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| Title: | 以計算分析方法比較陰謀論與不實資訊的文本： 以社交媒體中的伊維菌素討論為例
Comparing Texts of Conspiracy Theories and Misinformation by Computational methods: The Case Study of Ivermectin Discussions on Social Media |
| Authors: | 楊聲輝
Yang, Sheng-Hui |
| Contributors: | 鄭宇君
Cheng, Yu-Chung
楊聲輝
Yang, Sheng-Hui |
| Keywords: | 陰謀論
伊維菌素
不實資訊
文本分析
LDA
黨派動機推理
Conspiracy Theories
Ivermectin
Misinformation
Texts analysis
LDA
Partisan Motivated Reasoning |
| Date: | 2024 |
| Issue Date: | 2024-04-01 14:23:30 (UTC+8) |
| Abstract: | 本研究以 2021 年 Facebook 的伊維菌素（Ivermectin）討論為個案研究，當時全球面臨嚴重的新冠疫情，卻沒有治療新冠的特效藥，因此社交媒體上的官方敘事，與另類藥物伊維菌素產生了競合關係。例如，有人聲稱伊維菌素可以替代疫苗、城市封鎖或 Covid-19 篩檢等新冠防疫措施。這些不實資訊（Misinformation）對於公共衛生和政府政策推動產生了危害，甚至演變成了陰謀論（Conspiracy Theory），認為政府和商業組織正暗中共謀，並從打壓伊維菌素中獲利。
本研究以大數據文本和計算方法為基礎，深入研究不實資訊和陰謀論的特徵，採用黨派動機推理的心理機制為分析的理論框架，通過對不實資訊和陰謀論的文本進行計算分析和語言心理分析，藉此瞭解民主社會的政治極化現象。研究者通過Facebook (Meta) 官方許可用來收集公開社團與專頁貼文工具 CrowdTangle API ，爬取2021 年整年約 40 萬筆Ivermectin的貼文，根據語言篩選出 40621 筆英文資料進行分析。 研究設計上，採用LDA主題模型分析以及質化的小組討論編碼，為陰謀論、不實資訊和事實訊息分類，通過分析文本訊息的語言心理特徵，比較了不實資訊傳播者和陰謀論傳播者在黨派動機推理程度上的差異。
本研究發現，在伊維菌素的討論中，陰謀論與不實資訊經常交纏在一起，代理人、文本訊息與傳播動機在組間有著微妙的差異，本研究資料集還顯示不實資訊和陰謀論有可能同時並存，過往研究少有學者在同一個議題下，對兩者進行組間比較。
This study is a case study of the 2021 discussions on Ivermectin on Facebook. During this time, the world was grappling with the COVID-19 pandemic, and there was no specific treatment for the virus. This led to a competition between official narratives on social media and alternative remedies like Ivermectin. Some claimed that Ivermectin could replace vaccines, city lockdowns, or COVID testing as preventive measures. Such misinformation had adverse effects on public health and government policy and, in some cases, even evolved into conspiracy theories, suggesting that governments and businesses were secretly conspiring to profit from suppressing Ivermectin.
This study utilized big data text and computational methods to delve into the characteristics of misinformation and conspiracy theories. It employed partisan motivation reasoning as the theoretical framework for analysis. By conducting computational and psycholinguistic analyses of misinformation and conspiracy theory texts, the study aimed to understand political polarization in democratic societies.
The researcher collected approximately 400,000 Ivermectin-related posts from Facebook throughout 2021 using the CrowdTangle API. From these, 40,621 English-language posts were selected for analysis through language filtering. The research design included the use of LDA topic modeling and qualitative group discussions for coding, categorizing conspiracy theories, misinformation, and factual messages. By analyzing the language and psychological characteristics of the text messages, the study compared the differences in partisan motivation reasoning between spreaders of misinformation and conspiracy theories.
The study found that conspiracy theories and misinformation frequently intertwined in discussions about Ivermectin. There were subtle differences between agents, text messages, and dissemination motivations. The dataset also revealed that misinformation and conspiracy theories could coexist, a comparison that has been less explored in previous research on the same topic. |
| Reference: | Ahmed, W., Vidal-Alaball, J., Downing, J., & Seguí, F. L. (2020). COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data. Journal of medical internet research, 22(5), e19458.
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-36.3
Altay, S., Berriche, M., Heuer, H., Farkas, J., & Rathje, S. (2023). A survey of expert views on misinformation: Definitions, determinants, solutions, and future of the field. Harvard Kennedy School Misinformation Review, 4(4).
Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748), 305-307.
Arun, R., Suresh, V., Veni Madhavan, C. E., & Narasimha Murthy, M. N. (2010). On finding the natural number of topics with latent dirichlet allocation: Some observations. In Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I 14 (pp. 391-402). Springer Berlin Heidelberg.
Bak-Coleman, J. (2023). Limiting Factors in the Effectiveness of Crowd-Sourced Labeling for Combating Misinformation. SocArXiv Preprint.
Bantum, E. O. C., & Owen, J. E. (2009). Evaluating the validity of computerized content analysis programs for identification of emotional expression in cancer narratives. Psychological assessment, 21(1), 79.
Begg, I. M., Anas, A., & Farinacci, S. (1992). Dissociation of processes in belief: Source recollection, statement familiarity, and the illusion of truth. Journal of Experimental Psychology: General, 121(4), 446.
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
Bolsen, T., Druckman, J. N., & Cook, F. L. (2014). The influence of partisan motivated reasoning on public opinion. Political Behavior, 36, 235-262.
Brueck, H. (2021). Two fringe doctors created the myth that ivermectin is a ’miracle cure’ for COVID-19—whipping up false hope that could have deadly consequences. Business Insider. https://www.businessinsider.com/why-ivermectin-being-used-treat-covid-2-doctors-leading-charge-2021-9?r=US&IR=T/
Calvillo, D. P., Ross, B. J., Garcia, R. J., Smelter, T. J., & Rutchick, A. M. (2020). Political ideology predicts perceptions of the threat of COVID-19 (and susceptibility to fake news about it). Social Psychological and Personality Science, 11(8), 1119-1128.
Caly, L., Druce, J. D., Catton, M. G., Jans, D. A., & Wagstaff, K. M. (2020). The FDA-approved drug ivermectin inhibits the replication of SARS-CoV-2 in vitro. Antiviral research, 178, 104787.
Camargo, C. Q., & Simon, F. M. (2022). Mis-and disinformation studies are too big to fail: Six suggestions for the field’s future. Harvard Kennedy School Misinformation Review, 3(5).
Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7-9), 1775-1781.
Cassino, D., & Jenkins, K. (2013). Conspiracy Theories Prosper: 25% of Americans Are ‘Truthers.’. Fairleigh Dickinson University’s Public Mind Poll. January, 17.
Compton, J., van der Linden, S., Cook, J., & Basol, M. (2021). Inoculation theory in the post‐truth era: Extant findings and new frontiers for contested science, misinformation, and conspiracy theories. Social and Personality Psychology Compass, 15(6), e12602.
Connolly, J. M., Uscinski, J. E., Klofstad, C. A., & West, J. P. (2019). Communicating to the public in the era of conspiracy theory. Public Integrity, 21(5), 469-476.
Cook, J., Lewandowsky, S., & Ecker, U. K. (2017). Neutralizing misinformation through inoculation: Exposing misleading argumentation techniques reduces their influence. PloS one, 12(5), e0175799.
Davey, M. (2021). Huge study supporting ivermectin as Covid treatment withdrawn over ethical concerns. The Guardian, 15. https://www.theguardian.com/science/2021/jul/16/huge-study-supporting-ivermectin-as-covid-treatment-withdrawn-over-ethical-concerns?utm_term=Autofeed&CMP=twt_gu&utm_medium&utm_source=Twitter#Echobox=1626371932/
Douglas, K. M., Uscinski, J. E., Sutton, R. M., Cichocka, A., Nefes, T., Ang, C. S., & Deravi, F. (2019). Understanding conspiracy theories. Political psychology, 40, 3-35.
Elgazzar, A., Eltaweel, A., Youssef, S. A., Hany, B., Hafez, M., & Moussa, H. (2020). Efficacy and safety of ivermectin for treatment and prophylaxis of COVID-19 pandemic.
Elgesem, D., Steskal, L., & Diakopoulos, N. (2019). Structure and content of the discourse on climate change in the blogosphere: The big picture. In Climate Change Communication and the Internet (pp. 21-40). Routledge.
Enders, A. M., & Smallpage, S. M. (2019). Informational cues, partisan-motivated reasoning, and the manipulation of conspiracy beliefs. Political Communication, 36(1), 83-102.
