Fake news y coronavirusdetección de los principales actores y tendencias a través del análisis de las conversaciones en Twitter

Referencias bibliográficas

• Adajar, Aaron (2020). Aaron Adajar on Twitter: «Isa sa pinakamaganda mong maa-ambag about Covid-19 outbreak ay “pananahimik” Stop spreading fake news and do research first before posting something online. And also, Wash your hands intensely. Avoid touching your face. Pray. Pray. Pray». Twitter, March 9. https://twitter.com/aaronadajar/status/1237029903112888321
• Aguilar-Gallegos, Norman; Martínez-González, Enrique-Genaro; Aguilar-Ávila, Jorge; Santoyo-Cortés, Horacio; Muñoz-Rodríguez, Manrrubio; García-Sánchez, Edgar-Iván (2016). “Análisis de redes sociales para catalizar la innovación agrícola: De los vínculos directos a la integración y radialidad”. Estudios gerenciales, v. 32, n. 140, pp. 197-207. https://doi.org/10.1016/j.estger.2016.06.006
• Ahmed, Wasim; Lugovic, Sergej (2019). “Social media analytics: Analysis and visualisation of news diffusion using NodeXL. Online information review”, v. 43, n. 1, pp. 149-160. https://doi.org/10.1108/OIR-03-2018-0093
• Albalawi, Yahya; Nikolov, Nikola S.; Buckley, Jim (2019). “Trustworthy health-related tweets on social media in Saudi Arabia: Tweet metadata analysis”. Journal of medical internet research, v. 21, n. 10, e14731. https://doi.org/10.2196/14731
• Allcott, Hunt; Gentzkow, Matthew (2017). “Social media and fake news in the 2016 election”. Journal of economic perspectives, v. 31, n. 2, pp. 211-236. https://doi.org/10.1257/jep.31.2.211
• Araujo, Matheus; Mejova, Yelena; Weber, Ingmar; Benevenuto, Fabrizio (2017). “Using Facebook ads audiences for global lifestyle disease surveillance: Promises and limitations”. In: Proceedings of the 2017 ACM on Web science conference, pp. 253-257. https://doi.org/10.1145/3091478.3091513
• Bae, Ka-Ryeong; Kwon, Sunyoung; Cho, Juhee (2019). “What cancer survivors are discussing on the internet about returning to work: A social network analysis”. Asian oncology nursing, v. 19, n. 1, pp. 37-46. https://doi.org/10.5388/aon.2019.19.1.37
• Bakal, Gotkhan; Kavuluru, Ramakanth (2017). “On quantifying diffusion of health information on Twitter”. 2017 IEEE EMBS International conference on biomedical & health informatics (BHI), pp. 485-488. https://doi.org/10.1109/BHI.2017.7897311
• Bakshy, Eytan; Hofman, Jake M.; Mason, Winter A.; Watts, Duncan J. (2011). “Everyone’s an influencer: Quantifying influence on Twitter”. Proceedings of the Fourth ACM International conference on web search and data mining - WSDM ’11, pp. 65-74. https://doi.org/10.1145/1935826.1935845
• Benson, Phil (2016). The discourse of YouTube: Multimodal text in a global context. Routledge. https://doi.org/10.4324/9781315646473
• Berkowitz, Dan; Schwartz, David-Asa (2016). “Miley, CNN and The Onion: When fake news becomes realer than real”. Journalism practice, v. 10, n. 1, pp. 1-17. https://doi.org/10.1080/17512786.2015.1006933
• Borgatti, Stephen P.; Mehra, Ajay; Brass, Daniel J.; Labianca, Giuseppe (2009). “Network analysis in the social sciences”. Science, v. 323, n. 5916, pp. 892-895. https://doi.org/10.1126/science.1165821
• Boyd, Michael S. (2014). “(New) participatory framework on YouTube? Commenter interaction in US political speeches”. Journal of pragmatics, v. 72, pp. 46-58. https://doi.org/10.1016/j.pragma.2014.03.002
• Broniatowski, David A.; Jamison, Amelia M.; Qi, Sihua; AlKulaib, Lulwah; Chen, Tao; Benton, Adrian; Quinn, Sandra C.; Dredze, Mark (2018). “Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate”. American journal of public health, v. 108, n. 10, pp. 1378-1384. https://doi.org/10.2105/AJPH.2018.304567
