The coronavirus pandemic has been accompanied by the spread of misinformation on social media. The Plandemic conspiracy theory holds that the pandemic outbreak was planned to create a new social order. This study examines the evolution of this popular conspiracy theory from a dynamic network perspective. Guided by the analytical framework of network evolution, the current study explores drivers of tie changes in the Plandemic communication network among serial participants over a 4-month period. Results show that tie changes are explained by degree-based and closure-based structural features (i.e. tendencies toward transitive closure and shared popularity and tendencies against in-degree activity and transitive reciprocated triplet) and nodal attributes (i.e. bot probability and political preference). However, a participant’s level of anger expression does not predict the evolution of the observed network.
AhmedWSeguíFLVidal-AlaballJ, et al. (2020) Covid-19 and the “film your hospital” conspiracy theory: social network analysis of Twitter data. Journal of Medical Internet Research22(10): e22374.
2.
BarabásiALAlbertR (1999) Emergence of scaling in random networks. Science286(5439): 509–512.
3.
BennettWLLivingstonS (2018) The disinformation order: disruptive communication and the decline of democratic institutions. European Journal of Communication33(2): 122–139.
4.
BentleyRAShennanSJ (2005) Random copying and cultural evolution. Science309(5736): 877–879.
5.
BlockP (2015) Reciprocity, transitivity, and the mysterious three-cycle. Social Networks40: 163–173.
BurtRS (2005) Brokerage and Closure: An Introduction to Social Capital. Oxford: Oxford University Press.
8.
ChenELermanKFerraraE (2020) Tracking social media discourse about the COVID-19 pandemic: development of a public coronavirus Twitter data set. JMIR Public Health and Surveillance6(2): e19273.
9.
ChoiS (2014) Flow, diversity, form, and influence of political talk in social-media-based public forums. Human Communication Research40(2): 209–237.
10.
ChoiSYangJSChenW (2018) Longitudinal change of an online political discussion forum: antecedents of discussion network size and evolution. Journal of Computer-Mediated Communication23(5): 260–277.
11.
ConoverMDRatkiewiczJFranciscoM, et al. (2011) Political polarization on Twitter. In: Proceedings of the fifth international AAAI conference on weblogs and social media, pp. 89–96. Available at: https://ojs.aaai.org/index.php/ICWSM/article/view/14126
12.
CresciS (2020) A decade of social bot detection. Communications of the ACM63(10): 72–83.
13.
DavisCAVarolOFerraraE, et al. (2016) BotOrNot: a system to evaluate social bots. In: Proceedings of the 25th international conference companion on World Wide Web, pp. 273–274. Available at: https://dl.acm.org/doi/10.1145/2872518.2889302
14.
De VeirmanMCaubergheVHuddersL (2017) Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. International Journal of Advertising36(5): 798–828.
FerraraEVarolODavisC, et al. (2016) The rise of social bots. Communications of the ACM59(7): 96–104.
18.
González-BailónSWangNRiveroA, et al. (2014) Assessing the bias in samples of large online networks. Social Networks38: 16–27.
19.
GrimmeCAssenmacherDAdamL (2018) Changing perspectives: is it sufficient to detect social bots? In: MeiselwitzG (ed.) Social Computing and Social Media: User Experience and Behavior. Cham: Springer, pp. 445–461.
20.
GruzdAMaiP (2020) Going viral: how a single tweet spawned a COVID-19 conspiracy theory on Twitter. Big Data & Society7(2): 1–9.
21.
HagenLNeelySKellerTE, et al. (2020) Rise of the machines? Examining the influence of social bots on a political discussion network. Social Science Computer Review. Available at: https://journals.sagepub.com/doi/10.1177/0894439320908190
HeiderF (1958) The Psychology of Interpersonal Relations. New York: John Wiley & Sons.
24.
HeppA (2020) Artificial companions, social bots and work bots: communicative robots as research objects of media and communication studies. Media, Culture & Society42(7–8): 1410–1426.
25.
JacksonMO (2019) The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviors. New York: Vintage Books.
