HAARP conspiracyAnalysis of its role in the 2023 Turkey & Syria earthquakes on Twitter

  1. Arce-García, Sergio 1
  2. Díaz-Campo, Jesús 1
  1. 1 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

Revista:
Estudios sobre el mensaje periodístico

ISSN: 1988-2696

Año de publicación: 2024

Volumen: 30

Número: 2

Páginas: 323-333

Tipo: Artículo

DOI: 10.5209/ESMP.95257 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Estudios sobre el mensaje periodístico

Resumen

O Twitter (atualmente "X") é um terreno fértil para a disseminação de desinformação, com foco especial em teorias da conspiração, especialmente aquelas relacionadas ao programa de radiocomunicação HAARP. Este estudo examina uma teoria da conspiração que vincula esse projeto aos terremotos de 2023 na Turquia e na Síria. Rastreando a palavra-chave "HAARP" no Twitter de 4 a 20 de fevereiro de 2023 em 11 idiomas, analisamos mais de 500.000 tweets usando teoria de rede, análise estatística, quantificação de emoção e polaridade, processamento de linguagem natural e metodologia Disarm. Os resultados mostram um padrão consistente em todos os idiomas, com aspectos emocionais contribuindo significativamente para a disseminação. O estudo conclui que a campanha de desinformação opera globalmente com uma estratégia definida, incorporando nuances locais. A metodologia Disarm é considerada adequada para analisar esse tipo de campanha.