• Brubaker, Pamela-Jo; Wilson, Christopher (2018). “Let’s give them something to talk about: Global brands’ use of visual content to drive engagement and build relationships”. Public relations review, v. 44, n. 3, pp. 342-352. https://doi.org/10.1016/j.pubrev.2018.04.010
• Cheng, Tiffany Yi-Mei; Liu, Lisa; Woo, Benjamin K. (2018). “Analyzing Twitter as a platform for Alzheimer-related dementia awareness: Thematic analyses of tweets”. JMIR aging, v. 1, n. 2, e11542. https://doi.org/10.2196/11542
• Chu, Zi; Gianvecchio, Steven; Wang, Haining; Jajodia, Sushil (2012). “Detecting automation of Twitter accounts: Are you a human, bot, or cyborg?”. IEEE Transactions on dependable and secure computing, v. 9, n. 6, pp. 811-824. https://doi.org/10.1109/TDSC.2012.75
• Chung, Chia-Fang; Agapie, Elena; Schroeder, Jessica; Mishra, Sonali; Fogarty, James; Munson, Sean A. (2017). “When personal tracking becomes social: Examining the use of Instagram for healthy eating”. Proceedings of the 2017 CHI Conference on human factors in computing systems, pp. 1674-1687. https://doi.org/10.1145/3025453.3025747
• Clauset, Aaron; Newman, Mark E. J.; Moore, Cristopher (2004). “Finding community structure in very large networks”. Physical review E, v. 70, n. 6, pp. 66-111. https://doi.org/10.1103/PhysRevE.70.066111
• Cosentino, Gabriele (2020). “Polarize and conquer: Russian influence operations in the United States”. In: Cosentino, Gabriele. Social media and the post-truth world order. Springer International Publishing, pp. 33-57. ISBN: 978 3 030430054 https://doi.org/10.1007/978-3-030-43005-4_2
• Currie-Sivek, Susan; Bloyd-Peshkin, Sharon (2018). “Where do facts matter?: The digital paradox in magazines’ fact-checking practices”. Journalism practice, v. 12, n. 4, pp. 400-421. https://doi.org/10.1080/17512786.2017.1307694
• Dossis, Michael; Amanatidis, Dimitrios; Mylona, Ifigeneia (2015). “Mining Twitter data: Case studies with trending hashtags”. In: Proceedings in ARSA-Advanced research in scientific areas, 4th Virtual international conference on advanced research in scientific areas (ARSA), Slovakia, pp. 242-246. https://doi.org/10.18638/arsa.2015.4.1.751
• Dredze, Mark; Broniatowski, David A.; Hilyard, Karen M. (2016). “Zika vaccine misconceptions: A social media analysis”. Vaccine, v. 34, n. 30, pp. 3441-3442. https://doi.org/10.1016/j.vaccine.2016.05.008
• Eichenwald, Kurt (2020). Kurt Eichenwald on Twitter: “...It is almost incomprehensible that the second stupidest man in the world is leading the Covid-19 team for the American government, while reporting to the stupidest man in the world who tells everyone it isn’t real, it isn’t bad, go to work, fake news, hoax, fake numbers, etc.”. Twitter, March 7. https://twitter.com/kurteichenwald/status/1236068998883741696
• Evans, Matt (2016). “Information dissemination in new media: YouTube and the Israeli–Palestinian conflict”. Media, war & conflict, v. 9, n. 3, pp. 325-343. https://doi.org/10.1177/1750635216643113
• Fernández, Deborah (2019). “Análisis de grafos en redes sociales: Medidas de centralidad”. Datahack, 19 agosto. https://www.datahack.es/grafos-redes-sociales-centralidad
• Fox, Susannah (2011). “The social life of health information, 2011”. Pew Research Center: Internet, science & tech, 12 May. https://www.pewresearch.org/internet/2011/05/12/the-social-life-of-health-information-2011
• Freeman, Linton C. (2004). “The development of social network analysis”. A study in the sociology of science, v. 1, p. 687. ISBN: 978 1 594577147
• Frish, Yael; Greenbaum, Dov (2017). “Is social media a cesspool of misinformation? Clearing a path for patient-friendly safe spaces online”. The American journal of bioethics, v. 17, n. 3, pp. 19-21. https://doi.org/10.1080/15265161.2016.1274795
