6,255
Views
19
CrossRef citations to date
0
Altmetric
Coronavirus – Research Paper

Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations

, , , &
Pages 1-13 | Received 13 Aug 2021, Accepted 03 Nov 2021, Published online: 21 Jan 2022

References

  • Hernandez RG, Hagen L, Walker K, O’Leary H, Lengacher C. The COVID-19 vaccine social media infodemic: healthcare providers’ missed dose in addressing misinformation and vaccine hesitancy. Hum Vaccin Immunother. 2021;17(9):2962–2964. doi:10.1080/21645515.2021.1912551.
  • Puri N, Coomes EA, Haghbayan H, Gunaratne K. Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases. Hum Vaccin Immunother. 2020;16(11):2586–93. doi:10.1080/21645515.2020.1780846.
  • Wiysonge CS, Ndwandwe D, Ryan J, Jaca A, Batouré O, Anya BPM, Cooper S. Vaccine hesitancy in the era of COVID-19: could lessons from the past help in divining the future? Hum Vaccin Immunother. 2021;1–3. doi:10.1080/21645515.2021.1893062.
  • Blume S. A nti-vaccination movements and their interpretations. Soc Sci Med. 2006;62(3):628–42. doi:10.1016/j.socscimed.2005.06.020.
  • Dredze M, Broniatowski DA, Smith M, Hilyard KM. Understanding vaccine refusal: why we need social media now. Am J Prev Med. 2016;50(4):550. doi:10.1016/j.amepre.2015.10.002.
  • Ahmed N, Quinn SC, Hancock GR, Freimuth VS, Jamison A. Social media use and influenza vaccine uptake among White and African American adults. Vaccine. 2018;36(49):7556–61. doi:10.1016/j.vaccine.2018.10.049.
  • Joint statement by WHO U U, UNDP, UNESCO, UNAIDS, ITU, UN Global Pulse, and IFRC. Managing the COVID-19 infodemic: Promoting healthy behaviours and mitigating the harm from misinformation and disinformation; 2020.
  • Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a tool for health research: a systematic review. Am J Public Health. 2017;107(1):e1–e8. doi:10.2105/AJPH.2016.303512.
  • Rains SA, Leroy G, Warner EL, Harber P. Psycholinguistic markers of COVID-19 conspiracy tweets and predictors of tweet dissemination. Health Commun. 2021;1–10. doi:10.1080/10410236.2021.1929691.
  • Criss S, Nguyen TT, Norton S, Virani I, Titherington E, Tillmanns EL, Kinnane C, Maiolo G, Kirby AB, Gee GC, et al. Advocacy, hesitancy, and equity: exploring U.S. Race-related discussions of the COVID-19 vaccine on Twitter. Int J Environ Res Public Health. 2021;18(11):5693. doi:10.3390/ijerph18115693.
  • To QG, To KG, Huynh VAN, Nguyen NTQ, Ngo DTN, Alley SJ, Tran ANQ, Tran ANP, Pham NTT, Bui TX, et al. Applying machine learning to identify anti-vaccination Tweets during the COVID-19 pandemic. Int J Environ Res Public Health. 2021;18(8):4069. doi:10.3390/ijerph18084069.
  • Love B, Himelboim I, Holton A, Stewart K. Twitter as a source of vaccination information: content drivers and what they are saying. Am J Infect Control. 2013;41(6):568–70. doi:10.1016/j.ajic.2012.10.016.
  • Massey PM, Leader A, Yom-Tov E, Budenz A, Fisher K, Klassen AC. Applying multiple data collection tools to quantify human papillomavirus vaccine communication on Twitter. J Med Internet Res. 2016;18(12):e318. doi:10.2196/jmir.6670.
  • Tomeny TS, Vargo CJ, El-Toukhy S. Geographic and demographic correlates of autism-related anti-vaccine beliefs on Twitter, 2009-15. Soc Sci Med. 2017;191:168–75. doi:10.1016/j.socscimed.2017.08.041.
  • Blankenship EB, Goff ME, Yin J, Tse, ZT, Fu, KW, Liang, H, Saroha, N, Fung, IC. Sentiment, contents, and retweets: a study of two vaccine-related Twitter datasets. Perm J. 2018;22:17–138.