Referencias bibliográficas

  • Allington, D., Duffy, B., Wessely, S., Dhavan, N. & Rubin, J. (2021). Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency. Psychological Medicine, 51(10), 1763-1769. https://doi.org/10.1017/s003329172000224x
  • Arce-García, S., Said-Hung, E. & Mottareale-Calvanese, D. (2022). Astroturfing as a strategy for manipulating public opinion on Twitter during the pandemic in Spain. Profesional de la información, 31(3), e310310. https://doi.org/10.3145/epi.2022.may.10
  • Arce-García, S., Said-Hung, E. & Mottareale-Calvanese, D., (2023). Tipos De Campaña Astroturfing De Contenidos Desinformativos Y Polarizados En Tiempos De Pandemia En España. Revista ICONO 14. Revista Científica De Comunicación Y Tecnologías Emergentes, 21(1). https://doi.org/10.7195/ri14.v21i1.1890
  • Agur Colin, G. L. (2021). Actors, Partisan Inclination, and Emotions: An Analysis of Government Shutdown News Stories Shared on Twitter. Social Media+Society, 7(2), 20563051211008816. https://doi.org/10.1177/20563051211008816
  • Barabási, A.-L. (2016). Network Science. Cambridge: Cambridge University Press.
  • Barrie, C. & Chun-Ting Ho, J. (2021). AcademictwitteR: an R package to access the Twitter Academic Research Product Track v2 API endpoint. Journal of Open Source Software, 6 (62), 3272. https://doi.org/10.21105/joss.03272
  • Baviera, T. (2018). Influence in the political Twitter sphere: Authority and retransmission in the 2015 and 2016 Spanish General Elections. European Journal of Communication, 33(3), 321-337. https://doi.org/10.1177/0267323118763910
  • Birzai, I. (2021, 15th December). Sputnik o nominalizează pe Diana Șoșoacă drept “omul politic al anului 2021” din România. Argumentele invocate. Ziare.com. https://bit.ly/3RViotI
  • Blondel, V., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10. https://doi.org/10.48550/arXiv.0803.0476
  • Butler, K. (2023). The Far-Right Bounty Hunter Behind the Explosive Popularity of “Died Suddenly”. Mother Jones. https://bit.ly/4aPKcs7
  • Campos-Domínguez, E. & Calvo, D. (2017). Electoral campaign on the Internet: Planning, impact and viralization on Twitter during the Spanish general election, 2015. Comunicación y Sociedad, 29, 79-101.
  • Casero-Ripollés, A., Feenstra, R.A. & Tormey, S. (2016). Old and new media logics in an electoral campaign: The case of Podemos and the two-way street mediatization of politics. The International Journal of Press/Politics, 21(3), 378-397. https://doi.org/10.1177/1940161216645340
  • Cassese, E.C., Farhart, Christina E. & Miller, J.M. (2020). Gender differences in COVID-19 conspiracy theory beliefs. Politics & Gender, 16, 1009–1018. https://doi.org/10.1017/S1743923X20000409
  • Chen Jundong, H. & Shafaeat, Z.H. (2020). Analyzing the sentiment correlation between regular tweets and retweets. Social Network Analysis and Mining, 10, 13. https://doi.org/10.1007/s13278-020-0624-4
  • Colleoni, E., Rozza, A. & 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. https://doi.org/10.1111/jcom.12084
  • European Commission (2019). Tackling online disinformation. https://bit.ly/41NPb8V
  • Cramer, M. (2022, 21st January). Court Battle Over a Ventilator Takes a Patient From Minnesota to Texas. The New York Times. https://bit.ly/3RVm6np
  • Dewitt, D., Atkinson, M. & Wegner, D. (2018). How conspiracy theories spread’. In: Uscinski JE (ed.) Conspiracy Theories and the People Who Believe Them. Oxford University Press, 319–336.
  • Díaz-Campo, J., Segado-Boj, F. & Fernández-Gómez, E. (2021). Hábitos del usuario y tipo de red social como predictores de consumo y difusión de noticias. Profesional de la información, 30(4), e300417. https://doi.org/10.3145/epi.2021.jul.17
  • Digi24 (2021, 16th December). ‘Noi detalii în scandalul Șoșoacă-Rai Uno. Jurnalista italiană: Soțul senatoarei i-a spus translatoarei: "Vă arunc pe fereastră"’. Digi24. https://bit.ly/3RViotI
  • Disarm (2022). Disarm framework explorer. Disarm Foundation. http://bit.ly/4axAgTt
  • Enders, A.M., Uscinski, J.E., Seelig, M.I., Klofstad, C.A., Wuchty, S., Funchion, J.R., Murthi, M.N., Premaratne, K. & Stoler, J. (2021). The relationship between social media use and beliefs in conspiracy theories and misinformation. Political Behaviour, 1-24. https://doi.org/10.1007/s11109-021-09734-6
  • Erokhin, D. & Komendantova, N. (2023). The role of bots in spreading conspiracies: Case study of discourse about earthquakes on Twitter. International Journal of Disaster Risk Reduction, 92, 103740. https://doi.org/10.1016/j.ijdrr.2023.103740
  • European Union-External Action (2023). 1st EEAS Report on Foreign Information Manipulation and Interference Threats. Towards a framework for networked defence. https://bit.ly/3HaIEvt
  • Fredheim, R. (2023). Virtual manipulation brief 2023/1. Generative AI and its implications for social media analysis. Nato Strategic Communications Centre of Excellence. https://bit.ly/3OjIEgL
  • Gkinopoulos Theofilps, M.S. (2023). How exposure to real conspiracy theories motivates collective action and political engagement? Τhe moderating role of primed victimhood and underlying emotional mechanisms in the case of 2018 bushfire in Attica. Journal of Applied Social Psychology, 53(1), 21-38. https://doi.org/10.1111/jasp.12923