• Ghenai, Amira; Mejova, Yelena (2017). “Catching Zika fever: Application of crowdsourcing and machine learning for tracking health misinformation on Twitter”. 2017 IEEE International conference on healthcare informatics (ICHI), pp. 518-518. https://doi.org/10.1109/ICHI.2017.58
• Ghenai, Amira; Mejova, Yelena (2018). “Fake cures: User-centric modeling of health misinformation in social media”. Proceedings of the ACM on human-computer interaction, 2 (CSCW), pp. 1-20. https://doi.org/10.1145/3274327
• Gibbs, William J.; McKendrick, Joseph (eds.). (2015). Contemporary research methods and data analytics in the news industry. Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-4666-8580-2
• Gonçalves-Sá, Joana (2020). “In the fight against the new coronavirus outbreak, we must also struggle with human bias”. Nature medicine, v. 26, n. 3, pp. 305-305. https://doi.org/10.1038/s41591-020-0802-y
• Gu, Rui; Hong, Yili-Kevin (2019). “Addressing health misinformation dissemination on mobile social media”. In: 2019 ICIS Conference. https://aisel.aisnet.org/icis2019/is_health/is_health/29
• Gueham, Farid (2017). Le fact-checking: une réponse à la crise de l’information et de la démocratie. Paris: Fondation pour l’innovation politique. http://www.fondapol.org/wp-content/uploads/2017/07/1110-Fact-checking_2017-07-10_web.pdf
• Guidry, Jeanine P. D.; Jin, Yan; Orr, Caroline A.; Messner, Marcus; Meganck, Shana (2017). “Ebola on Instagram and Twitter: How health organizations address the health crisis in their social media engagement”. Public relations review, v. 43, n. 3, pp. 477-486. https://doi.org/10.1016/j.pubrev.2017.04.009
• Hansen, Derek; Shneiderman, Ben; Smith, Marc A. (2010). Analyzing social media networks with NodeXL: Insights from a connected world. Morgan Kaufmann. ISBN: 978 0 123822291
• Harel, David; Koren, Yehuda (2000). “A fast multi-scale method for drawing large graphs”. Proceedings of the Working conference on advanced visual interfaces - AVI’00, pp. 282-285. https://doi.org/10.1145/345513.345353
• Harper, Rob (2020). @robjh1: The Conservative Black Cowboy on Twitter: “Rush Limbaugh and AG Bill Barr call out the left wing media. The left wing media has become agents of division and anarchy. Instead of informing they seek to attack and destroy with fake news. ##FakeNewsMedia #coronavirus”. Twitter. https://twitter.com/robjh1/status/1233266971237593088
• Hasting, Carl (2020). Carl Hasting on Twitter: “I passed someone from an adjacent office today and said, “Are you following this Coronavirus situation? Scary.” She replied, “Fake news!” And there you have the danger of Trump and Fox ignorantly downplaying a world crisis and blaming it on the Dems”. Twitter, February 27. https://twitter.com/67jewelcdh/status/1232885944623063040
• Herchel-Thaddeus, Machacon (2016). “A topological data analysis approach to visualizing Ebola tweets”. Japan journal for medical informatics, v. 36, n. 5, pp. 253-269. https://doi.org/10.14948/jami.36.253
• Himelboim, Itai; Han, Jeong-Yeob (2014). “Cancer talk on Twitter: Community structure and information sources in breast and prostate cancer social networks”. Journal of health communication, v. 19, n. 2, pp. 210-225. https://doi.org/10.1080/10810730.2013.811321
• Hou, Rui; Pérez-Rosas, Verónica; Loeb, Stacy; Mihalcea, Rada (2019). “Towards automatic detection of misinformation in online medical videos”. In: 2019 International conference on multimodal interaction, pp. 235-243. https://arxiv.org/abs/1909.01543
• Jamison, Amelia M.; Broniatowski, David A.; Quinn, Sandra-Crouse (2019). “Malicious actors on Twitter: A guide for public health researchers”. American journal of public health, v. 109, n. 5, pp. 688-692. https://doi.org/10.2105/AJPH.2019.304969
• Javanainen, Petra-Marika (2020). The role of social media in attitudes towards vaccinations: Social media as a tool in vaccination movements. http://www.theseus.fi/handle/10024/334093
