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Conspiracy vs science: A large-scale analysis of online discussion cascades

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Abstract

With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments from an online social media platform, we investigate the propagation of two distinct narratives–(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable. We find that conspiracy cascades tend to propagate in a multigenerational branching process whereas science cascades are more likely to grow in a breadth-first manner. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, and is more viral and bursty than science information. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. We also find that conspiracy cascades are much more concerned with political and controversial topics. After applying machine learning models, we achieve an AUC score of nearly 90% in discriminating conspiracy from science narratives using the constructed features.

We further investigate user’s role during the growth of cascades. In contrast with previous assumption that misinformation is primarily driven by a small set of users, we find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades, imposing new challenges on the management of misinformation. Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information; Instead, we find that conspiracy information from media outlets with left or right orientation triggers smaller cascades and is less viral than information from online social media platforms (e.g., Twitter and Imgur) whose political orientations are unclear. Our study provides complementing evidence to current misinformation research and has practical policy implications to stem the propagation and mitigate the influence of misinformation online.

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Notes

  1. https://time.com/5811939/un-chief-coronavirus-misinformation.

  2. https://www.reddit.com.

  3. The raw data we used in this study are acquired from and publicly available at https://files.pushshift.io/reddit.

  4. Kolmogorov-Smirnov test is abbreviated as K-S test hereafter.

  5. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html.

  6. https://mediabiasfactcheck.com.

References

  1. Allcott, H., Gentzkow, M.: Social media and fake news in the 2016 election. J. Econ. Perspect. 31(2), 211–36 (2017)

    Article  Google Scholar 

  2. Almaatouq, A., Shmueli, E., Nouh, M., Alabdulkareem, A., Singh, V.K., Alsaleh, M., Alarifi, A., Alfaris, A., et al: If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. Int. J. Inf. Secur. 15(5), 475–491 (2016)

    Article  Google Scholar 

  3. Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J., Tiwari, M.: Global diffusion via cascading invitations: Structure, growth, and homophily. In: Proceedings of the 24th International Conference on World Wide Web. ACM, pp. 66–76 (2015)

  4. Aral, S., Eckles, D.: Protecting elections from social media manipulation. Science 365(6456), 858–861 (2019)

    Article  Google Scholar 

  5. Bessi, A., Coletto, M., Davidescu, G.A., Scala, A., Caldarelli, G., Quattrociocchi, W.: Science vs conspiracy: Collective narratives in the age of misinformation. PloS ONE 10(2), e0118093 (2015)

    Article  Google Scholar 

  6. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  7. Bovet, A., Makse, H.A.: Influence of fake news in Twitter during the 2016 US presidential election. Nat. Commun. 10(1), 7 (2019)

    Article  Google Scholar 

  8. Brady, W.J., Wills, J.A., Jost, J.T., Tucker, J.A., Van Bavel, J.J.: Emotion shapes the diffusion of moralized content in social networks. Proc. Natl. Acad. Sci. 114(28), 7313–7318 (2017)

    Article  Google Scholar 

  9. Callaway, D.S., Newman, M.E., Strogatz, S.H., Watts, D.J.: Network robustness and fragility: Percolation on random graphs. Phys. Rev. Lett. 85(25), 5468 (2000)

    Article  Google Scholar 

  10. Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted?. In: Proceedings of the 23rd International Conference on World Wide Web. ACM, pp 925–936 (2014)

  11. Cheng, J., Bernstein, M., Danescu-Niculescu-Mizil, C., Leskovec, J.: Anyone can become a troll: Causes of trolling behavior in online discussions. In: Proceedings of the 2017 ACM conference on Computer Supported Cooperative Work & Social Computing. ACM, pp 1217–1230 (2017)

  12. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H.E., Quattrociocchi, W.: The spreading of misinformation online. Proc. Natl. Acad. Sci. 113(3), 554–559 (2016)

    Article  Google Scholar 

  13. Friggeri, A., Adamic, L., Eckles, D., Cheng, J.: Rumor cascades. In: Proceedings of the International AAAI Conference on Web and Social Media. Association for the Advancement of Artificial Intelligence, vol. 8, pp 101–110 (2014)