Farmer, B. (2021). As constituents clamor for ivermectin, Republican politicians embrace their cause. npr. https://www.npr.org/sections/health-shots/2021/11/04/1050680597/as-constituents-clamor-for-ivermectin-republican-politicians-embrace-their-cause/
FLCCC. (2021). Return of Organization Exempt From Income Tax Public-Disclosure-2021. FLCCC. https://covid19criticalcare.com/wp-content/uploads/2022/11/Public-Disclosure-2021.pdf/
Fong, A., Roozenbeek, J., Goldwert, D., Rathje, S., & van der Linden, S. (2021). The language of conspiracy: A psychological analysis of speech used by conspiracy theorists and their followers on Twitter. Group Processes & Intergroup Relations, 24(4), 606-623.
Francis, M. E., & Booth, R. J. (1993). Linguistic inquiry and word count. Southern Methodist University: Dallas, TX, USA.
Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051), 1878-1881.
Gorski, D. (2023). The American Board of Internal Medicine finally acts against two misinformation-spreading doctors. BMJ Evidence-Based Medicine. https://sciencebasedmedicine.org/the-american-board-of-internal-medicine-finally-acts-against-two-misinformation-spreading-doctors/
Griffiths, T., Steyvers, M., Blei, D., & Tenenbaum, J. (2004). Integrating topics and syntax. Advances in neural information processing systems, 17.
Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), 267-297.
Gustafson, A., & Rice, R. E. (2019). The effects of uncertainty frames in three science communication topics. Science Communication, 41(6), 679-706.
Gustafson, A., & Rice, R. E. (2020). A review of the effects of uncertainty in public science communication. Public Understanding of Science, 29(6), 614-633.
Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108, 814–834.
Hansen, J., Holm, L., Frewer, L., Robinson, P., & Sandøe, P. (2003). Beyond the knowledge deficit: recent research into lay and expert attitudes to food risks. Appetite, 41(2), 111-121.
Hansson, S. O. (2017). Science denial as a form of pseudoscience. Studies in History and Philosophy of Science Part A, 63, 39-47.
Heidary, F., & Gharebaghi, R. (2020). Ivermectin: a systematic review from antiviral effects to COVID-19 complementary regimen. The Journal of antibiotics, 73(9), 593-602.
Hill, A. (2021). How my ivermectin research led to Twitter death threats. The Guardian. https://www.theguardian.com/world/2021/oct/13/how-my-ivermectin-research-led-to-twitter-death-threats/
Hill, A., Garratt, A., Levi, J., Falconer, J., Ellis, L., McCann, K., ... & Wentzel, H. (2021, November). Retracted: meta-analysis of randomized trials of ivermectin to treat SARS-CoV-2 infection. In Open forum infectious diseases (Vol. 8, No. 11, p. ofab358). US: Oxford University Press.
Howard, J. & Christensen, J. (2021). FDA warns against using anti-parasitic drug for Covid-19 after reports of hospitalizations. CNN. https://edition.cnn.com/2021/03/05/health/ivermectin-covid-19-fda-statement-wellness/index.html/
Howard, J. (2019). Confirmation bias, motivated cognition, the backfire effect. Cognitive Errors and Diagnostic Mistakes: A Case-Based Guide to Critical Thinking in Medicine, 57-88.
Jack, C. (2017). Lexicon of Lies: Terms for problematic information. Data & Society. https://datasociety.net/pubs/oh/DataAndSociety_LexiconofLies.pdf
Jolley, D., & Douglas, K. M. (2014). The effects of anti-vaccine conspiracy theories on vaccination intentions. PloS one, 9(2), e89177.
Kahne, J., & Bowyer, B. (2017). Educating for democracy in a partisan age: Confronting the challenges of motivated reasoning and misinformation. American educational research journal, 54(1), 3-34.
Keeley, B. L. (1999). Of conspiracy theories. Journal of Philosophy 96 (3):109-126.
Kim, B., Xiong, A., Lee, D., & Han, K. (2021). A systematic review on fake news research through the lens of news creation and consumption: Research efforts, challenges, and future directions. PloS one, 16(12), e0260080.
Klein, C., Clutton, P., & Dunn, A. G. (2019). Pathways to conspiracy: The social and linguistic precursors of involvement in Reddit’s conspiracy theory forum. PloS one, 14(11), e0225098.
Kong, Q., Booth, E., Bailo, F., Johns, A., & Rizoiu, M. A. (2022, May). Slipping to the extreme: A mixed method to explain how extreme opinions infiltrate online discussions. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 16, pp. 524-535).
Kunda, Z. (1990). The case for motivated reasoning. Psychological bulletin, 108(3), 480.
Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., ... & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094-1096.
Lee, M. (2021). Network of Right-Wing Health Care Providers Is Making Millions Off Hydroxychloroquine and Ivermectin, Hacked Data Reveals. The Intercept. https://theintercept.com/2021/09/28/covid-telehealth-hydroxychloroquine-ivermectin-hacked/
Lewandowsky, S. (2021). Conspiracist cognition: chaos, convenience, and cause for concern. Journal for Cultural Research, 25(1), 12-35.。
Lewandowsky, S., Ecker, U. K., & Cook, J. (2017). Beyond misinformation: Understanding and coping with the “post-truth” era. Journal of applied research in memory and cognition, 6(4), 353-369.
Lo, S. Y., Li, S. C. S., & Wu, T. Y. (2021). Exploring psychological factors for COVID-19 vaccination intention in Taiwan. Vaccines, 9(7), 764.
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature human behaviour, 5(3), 337-348.
Lötscher, A. (2011). Text und Thema: Studien zur thematischen Konstituenz von Texten (Vol. 81). Walter de Gruyter.
Lutzke, L., Drummond, C., Slovic, P., & Árvai, J. (2019). Priming critical thinking: Simple interventions limit the influence of fake news about climate change on Facebook. Global environmental change, 58, 101964.
Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., ... & Adam, S. (2021). Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. In Computational methods for communication science (pp. 13-38). Routledge.
Manikonda, L., Nevo, D., Horne, B. D., Arrington, C., & Adali, S. (2022). The reasoning behind fake news assessments: a linguistic analysis. AIS Transactions on Human-Computer Interaction, (2), 230-253.
McIntyre, L. (2019). The scientific attitude: Defending science from denial, fraud, and pseudoscience. Mit Press.
MEPS. (2022). Ivermectin Drug Usage Statistics, United States, 2013 - 2020. Agency for Healthcare Research and Quality (AHRQ), Rockville, MD. ClinCalc DrugStats Database version 2022.08. https://clincalc.com/DrugStats/Drugs/Ivermectin/
Miller, J. D. (1983). Scientific literacy: A conceptual and empirical review. Daedalus, 29-48.
Miró-Llinares, F., & Aguerri, J. C. (2023). Misinformation about fake news: A systematic critical review of empirical studies on the phenomenon and its status as a ‘threat’. European Journal of Criminology, 20(1), 356-374.
Mitra, T., Counts, S., & Pennebaker, J. (2016). Understanding anti-vaccination attitudes in social media. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 10, No. 1, pp. 269-278).
Nefes, T. S. (2014). Rationale of conspiracy theorizing: Who shot the President Chen Shui-bian?. Rationality and Society, 26(3), 373-394.
Nisbet, E. C., Cooper, K. E., & Garrett, R. K. (2015). The partisan brain: How dissonant science messages lead conservatives and liberals to (dis) trust science. The ANNALS of the American Academy of Political and Social Science, 658(1), 36-66.
Omar, M., On, B. W., Lee, I., & Choi, G. S. (2015). LDA topics: Representation and evaluation. Journal of Information Science, 41(5), 662-675.
Parra, D., Trattner, C., Gómez, D., Hurtado, M., Wen, X., & Lin, Y. R. (2016). Twitter in academic events: a study of temporal usage, communication, sentimental and topical patterns in 16 computer science conferences. Computer Communications, 73, 301-314.
Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). LIWC2007: Linguistic inquiry and word count. Austin, Texas: liwc. net.
Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39-50.
Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological science, 31(7), 770-780.
Pérez-Curiel, C., & Rivas-de Roca, R. (2022). Realities and Challenges of a Democracy in Crisis. Impact of Disinformation and Populism on the Media System. In Communication and Smart Technologies: Proceedings of ICOMTA 2021 (pp. 94-103). Springer Singapore.
Peterson, E., & Iyengar, S. (2021). Partisan gaps in political information and information‐seeking behavior: motivated reasoning or cheerleading?. American Journal of Political Science, 65(1), 133-147.
Piper, K. (2021). How bad research clouded our understanding of Covid-19. The Vox. https://www.vox.com/future-perfect/22776428/ivermectin-science-publication-research-fraud/
Posetti, J., Simon, F., & Shabbir, N. (2019). Lessons in innovation: How international news organisations combat disinformation through mission-driven journalism. Reuters Institute for the Study of Journalism: Report.