• Jones, Sarah (2020). Sarah Reese Jones on Twitter: “That Vanity Fair story Trump doesn’t want you to read... “Publicly, he sees it as yet another («Fake news») media war; privately, he worries about virus-carrying journalists on Air Force One” “He’s Definitely Melting Down Over This”. Twitter, March 11. https://twitter.com/politicussarah/status/1237778298530344961
• Kaleel, Shakira-Banu; Abhari, Abdolreza (2015). “Cluster-discovery of Twitter messages for event detection and trending”. Journal of computational science, v. 6, pp. 47-57. https://doi.org/10.1016/j.jocs.2014.11.004
• Kata, Anna (2012). “Anti-vaccine activists, Web 2.0, and the postmodern paradigm – An overview of tactics and tropes used online by the anti-vaccination movement”. Vaccine, v. 30, n. 25, pp. 3778-3789. https://doi.org/10.1016/j.vaccine.2011.11.112
• Laylavi, Farhad; Rajabifard, Abbas; Kalantari, Mohsen (2017). “Event relatedness assessment of Twitter messages for emergency response”. Information processing & management, v. 53, n. 1, pp. 266-280. https://doi.org/10.1016/j.ipm.2016.09.002
• Leonhardt, James (2015). “Going viral on YouTube”. Journal of digital & social media marketing, v. 3, n. 1, pp. 21-30. https://www.ingentaconnect.com/content/hsp/jdsmm/2015/00000003/00000001/art00004
• Lichoti, Jacqueline-Kasiiti; Davies, Jocelyn; Kitala, Philip M.; Githigia, Samuel M.; Okoth, Edward; Maru, Yiheyis; Bukachi, Salome A.; Bishop, Richard P. (2016). “Social network analysis provides insights into African swine fever epidemiology”. Preventive veterinary medicine, v. 126, pp. 1-10. https://doi.org/10.1016/j.prevetmed.2016.01.019
• Lipschultz, Jeremy-Harris (2017). Social media communication: Concepts, practices, data, law and ethics (2nd ed.). New York: Routledge. ISBN: 978 1 315388144 https://doi.org/10.4324/9781315388144
• Mejova, Yelena; Haddadi, Hamed; Abbar, Sofiane; Ghahghaei, Azadeh; Weber, Ingmar (2015). “Dietary habits of an expat nation: Case of Qatar”. In: Healthcare Informatics (ICHI), 215 International Conference on. IEEE, pp. 57–62. https://doi.org/ 10.1109/ICHI.2015.13.
• Ministério da Saúde da Brasil (2020). Ministério da Saúde on Twitter: “As fake news são um grande obstáculo no combate ao #coronavírus (Covid-19). Além de desinformar, elas podem gerar um alarde desnecessário entre a população. Antes de compartilhar notícias, confirme a veracidade delas. Proteja você e sua família. Saiba mais“. Twitter, Março 4. https://twitter.com/minsaude/status/1235007050159190016
• Mohr, Iris (2014). “Going viral: An analysis of YouTube videos”. Journal of marketing development and competitiveness, v. 8, n. 3, pp. 43-48. http://na-businesspress.homestead.com/JMDC/MohrI_Web8_3_.pdf
• Moorhead, S. Anne; Hazlett, Diane E.; Harrison, Laura; Carroll, Jennifer K.; Irwin, Anthea; Hoving, Ciska (2013). “A new dimension of health care: Systematic review of the uses, benefits, and limitations of social media for health communication”. Journal of medical internet research, v. 15, n. 4, e85. https://doi.org/10.2196/jmir.1933
• NPR (2020). NPR on Twitter: “The state of Missouri is suing televangelist Jim Bakker and his production company to stop them from advertising or selling a fake coronavirus remedy. The Covid-19 disease does not yet have a treatment or cure“. Twitter, March 12. https://twitter.com/npr/status/1238014380526186499
• Odlum, Michelle; Yoon, Sunmoo (2015). “What can we learn about the Ebola outbreak from tweets?”. American journal of infection control, v. 43, n. 6, pp. 563-571. https://doi.org/10.1016/j.ajic.2015.02.023
• Otte, Evelien; Rousseau, Ronald (2002). “Social network analysis: A powerful strategy, also for the information sciences”. Journal of information science, v. 28, n. 6, pp. 441-453.