  • Broniatowski DA, Jamison AM, Qi S, AlKulaib L, Chen T, Benton A, Quinn SC, Dredze M. Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. Am J Public Health. 2018;108(10):1378–84. doi:10.2105/AJPH.2018.304567.
  • Yuan X, Schuchard RJ, Crooks AT. Examining emergent communities and social bots within the polarized online vaccination debate in Twitter. Social Media+ Soc. 2019;5:2056305119865465.
  • Featherstone JD, Ruiz JB, Barnett GA, Millam BJ. Exploring childhood vaccination themes and public opinions on Twitter: a semantic network analysis. Telematics Inf. 2020;54:101474. doi:10.1016/j.tele.2020.101474.
  • Himelboim I, Xiao X, Lee DKL, Wang MY, Borah P. A social networks approach to understanding vaccine conversations on Twitter: network clusters, sentiment, and certainty in HPV social networks. Health Commun. 2020;35(5):607–15. doi:10.1080/10410236.2019.1573446.
  • Horawalavithana S, Ng KW, Iamnitchi A. Twitter is the megaphone of cross-platform messaging on the white helmets. International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation; 2020. Springer. p. 235–44.
  • Jamison A, Broniatowski DA, Smith MC, Parikh KS, Malik A, Dredze M, Quinn SC. Adapting and extending a typology to identify vaccine misinformation on Twitter. Am J Public Health. 2020;110(S3):S331–S9. doi:10.2105/AJPH.2020.305940.
  • Milani E, Weitkamp E, Webb P. The visual vaccine debate on Twitter: a social network analysis. Media Commun. 2020;8(2):364–75. doi:10.17645/mac.v8i2.2847.
  • Nuzhath T, Tasnim S, Sanjwal RK, Trisha, NF, Rahman, M, Mahmud, SMF, Arman, A, Chakraborty, S, Hossain, MM. COVID-19 vaccination hesitancy, misinformation and conspiracy theories on social media: a content analysis of Twitter data; SocArXiv 2020.
  • Righetti N. Health politicization and misinformation on Twitter. A study of the Italian Twittersphere from before, during and after the law on mandatory vaccinations; 2020.
  • Tavoschi L, Quattrone F, D’Andrea E, Ducange P, Vabanesi M, Marcelloni F, Lopalco PL. Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy. Hum Vaccin Immunother. 2020;16(5):1062–69. doi:10.1080/21645515.2020.1714311.
  • Walter D, Ophir Y, Jamieson KH. Russian Twitter accounts and the partisan polarization of vaccine discourse, 2015–2017. Am J Public Health. 2020;110(5):718–24. doi:10.2105/AJPH.2019.305564.
  • Prieto Santamaria L, Tuñas JM, Fernandez Peces-Barba D, Jaramillo A, Cotarelo M, Menasalvas E, Conejo Fernández A, Arce A, Gil de Miguel A, Rodríguez González A, et al. Influenza and measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018. Hum Vaccin Immunother. 2021:1–15. doi:10.1080/21645515.2021.1877597
  • Thelwall M, Kousha K, Thelwall S. Covid-19 vaccine hesitancy on English-language Twitter. Profesional de la información (EPI). 2021;30(2): e300212. doi:10.3145/epi.2021.mar.12.
  • Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, Byrd B, Smyser J. Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. J Commun Healthc. 2020;1–8. doi:10.1080/17538068.2020.1822726.
  • Cossard A, Morales GDF, Kalimeri K, Mejova Y, Paolotti D, Starnini M. Falling into the echo chamber: the Italian vaccination debate on Twitter. Proceedings of the International AAAI conference on web and social media; 2020. p. 130–40.
  • Hoffman BL, Felter EM, Chu K-H, Shensa A, Hermann C, Wolynn T, Williams D, Primack BA. It’s not all about autism: the emerging landscape of anti-vaccination sentiment on Facebook. Vaccine. 2019;37(16):2216–23. doi:10.1016/j.vaccine.2019.03.003.