JangSMGengTLiJYQ, et al. (2018) A computational approach for examining the roots and spreading patterns of fake news: evolution tree analysis. Computers in Human Behavior84: 103–113.
28.
JiangJChenEYanS, et al. (2020) Political polarization drives online conversations about COVID-19 in the United States. Human Behavior and Emerging Technologies2(3): 200–211.
KovacsBKleinbaumAM (2020) Language-style similarity and social networks. Psychological Science31(2): 202–213.
32.
MargolinDLiaoW (2018) The emotional antecedents of solidarity in social media crowds. New Media & Society20(10): 3700–3719.
33.
MengJ (2016) Your health buddies matter: preferential selection and social influence on weight management in an online health social network. Health Communication31(12): 1460–1471.
34.
MohammadSKiritchenkoSZhuX (2013) NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the seventh international workshop on semantic evaluation, pp. 321–327. Available at: https://www.aclweb.org/anthology/S13-2053
35.
MongePRContractorN (2003) Theories of Communication Networks. Oxford: Oxford University Press.
PengTQLiuMWuY, et al. (2016) Follower-Followee network, communication networks, and vote agreement of the U.S. members of congress. Communication Research43(7): 996–1024.
38.
RauchfleischAKaiserJ (2020) The False positive problem of automatic bot detection in social science research. PLoS One15(10): e0241045.
RobinsGPattisonPWangP (2009) Closure, connectivity and degree distributions: exponential random graph (p*) models for directed social networks. Social Networks31(2): 105–117.
41.
RomerDJamiesonKH (2020) Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Social Science & Medicine263: 113356.
42.
ShaoCCiampagliaGLVarolO, et al. (2018) The spread of low-credibility content by social bots. Nature Communications9(1): 4787.
43.
ShinJJianLDriscollK, et al. (2018) The diffusion of misinformation on social media: temporal pattern, message, and source. Computers in Human Behavior83: 278–287.
44.
SongH (2015) Uncovering the structural underpinnings of political discussion networks: evidence from an exponential random graph model. Journal of Communication65(1): 146–169.
45.
SongHChoJBenefieldGA (2020) The dynamics of message selection in online political discussion forums: self-segregation or diverse exposure?Communication Research47(1): 125–152.
46.
SundarSS (2020) Rise of machine agency: a framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication25(1): 74–88.
47.
Swire-ThompsonBLazerD (2020) Public health and online misinformation: challenges and recommendations. Annual Review of Public Health41(1): 433–451.
48.
ThorsonKEdgerlySKligler-VilenchikN, et al. (2016) Seeking visibility in a big tent: Digital communication and the People’s Climate March. International Journal of Communication10: 4784–4806. Available at: https://ijoc.org/index.php/ijoc/article/viewFile/4703/1802
49.
Van ZomerenMSpearsRFischerAH, et al. (2004) Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy. Journal of Personality and Social Psychology87(5): 649–664.
50.
VarolOFerraraEDavisCA, et al. (2017) Online human-bot interactions: detection, estimation, and characterization. In: Proceedings of the eleventh international AAAI conference on web and social media, pp. 280–289. Available at: https://ojs.aaai.org/index.php/ICWSM/article/view/14871
51.
WangLYangAThorsonK (2021) Serial participants of social media climate discussion as a community of practice: a longitudinal network analysis. Information, Communication & Society24: 941–959.
52.
WeeksBE (2015) Emotions, partisanship, and misperceptions: how anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation. Journal of Communication65(4): 699–719.
WoodMJ (2018) Propagating and debunking conspiracy theories on Twitter during the 2015–2016 Zika virus outbreak. Cyberpsychology, Behavior, and Social Networking21(8): 485–490.
55.
WoolleySCHowardPN (2016) Political communication, computational propaganda, and autonomous agents—Introduction. International Journal of Communication10: 4882–4890. Available at: https://ijoc.org/index.php/ijoc/article/view/6298/1809
56.
XuYFulkJMongeP (2021) Ecological influences on network tie dissolution in the evolution of affiliation networks in the International Communication Association, 2009–2015. Human Communication Research47(1): 75–103.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