• Paolillo, John C. (2008). Structure and network in the YouTube core. Proceedings of the 41st Annual Hawaii international conference on system sciences (HICSS 2008), pp. 156-156. https://doi.org/10.1109/HICSS.2008.415
• Park, Se-Jung; Lim, Yon-Soo; Park, Han-Woo (2015). “Comparing Twitter and YouTube networks in information diffusion: The case of the ‘Occupy Wall Street’ movement”. Technological forecasting and social change, v. 95, pp. 208-217. https://doi.org/10.1016/j.techfore.2015.02.003
• Pauner-Chulvi, Cristina (2018). “Noticias falsas y libertad de expresión e información. El control de los contenidos informativos en la red”. Teoría y realidad constitucional, n. 41, pp. 297-318. https://doi.org/10.5944/trc.41.2018.22123
• Ross, Andrew S.; Caldwell, David (2020). “‘Going negative’: An appraisal analysis of the rhetoric of Donald Trump on Twitter”. Language & communication, n. 70, pp. 13-27. https://doi.org/10.1016/j.langcom.2019.09.003
• Rupar, Aaron (2020). Aaron Rupar on Twitter: “Trump’s response to @acosta’s good question about concerns he isn’t taking coronavirus seriously enough was to call CNN ‘fake news’. White House handlers then immediately forced reporters out of the room“. Twitter, March 11. https://twitter.com/atrupar/status/1237875754576146445
• Scanfeld, Daniel; Scanfeld, Vanessa; Larson, Elaine L. (2010). “Dissemination of health information through social networks: Twitter and antibiotics”. American journal of infection control, v. 38, n. 3, pp. 182-188. https://doi.org/10.1016/j.ajic.2009.11.004
• Scott, John; Carrington, Peter (2014). The SAGE Handbook of social network analysis. London: SAGE Publications Ltd. ISBN: 978 1 847873958. https://doi.org/10.4135/9781446294413
• Seo, Sungwon; Kim, Jong-Kook; Kim, Sung-Il; Kim, Jeewoo; Kim, Joongheon (2019). “Semantic hashtag relation classification using co-occurrence word information”. Wireless personal communications, v. 107, n. 3, pp. 1355-1365. https://doi.org/10.1007/s11277-018-5745-y
• Shao, Chengcheng; Hui, Pik-Mai; Wang, Lei; Jiang, Xinwen; Flammini, Alessandro; Menczer, Filippo; Ciampaglia, Giovanni-Luca (2018). “Anatomy of an online misinformation network”. PLoS one, v. 13, n. 4, e0196087. https://doi.org/10.1371/journal.pone.0196087
• Sharevski, Filipo; Jachim, Peter; Florek, Kevin (2020). “To tweet or not to tweet: Covertly manipulating a Twitter debate on vaccines using malware-induced misperceptions”. arXiv:2003.12093 [cs]. http://arxiv.org/abs/2003.12093
• Sherman, Gabriel (2020). “He’s definitely melting down over this: Trump, germaphobe in chief, struggles to control the Covid-19 story”. Vanity fair, 9 March. https://www.vanityfair.com/news/2020/03/trump-germaphobe-in-chief-struggles-to-control-the-covid-19-story
• Shu, Kai; Sliva, Amy; Wang, Suhang; Tang, Jiliang; Liu, Huan (2017). “Fake news detection on social media: A data mining perspective”. ACM SIGKDD Explorations newsletter, v. 19, n. 1, pp. 22-36. https://doi.org/10.1145/3137597.3137600
• Skeptical7th (2019). “Fact checking ‘Educating liberals’ aka Dylan Wheeler”. The seventh degree. https://www.theseventhdegree.net/news/2018/11/3/fact-checking-educating-liberals
• Smith, Marc; Milic-Frayling, Natasa; Shneiderman, Ben; Mendes-Rodrigues, Eduarda; Leskovec, Jure; Dunne, Cody (2010). NodeXL: A free and open network overview, discovery and exploration add-in for Excel 2007/2010. Social Media Research Foundation. https://www.smrfoundation.org