  • Klimiuk K, Czoska A, Biernacka K, Balwicki Ł. Vaccine misinformation on social media–topic-based content and sentiment analysis of Polish vaccine-deniers’ comments on Facebook. Hum Vaccin Immunother. 2021;17(7):2026–35. doi:10.1080/21645515.2020.1850072.
  • Tustin JL, Crowcroft NS, Gesink D, Johnson I, Keelan J, Lachapelle B. User-driven comments on a Facebook advertisement recruiting Canadian parents in a study on immunization: content analysis. JMIR Public Health Surveillance. 2018;4(3):e10090. doi:10.2196/10090.
  • Orr D, Baram-Tsabari A, Landsman K. Social media as a platform for health-related public debates and discussions: the Polio vaccine on Facebook. Isr J Health Policy Res. 2016;5(1):1–11. doi:10.1186/s13584-016-0093-4.
  • Buller DB, Walkosz BJ, Berteletti J, Pagoto SL, Bibeau J, Baker K, Hillhouse J, Henry KL. Insights on HPV vaccination in the United States from mothers’ comments on Facebook posts in a randomized trial. Hum Vaccin Immunother. 2019;15(7–8):1479–87. doi:10.1080/21645515.2019.1581555.
  • Kearney MD, Selvan P, Hauer MK, Leader AE, Massey PM. Characterizing HPV vaccine sentiments and content on Instagram. Health Educ Behav. 2019;46(2_suppl):37S–48S. doi:10.1177/1090198119859412.
  • Basch CH, MacLean SA. A content analysis of HPV related posts on Instagram. Hum Vaccin Immunother. 2019;15(7–8):1476–78. doi:10.1080/21645515.2018.1560774.
  • Massey PM, Kearney MD, Hauer MK, Selvan P, Koku E, Leader AE. Dimensions of misinformation about the HPV vaccine on Instagram: content and network analysis of social media characteristics. J Med Internet Res. 2020;22(12):e21451. doi:10.2196/21451.
  • Guidry JP, Carlyle K, Messner M, Jin Y. On pins and needles: how vaccines are portrayed on Pinterest. Vaccine. 2015;33(39):5051–56. doi:10.1016/j.vaccine.2015.08.064.
  • Guidry J, Messner M. Social media crisis commun. In: Austin, L, and Jin, Y, editors. Social media and crisis communication. (New York, NY). 2017. p. 267–279.
  • Guidry JP, Vraga EK, Laestadius LI, Miller CA, Occa A, Nan X, Ming HM, Qin Y, Fuemmeler BF, Carlyle KE, et al. HPV vaccine searches on Pinterest: before and after Pinterest’s actions to moderate content. Am J Public Health. 2020;110(S3):S305–S11. doi:10.2105/AJPH.2020.305827.
  • Ache KA, Wallace LS. Human papillomavirus vaccination coverage on YouTube. Am J Prev Med. 2008;35(4):389–92. doi:10.1016/j.amepre.2008.06.029.
  • Briones R, Nan X, Madden K, Waks L. When vaccines go viral: an analysis of HPV vaccine coverage on YouTube. Health Commun. 2012;27(5):478–85. doi:10.1080/10410236.2011.610258.
  • Song MY-J, Gruzd A. Examining sentiments and popularity of pro-and anti-vaccination videos on YouTube. Proceedings of the 8th International Conference on Social Media & Society. 2017; Toronto, Canada. p. 1–8.
  • Donzelli G, Palomba G, Federigi I, Aquino F, Cioni L, Verani M, Carducci A, Lopalco P. Misinformation on vaccination: a quantitative analysis of YouTube videos. Hum Vaccin Immunother. 2018;14(7):1654–59. doi:10.1080/21645515.2018.1454572.
  • Żuk P, Żuk P. Right-wing populism in Poland and anti-vaccine myths on YouTube: political and cultural threats to public health. Glob Public Health. 2020;15(6):790–804. doi:10.1080/17441692.2020.1718733.
  • Jennings W, Stoker G, Bunting H, Valgarðsson VO, Gaskell J, Devine D, McKay L, Mills MC. Lack of trust, conspiracy beliefs, and social media use predict COVID-19 vaccine hesitancy. Vaccines. 2021;9(6):593. doi:10.3390/vaccines9060593.