  • Goyanes, M., Ardèvol-Abreu, A. & Gil De Zúñiga, H. (2021). Antecedents of news avoidance: competing effects of political interest, news overload, trust in news media, and “news finds me” perception”. Digital Journalism, 1-18. http://dx.doi.org/10.1080/21670811.2021.1990097
  • Guess, A., Nyhan, B. & Reifler, J. (2018). Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 US presidential campaign. European Research Council. https://cutt.ly/FOgUe1R
  • Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. (2014). ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. Plos One, 9(6), e98679. https://doi.org/10.1371/journal.pone.0098679
  • Jamieson, K.H. & Albarracín, D. (2020). The relation between media consumption and misinformation at the outset of the sars-cov-2 pandemic in the US’. The Harvard Kennedy School Misinformation Review, 1(2), 1–22. https://doi.org/10.37016/mr-2020-012
  • Jockers, M. (2017). Syuzhet, extracts sentiment and sentiment-derived plot arcs from text. https://bit.ly/4a4xl4G
  • López-García, G. (2016). ‘Nuevos’ y ‘viejos’ liderazgos: la campaña de las elecciones generales españolas de 2015 en Twitter’. Comunicación y Sociedad, 29(3), 149-167. http://dx.doi.org/10.15581/003.29.35829
  • Mahl, D., Schäfer, M.S. & Zeng, J. (2022). Conspiracy theories in online environments: An interdisciplinary literature review and agenda for future research. New Media & Society, 14614448221075759. https://doi.org/10.1177/14614448221075759
  • Mahl, D., Zeng, J. & Schäfer, M. (2021). From “nasa lies” to “reptilian eyes”: mapping communication about 10 conspiracy theories, their communities, and main propagators on Twitter. Social Media + Society, 7(2), 1–12. https://doi.org/10.1177/20563051211017482
  • Mede, N. G. & Schäfer, M. (2020). Science-related populism: Conceptualizing populist demands toward science. Public Understanding of Science, 29(5). 473-491. https://doi.org/10.1177/0963662520924259
  • Miller, J.M. (2020). Psychological, political, and situational factors combine to boost Covid-19 conspiracy theory beliefs. Canadian Journal of Political Science, 53, 327–334. https://doi.org/10.1017/S000842392000058X
  • Mohammad, S. & Turney, P.D. (2010). Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon. In: Inkpen, Diana; Strapparava, Carlo. Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 26-34. Los Ángeles: Association for Computational Linguistics. https://bit.ly/4aexvGL
  • Mohammad, S. & Turney, P.D. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, 29(3), 436-465. https://doi.org/10.48550/arXiv.1308.6297
  • Newman, H. (2022). Foreign information manipulation and interference defence standards: Test for rapid adoption of the common language and framework ‘DISARM’ (prepared in cooperation with Hybrid COE). Nato Strategic Communications. Centre of Excellence. https://bit.ly/3NUUM7z
  • Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. New York: Penguin.
  • Radford, B. (2014). HAARP conspiracy theory’s weather super-weapon’s program shuts down. Skeptical Inquirer, 38(5), 7-9.
  • Sauter, D.A., Eisner, F., Ekman, P. & Scott, S.K. (2010). Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations: correctio”. PNAS. Proceedings of the National Academy of Sciences, 107(6), 2408-2412. https://doi.org/10.1073/pnas.0908239106
  • Søe, S.O. (2018). Algorithmic detection of misinformation and disinformation: Gricean perspectives. Journal of Documentation, 74(2), 309-332. https://doi.org/10.1108/JD-05-2017-0075
  • Stempel, C., Hargrove, T. & Stempel, G.H. (2007). Media use, social structure, and belief in 9/11 conspiracy theories. Journalism & Mass Communication Quarterly, 8, 353–372. https://doi.org/10.1177/107769900708400210
  • Stieglitz, S. & Dang-Xuan, L. (2013). Emotions and information diffusion in social media –Sentiment of microblogs and sharing behaviour. Journal of Management Information Systems, 29(4), 217-248. https://bit.ly/3Tzxfv1
  • Sunstein, C. R. & Vermeule, A. (2009). Conspiracy theories: causes and cures*. Journal of Political Philosophy, 17(2), 202–227. https://doi.org/10.1111/j.1467-9760.2008.00325.x
  • Swati, U., Pranali, C. & Pragati, S. (2015). Sentiment analysis of news articles using machine learning approach. International Journal of Advances in Electronics and Computer Science, 2 (4), 114-116.
  • Tandoc Jr, E.C.; Lim Zheng, W. & Ling, R. (2018). Defining “fake news” A typology of scholarly definitions. Digital Journalism, 6(2), 137-153. https://doi.org/10.1080/21670811.2017.1360143
  • Uscinski, J.E. (2018). The study of conspiracy theories. Argumenta, 3(2), 233–245.
  • Van Der Linden, S., Maibach, E., Cook, J., Leiserowitz, A. & Lewandowsky, S. (2017). Inoculating against misinformation. Science, 358(6367), 1141-1142. https://doi.org/10.1126/science.aar4533
  • Waisbord, S. (2018). Truth is what happens to news: On journalism, fake news, and post-truth. Journalism Studies, 19(13), 1866-1878. https://doi.org/10.1080/1461670X.2018.1492881
  • Zeng, J. & Schäfer, M. S. (2021). Conceptualizing “dark platforms” Covid-19-related conspiracy theories on 8kun and Gab. Digital Journalism, 9(9), 1321–1343. https://doi.org/10.1080/21670811.2021.1938165
  • Zhao, Z., Zhao, J., Jichang, S., Sano, Y., Levy, O., Takayasu, H., Takayasu, M., Li, D., Wu, J. & Havlin, S. (2020). Fake news propagates differently from real news even at early stages of spreading’. EPJ Data Science, 9(7). https://doi.org/10.1140/epjds/s13688-020-00224-z