• Staub, Harvey (2020). Harvey Staub on Twitter: “MSNBC Hopes Americans Dying From Coronavirus Will “Take Down Trump’s Presidency” https://t.co/XUBaj6Y8x1 Think. The fake news media wants you to die so that it hurts Pres. Trump. This is equivalent to Goebbels Nazi Propaganda“. Twitter, March 12. https://twitter.com/harveystaub1/status/1237926081480491008
• Stieglitz, Stefan; Mirbabaie, Milad; Ross, Bjorn; Neuberger, Christoph (2018). “Social media analytics – Challenges in topic discovery, data collection, and data preparation”. International journal of information management, n. 39, pp. 156-168. https://doi.org/10.1016/j.ijinfomgt.2017.12.002
• Syed-Abdul, Shabbir; Fernández-Luque, Luis; Jian, Wen-Shan; Li, Yu-Chuan; Crain, Steven; Hsu, Min-Huei; Wang, Yao-Chin; Khandregzen, Dorjsuren; Chuluunbaatar, Enkhzaya; Nguyen, Phung-Aanh; Liou, Der-Ming (2013). “Misleading health-related information promoted through video-based social media: Anorexia on YouTube”. Journal of medical internet research, v. 15, n. 2, e30. https://doi.org/10.2196/jmir.2237
• Tatchell, Peter (2020). Peter Tatchell on Twitter: “Heroic #HongKong bookseller #GuiMinhai abducted from Thailand in 2015 by Beijing agents. Now jailed for 10 years on fake espionage charges. China announced sentence during coronavirus outbreak to bury it https://t.co/dJktPpkVQN FOLLOW @demosisto @CHRDnet @hrichina @joshuawongcf“. Twitter, February 27. https://twitter.com/PeterTatchell/status/1233113354253803521
• Tolson, Andrew (2010). “A new authenticity? Communicative practices on YouTube”. Critical discourse studies, v. 7, n. 4, pp. 277-289. https://doi.org/10.1080/17405904.2010.511834
• Trump, Donald (2020). Donald J. Trump on Twitter: «Vanity Fair Magazine, which will soon be out of business, and their third rate Fake reporters, who make up sources which don’t exist, wrote yet another phony & boring hit piece. The facts are just the opposite. Our team is doing a great job with CoronaVirus!». Twitter, March 11. https://twitter.com/realdonaldtrump/status/1237745593876873217
• Túñez-López, José-Miguel; Toural-Bran, Carlos; Cacheiro-Requeijo, Santiago (2018). “Uso de bots y algoritmos para automatizar la redacción de noticias: Percepción y actitudes de los periodistas en España”. El profesional de la información, v. 27, n. 4, pp. 750-758. https://doi.org/10.3145/epi.2018.jul.04
• Vázquez-Herrero, Jorge; Vizoso, Ángel; López-García, Xosé (2019). “Innovación tecnológica y comunicativa para combatir la desinformación: 135 experiencias para un cambio de rumbo”. El profesional de la información, v. 28, n. 3, e280301. https://doi.org/10.3145/epi.2019.may.01
• Verweij, Peter (2012). “Twitter links between politicians and journalists”. Journalism practice, v. 6, n. 5-6, pp. 680-691. https://doi.org/10.1080/17512786.2012.667272
• Vosoughi, Soroush; Mohsenvand, Mostafa-Neo; Roy, Deb (2017). “Rumor gauge: Predicting the veracity of rumors on Twitter”. ACM Transactions on knowledge discovery from data, v. 11, n. 4, article 50. https://doi.org/10.1145/3070644
• Wang, Tao; Brede, Markus; Ianni, Antonella; Mentzakis, Emmanouil (2017). “Detecting and characterizing eating-disorder communities on social media”. In: Proceedings of the Tenth ACM International conference on web search and data mining, pp. 91-100. https://doi.org/10.1145/3018661.3018706
• Wasserman, Stanley; Faust, Katherine (1994). Social network analysis: Methods and applications (vol. 8). Cambridge University Press. ISBN: 9781 1 39788618