  • Kulshrestha J, Zafar M, Noboa L, Gummadi K, Ghosh S. Characterizing information diets of social media users. Proceedings of the International AAAI Conference on Web and Social Media; 2015; University of Oxford, Oxford, UK.
  • Tandoc EC Jr, Lou C, Min VLH. Platform-swinging in a poly-social-media context: how and why users navigate multiple social media platforms. J Comp-Mediated Commun. 2019;24(1):21–35. doi:10.1093/jcmc/zmy022.
  • Madianou M, Miller D. Polymedia: towards a new theory of digital media in interpersonal communication. Int J Cult Stud. 2013;16(2):169–87. doi:10.1177/1367877912452486.
  • Wilson T, Starbird K. Cross-platform disinformation campaigns: lessons learned and next steps. Harvard Kennedy Sch Misinf Rev. 2020;1. doi:10.37016/mr-2020-38.
  • Douglas KM, Uscinski JE, Sutton RM, Cichocka A, Nefes T, Ang CS, Deravi F. Understanding conspiracy theories. Polit Psychol. 2019;40(S1):3–35. doi:10.1111/pops.12568.
  • Van Prooijen J-W, Jostmann NB. Belief in conspiracy theories: the influence of uncertainty and perceived morality. Eur J Soc Psychol. 2013;43(1):109–15. doi:10.1002/ejsp.1922.
  • Van Prooijen J-W, Douglas KM. Conspiracy theories as part of history: the role of societal crisis situations. Mem Stud. 2017;10(3):323–33. doi:10.1177/1750698017701615.
  • Baden C, Sharon T. BLINDED BY THE LIES? Toward an integrated definition of conspiracy theories. Commun Theory. 2021;31(1):82–106. doi:10.1093/ct/qtaa023.
  • Romer D, Jamieson KH. Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Soc Sci Med. 2020;263:113356. doi:10.1016/j.socscimed.2020.113356.
  • Jolley D, Douglas KM. The effects of anti-vaccine conspiracy theories on vaccination intentions. PloS One. 2014;9(2):e89177. doi:10.1371/journal.pone.0089177.
  • Jolley D, Douglas KM. Prevention is better than cure: addressing anti‐vaccine conspiracy theories. J Appl Soc Psychol. 2017;47(8):459–69. doi:10.1111/jasp.12453.
  • van Prooijen J-W, Van Vugt M. Conspiracy theories: evolved functions and psychological mechanisms. Perspect Psychol Sci. 2018;13(6):770–88. doi:10.1177/1745691618774270.
  • Gruzd A, Mai P. Going viral: how a single tweet spawned a COVID-19 conspiracy theory on Twitter. Big Data Soc. 2020;7(2):2053951720938405. doi:10.1177/2053951720938405.
  • Gerts D, Shelley CD, Parikh N, Pitts T, Watson Ross C, Fairchild G, Vaquera Chavez NY, Daughton AR. “Thought I’d share first” and other conspiracy theory Tweets from the COVID-19 infodemic: exploratory study. JMIR Public Health Surveillance. 2021;7(4):e26527. doi:10.2196/26527.
  • Ahmed W, Vidal-Alaball J, Downing J, Seguí FL. COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data. J Med Internet Res. 2020;22(5):e19458. doi:10.2196/19458.
  • Ferrara E. # covid-19 on twitter: bots, conspiracies, and social media activism. arXiv Preprint arXiv: 200409531; 2020.
  • McCombs M, Shaw D. The agenda setting function of mass media. Public Opin Q. 1972;36(2):176–87. doi:10.1086/267990.
  • Scheufele DA, Tewksbury D. Framing, agenda setting, and priming: the evolution of three media effects models. J commun. 2007;57:9–20.
  • Ophir Y, Walter D, Arnon D, Lokmanoglu, A, Tizzoni, M, Carota, J, D'Antiga, LO, Nicastro, E. The framing of COVID-19 in Italian media and its relationship with community mobility: a mixed-method approach. J Health Commun. 2021;26(3):161–173. doi:10.1080/10810730.2021.1899344.
