43]Bakshy E, Hofman JM, Mason WA, Watts DJ. 2011. Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM ’11; New York, NY, USA. Association for Computing Machinery. p. 65–74.
  38. [44]Angelov D. 2020. Top2vec: distributed representations of topics. CoRR, arXiv, arXiv:2008.09470, preprint: not peer reviewed. · arXiv
  39. [45]Suh B, Hong L, Pirolli P, Chi EH. 2010. Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In 2010 IEEE Second International Conference on Social Computing. Minneapolis, MN: IEEE. p. 177–18. 10.1109/SocialCom.2010.33. · DOI
  40. [47]Mathew B, et al 2020. Hate begets hate: a temporal study of hate speech. Proc ACM Hum-Comput Interact. 4(CSCW2): 1– 24.
  41. [48]Cinelli M, et al 2021. Dynamics of online hate and misinformation. Sci Rep. 11(1): 1– 12. 34764344 PMC8585974
  42. [49]Atari M, et al 2021. Morally homogeneous networks and radicalism. Soc Psychol Personal Sci. 13(6): 19485506211059329.
  43. [50]Cer D, et al 2018. Universal sentence encoder for English. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Brussels, Belgium: ACL. p. 169–174.

Cited by

Sorted by citations

Authors
Goel, V., Sahnan, D., Dutta, S., Bandhakavi, A., & Chakraborty, T.
Affiliations
  • Indraprastha Institute of Information Technology Delhi (IN)
  • Jadavpur University (IN)
  • Logically, 9-12 Long Lane , London, EC1A 9HA
  • Indian Institute of Technology Delhi (IN)
Corresponding author
  • Goel, Vasu
  • Chakraborty, Tanmoy
ORCID
Venue
PNAS Nexus
Publisher
Oxford University Press (OUP)
Published
2023-02-08
Volume
2
Issue
3
Pages
pgad041-pgad041
ISSN
2752-6542 (ISSN-L)
DOI
10.1093/pnasnexus/pgad041
PMC
PMC10011877
OpenAlex
W4319594275
Cited by
25
References
3
Citations by year
2026: 8 · 2025: 11 · 2024: 4 · 2023: 2
FWCI
2.36
Type
journal article
Language
en
Open access
Licence
Locations
  • PNAS Nexus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Ramanujan Fellowship (SB/S2/RJN-073/2017)
  • Science and Engineering Research Board
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Social Media and Politics
Keywords
hate speech, echo chambers, information cascades, social networking sites, polarization, information dissemination, core-periphery structure, recommendation algorithms, content popularity, user interaction

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