• Wheeler, Dylan (2020). Educating Liberals on Twitter: “The stock market dropped 1200 points today - the single worst drop in its history. This is what happens when the Fake news Media scares the public into believing the Coronavirus is worse than the Spanish flu. They will do anything to hurt Trump’s image before election season“. Twitter, February 27. https://twitter.com/education4libs/status/1233146925689516032
• White, Sean (2020). Sean White USMCSDI on Twitter: “Dear America: The ONLY difference between THIS pandemic of Coronavirus and the other hundreds we’ve dealt with is that FAKE NEWS has decided to POLITICIZE it because scumbag, left wing slobs know NO shame NOBODY lost their minds like this when 13K Americans died of H1N1 in 2010“. Twitter, March 12. https://twitter.com/usmcsdi/status/1237895813667282945
• Williams, Shirley A.; Terras, Melissa M.; Warwick, Claire (2013). “What do people study when they study Twitter? Classifying Twitter related academic papers”. Journal of documentation, v. 69, n. 3, pp. 384-410. https://doi.org/10.1108/JD-03-2012-0027
• Wood, Michael J. (2018). “Propagating and debunking conspiracy theories on Twitter during the 2015–2016 Zika virus outbreak”. Cyberpsychology, behavior, and social networking, v. 21, n. 8, pp. 485-490. https://doi.org/10.1089/cyber.2017.0669
• Wu, Shaomei; Hofman, Jake M.; Mason, Winter A.; Watts, Duncan J. (2011). “Who says what to whom on Twitter”. Proceedings of the 20th International Conference on world wide web - WWW ’11, pp. 705-714. https://doi.org/10.1145/1963405.1963504
• Wukich, Clayton; Steinberg, Alan (2013). “Nonprofit and public sector participation in self-organizing information networks: Twitter hashtag and trending topic use during disasters: Self-organizing information networks”. Risk, hazards & crisis in public policy, v. 4, n. 2, pp. 83-109. https://doi.org/10.1002/rhc3.12036
• Xiong, Ying; Cho, Moonhee; Boatwright, Brandon (2019). “Hashtag activism and message frames among social movement organizations: Semantic network analysis and thematic analysis of Twitter during the #MeToo movement”. Public relations review, v. 45, n. 1, pp. 10-23. https://doi.org/10.1016/j.pubrev.2018.10.014
• Xu, Zhan (2019). “Personal stories matter: Topic evolution and popularity among pro- and anti-vaccine online articles”. Journal of computational social science, v. 2, n. 2, pp. 207-220. https://doi.org/10.1007/s42001-019-00044-w
• Xu, Zhan; Ellis, Lauren; Umphrey, Laura R. (2019). “The easier the better? Comparing the readability and engagement of online pro- and anti-vaccination articles”. Health education & behavior, v. 46, n. 5, pp. 790-797. https://doi.org/10.1177/1090198119853614
• Yang, Haodong; Yang, Christopher C. (2013). “Harnessing social media for drug-drug interactions detection”. 2013 IEEE International conference on healthcare informatics, pp. 22-29. https://doi.org/10.1109/ICHI.2013.10
• Yom-Tov, Elad; Fernández-Luque, Luis; Weber, Ingmar; Crain, Steven P. (2012). “Pro-anorexia and pro-recovery photo sharing: A tale of two warring tribes”. Journal of medical internet research, v. 14, n. 6, e151. https://doi.org/10.2196/jmir.2239
• Zubiaga, Arkaitz; Liakata, Maria; Procter, Rob; Wong-Sak-Hoi, Geraldine; Tolmie, Peter (2016). “Analysing how people orient to and spread rumours in social media by looking at conversational threads”. PLoS one, v. 11, n. 3, e0150989. https://doi.org/10.1371/journal.pone.0150989