  • Valenzuela S, Puente S, Flores PM. Comparing disaster news on Twitter and television: an intermedia agenda setting perspective. J Broadcast Electron Media. 2017;61(4):615–37. doi:10.1080/08838151.2017.1344673.
  • Rogstad I. Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. J IT Polit. 2016;13(2):142–58. doi:10.1080/19331681.2016.1160263.
  • Cruishank I, Ginossar T, Zheleva E, Berger-Wolf TY. Content and dynamics of websites shared over vaccine-related Tweets in COVID-19 conversations: a computational analysis. J Med Internet Res. 2021;23(11): e29127. doi:10.2196/29127.
  • Chen E, Lerman K, Ferrara E. Tracking social media discourse about the covid-19 pandemic: development of a public coronavirus twitter data set. JMIR Public Health Surveillance. 2020;6(2):e19273. doi:10.2196/19273.
  • Pezoa F, Reutter JL, Suarez F, Ugarte M, Vrgoč D. Foundations of JSON schema. Proceedings of the 25th International Conference on World Wide Web; 2016; Montreal, Canada. p. 263–273.
  • Huang B. Learning user latent attributes on social media [PhD thesis]. Carnegie Mellon University; 2021.
  • Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
  • Röder M, Both A, Hinneburg A. Exploring the space of topic coherence measures. Proceedings of the eighth ACM international conference on Web search and data mining; 2015; Shanghai, China. p. 399–408.
  • Boon-Itt S, Skunkan Y. Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modeling study. JMIR Public Health Surveillance. 2020;6(4):e21978. doi:10.2196/21978.
  • Downe‐Wamboldt B. Content analysis: method, applications, and issues. Health Care Women Int. 1992;13(3):313–21. doi:10.1080/07399339209516006.
  • Moulding R, Nix-Carnell S, Schnabel A, Nedeljkovic M, Burnside EE, Lentini AF, Mehzabin N. Better the devil you know than a world you don’t? Intolerance of uncertainty and worldview explanations for belief in conspiracy theories. Pers Individ Dif. 2016;98:345–54. doi:10.1016/j.paid.2016.04.060.
  • Giglietto F, Righetti N, Rossi L, Marino G. Coordinated link sharing behavior as a signal to surface sources of problematic information on Facebook. International Conference on Social Media and Society; 2020. p. 85–91.
  • Pacheco D, Hui P-M, Torres-Lugo C, Truong BT, Flammini A, Menczer F. Uncovering coordinated networks on social media: methods and case studies. arXiv preprint. 2020;arXiv:200105658.
  • False claim: Video shows Bill Gates presenting vaccine for religious fundamentalists to Pentagon; 2020. https://www.reuters.com/article/uk-factcheck-gates-fundamentalists-penta/false-claim-video-shows-bill-gates-presenting-vaccine-for-religious-fundamentalists-to-pentagon-idUSKBN22P35M. Accessed3 June 2021.
  • Menczer F. 4 Reasons why social media make us vulnerable to manipulation. Fourteenth ACM Conference on Recommender Systems; 2020. p. 1.
  • Yang K-C, Hui P-M, Menczer F. Bot electioneering volume: visualizing social bot activity during elections. Companion Proceedings of The 2019 World Wide Web Conference, WWW’19, ACM; 2019; New York, NY, USA. p. 214–217. doi:10.1145/3308560.3316499.
  • Jamison AM, Broniatowski DA, Quinn SC. Malicious actors on Twitter: a guide for public health researchers. Am J Public Health. 2019;109(5):688–92. doi:10.2105/AJPH.2019.304969.
  • Lahouati M, De Coucy A, Sarlangue J, Cazanave C. Spread of vaccine hesitancy in France: what about YouTube™? Vaccine. 2020;38(36):5779–82. doi:10.1016/j.vaccine.2020.07.002.
  • Basch CH, Hillyer GC, Zagnit EA, Basch CE. YouTube coverage of COVID-19 vaccine development: implications for awareness and uptake. Hum Vaccin Immunother. 2020;16(11):2582–85. doi:10.1080/21645515.2020.1790280.