  14. Goel, S., Anderson, A., Hofman, J., Watts, D.J.: The structural virality of online diffusion. Manag. Sci. 62(1), 180–196 (2015)

    Google Scholar 

  15. Goel, S., Watts, D.J., Goldstein, D.G.: The structure of online diffusion networks. In: Proceedings of the 13th ACM conference on Electronic Commerce. ACM, pp 623–638 (2012)

  16. Goh, K.I., Barabási, A. L.: Burstiness and memory in complex systems. EPL Europhys. Lett. 81(4), 48002 (2008)

    Article  MathSciNet  Google Scholar 

  17. Gómez, V., Kappen, H.J., Kaltenbrunner, A.: Modeling the structure and evolution of discussion cascades. In: Proceedings of the 22nd ACM conference on Hypertext and Hypermedia, pp 181–190 (2011)

  18. Gómez, V., Kappen, H.J., Litvak, N., Kaltenbrunner, A.: A likelihood-based framework for the analysis of discussion threads. World Wide Web 16 (5-6), 645–675 (2013)

    Article  Google Scholar 

  19. Gottfried, J., Shearer, E.: Americans’ online news use is closing in on TV news use, Pew Res. Cent. https://www.pewresearch.org/fact-tank/2017/09/07/americans-online-news-use-vs-tv-news-use/ (2017)

  20. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., Lazer, D.: Fake news on Twitter during the 2016 US presidential election. Science 363(6425), 374–378 (2019)

    Article  Google Scholar 

  21. Guan, L., Zhang, Y., Zhu, J.: Segmenting and characterizing adopters of e-books and paper books based on Amazon book reviews. In: Chinese National Conference on Social Media Processing. Springer, pp 85–97 (2016)

  22. Guess, A., Nagler, J., Tucker, J.: Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Sci. Adv. 5(1), eaau4586 (2019)

    Article  Google Scholar 

  23. Guess, A., Nyhan, B., Reifler, J.: Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign. Eur. Res. Counc. 9(3), 4 (2018)

    Google Scholar 

  24. Howell, L., et al.: Digital wildfires in a hyperconnected world. World Econ. Forum Rep. 3, 15–94 (2013)

    Google Scholar 

  25. Huang, Y.L., Starbird, K., Orand, M., Stanek, S.A., Pedersen, H.T.: Connected through crisis: Emotional proximity and the spread of misinformation online. In: Proceedings of the 18th ACM conference on Computer Supported Cooperative Work & Social Computing. ACM, pp 969–980 (2015)

  26. Jones, N.M., Thompson, R.R., Schetter, C.D., Silver, R.C.: Distress and rumor exposure on social media during a campus lockdown. Proc. Natl. Acad. Sci. 114(44), 11,663–11,668 (2017)

    Article  Google Scholar 

  27. Kumar, R., Mahdian, M., McGlohon, M.: Dynamics of conversations. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 553–562 (2010)

  28. Lazer, D.M., Baum, M.A., Benkler, Y., Berinsky, A.J., Greenhill, K.M., Menczer, F., Metzger, M.J., Nyhan, B., Pennycook, G., Rothschild, D., et al: The science of fake news. Science 359(6380), 1094–1096 (2018)

    Article  Google Scholar 

  29. Liang, H.: Broadcast versus viral spreading: the structure of diffusion cascades and selective sharing on social media. J. Commun. 68(3), 525–546 (2018)

    Article  Google Scholar 

  30. Medvedev, A.N., Delvenne, J.C., Lambiotte, R.: Modelling structure and predicting dynamics of discussion threads in online boards. J. Compl. Netw. 7(1), 67–82 (2019)

    Article  MathSciNet  Google Scholar 

  31. Mocanu, D., Rossi, L., Zhang, Q., Karsai, M., Quattrociocchi, W.: Collective attention in the age of (mis) information. Comput. Hum. Behav. 51, 1198–1204 (2015)

    Article  Google Scholar 

  32. Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text. Association for Computational Linguistics, pp 26–34 (2010)

  33. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word–emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Article  MathSciNet  Google Scholar 

  34. Newman, N., Fletcher, R., Kalogeropoulos, A., Levy, D., Nielsen, R.K: Reuters institute digital news report, pp 2017 (2017)

  35. Park, P.S., Blumenstock, J.E., W., M.M.: The strength of long-range ties in population-scale social networks. Science 362(6421), 1410–1413 (2018)

    Article  MathSciNet  Google Scholar 

  36. Pei, S., Muchnik, L., Tang, S., Zheng, Z., Makse, H.A.: Exploring the complex pattern of information spreading in online blog communities. PloS ONE 10(5), e0126,894 (2015)

    Article  Google Scholar 

  37. Pennycook, G., Rand, D.G.: Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc. Natl. Acad. Sci. 116(7), 2521–2526 (2019)

    Article  Google Scholar 

  38. Phan, X.H., Nguyen, C.T.: GibbsLDA++: A C/C++ implementation of Latent Dirichlet Allocation (LDA). Tech Rep (2007)

  39. Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: Identifying misinformation in microblogs. In: Proceedings of the conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp 1589–1599 (2011)

  40. Qiu, J., Li, Y., Tang, J., Lu, Z., Ye, H., Chen, B., Yang, Q., Hopcroft, J.E.: The lifecycle and cascade of Wechat social messaging groups. In: Proceedings of the 25th International Conference on World Wide Web. ACM, pp 311–320 (2016)

  41. Romero, D.M., Uzzi, B., Kleinberg, J.: Social networks under stress. In: Proceedings of the 25th International Conference on World Wide Web. ACM, pp 9–20 (2016)

  42. Ruths, D.: The misinformation machine. Science 363(6425), 348–348 (2019)

    Article  Google Scholar 

  43. Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.C., Flammini, A., Menczer, F.: The spread of low-credibility content by social bots. Nat. Commun. 9(1), 4787 (2018)

    Article  Google Scholar 

  44. Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)

    Article  Google Scholar 

  45. Singer, P., Lemmerich, F., West, R., Zia, L., Wulczyn, E., Strohmaier, M., Leskovec, J.: Why we read Wikipedia. In: Proceedings of the 26th International Conference on World Wide Web. ACM, pp 1591–1600 (2017)

  46. Starbird, K., Maddock, J., Orand, M., Achterman, P., Mason, R.M.: Rumors, falseflags, and digital vigilantes: Misinformation on Twitter after the 2013 Boston Marathonbombing. In: iConference 2014 Proceedings, pp. 654–662. iSchools (2014)

  47. Stella, M., Ferrara, E., De Domenico, M.: Bots increase exposure to negative and inflammatory content in online social systems. Proc. Natl. Acad. Sci. 115(49), 12,435–12,440 (2018)

    Article  Google Scholar 

  48. Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)

    Article  Google Scholar 

  49. Way, S.F., Larremore, D.B., Clauset, A.: Gender, productivity, and prestige in computer science faculty hiring networks. In: Proceedings of the 25th International Conference on World Wide Web. ACM, pp. 1169–1179 (2016)

  50. Zhang, Y., Guan, L., Chen, H., Zhu, J.: Using text mining to measure diffusion of innovation. In: The 67th Annual Conference of the International Communication Association: Interventions: Communication Research and Practice (2017)

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773255 and 61873167), Hong Kong RGC (GRF 11505119) and City University of Hong Kong (CCR 9360120 and HKIDS 9360163). The authors would like to thank Tai-Quan “Winson” Peng for critical reading of the early draft.

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Yafei Zhang, Lin Wang & Xiaofan Wang

  2. Department of Media and Communication, and School of Data Science, City University of Hong Kong, Hong Kong S.A.R., China

    Yafei Zhang & Jonathan J. H. Zhu

  3. Department of Automation, Shanghai University, Shanghai, 200444, China

    Xiaofan Wang

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  1. Yafei Zhang
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Appendix

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Table 2 Data descriptions
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Table 3 Media outlets and their political orientations
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Zhang, Y., Wang, L., Zhu, J.J.H. et al. Conspiracy vs science: A large-scale analysis of online discussion cascades. World Wide Web 24, 585–606 (2021). https://doi.org/10.1007/s11280-021-00862-x

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