Radnitz, S. (2022). Dilemmas of distrust: Conspiracy beliefs, elite rhetoric, and motivated reasoning. Political Research Quarterly, 75(4), 1143-1157.
Romer, D., & Jamieson, K. H. (2020). Conspiracy theories as barriers to controlling the spread of COVID-19 in the US. Social science & medicine, 263, 113356.
Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L., Recchia, G., ... & Van Der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society open science, 7(10), 201199.
Saunders, K. L. (2017). The impact of elite frames and motivated reasoning on beliefs in a global warming conspiracy: The promise and limits of trust. Research & Politics, 4(3), 2053168017717602.
Schaffner, B. F., & Roche, C. (2016). Misinformation and motivated reasoning: Responses to economic news in a politicized environment. Public Opinion Quarterly, 81(1), 86-110.
Scheufele, D. A. (2013). Communicating science in social settings. Proceedings of the National Academy of Sciences, 110(supplement_3), 14040-14047.
Shin, H., & Doyle, G. (2018, June). Alignment, acceptance, and rejection of group identities in online political discourse. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (pp. 1-8).
Sievert, C., & Shirley, K. (2014, June). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63-70).
Simis, M. J., Madden, H., Cacciatore, M. A., & Yeo, S. K. (2016). The lure of rationality: Why does the deficit model persist in science communication?. Public understanding of science, 25(4), 400-414.
Slothuus, R., & De Vreese, C. H. (2010). Political parties, motivated reasoning, and issue framing effects. The Journal of politics, 72(3), 630-645.
Su, L. Y. F., Scheufele, D. A., Bell, L., Brossard, D., & Xenos, M. A. (2017). Information-sharing and community-building: Exploring the use of Twitter in science public relations. Science Communication, 39(5), 569-597.
Taber C. S., Lodge M. (2006). Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50(3), 755–769.
Van Der Linden, S. (2022). Misinformation: susceptibility, spread, and interventions to immunize the public. Nature Medicine, 28(3), 460-467.
Van der Linden, S., Leiserowitz, A., Rosenthal, S., & Maibach, E. (2017). Inoculating the public against misinformation about climate change. Global challenges, 1(2), 1600008.
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. science, 359(6380), 1146-1151. Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policymaking (Vol. 27, pp. 1-107). Strasbourg: Council of Europe.
Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policymaking (Vol. 27, pp. 1-107). Strasbourg: Council of Europe.
Weber, L. (2023). Doctors who touted ivermectin as covid fix now pushing it for flu, RSV. The Washington Post. https://www.washingtonpost.com/health/2023/02/26/ivermectin-use-covid-flu-rsv/
Weikum, G. (2002). Foundations of statistical natural language processing. ACM SIGMOD Record, 31(3), 37-38.
Weinstein, B., & Heying, H. (2021, Jun 1). COVID, Ivermectin, and the Crime of the Century: DarkHorse Podcast with Pierre Kory & Bret Weinstei. DarkHorse Podcast. https://podcasts.apple.com/fi/podcast/covid-ivermectin-and-the-crime-of-the/id1471581521?i=1000523859023
Weller, K., & Puschmann, C. (2011). Twitter for scientific communication: How can citations/references be identified and measured? WebSci ’11, June 14-17, 2011, Koblenz, Germany.
Williams, D. The case for partisan motivated reasoning. Synthese 202, 89 (2023). https://doi.org/10.1007/s11229-023-04223-1
Willmore, A. (2016). This analysis shows how viral fake election news stories outperformed real news on facebook. BuzzFeed News. https://www.buzzfeednews.com/article/craigsilverman/viral-fake-election-news-outperformed-real-news-on-facebook
Woo, E. (2021). How COVID misinformation created a run on animal medicine. New York Times. https://www.nytimes.com/2021/09/28/technology/ivermectin-animal-medicine-shortage.html/
Wood, M. J., & Douglas, K. M. (2015). Online communication as a window to conspiracist worldviews. Frontiers in psychology, 6, 836.
Wood, M., & Douglas, K. (2018). Are conspiracy theories a surrogate for God?. In Handbook of conspiracy theory and contemporary religion (pp. 87-105). Brill.
Wood, T., & Porter, E. (2019). The elusive backfire effect: Mass attitudes’ steadfast factual adherence. Political Behavior, 41, 135-163.
World Health Organization. (2021). WHO advises that ivermectin only be used to treat COVID-19 within clinical trials. World Health Organization, Geneva. https://www.who.int/news-room/feature-stories/detail/who-advises-that-ivermectin-only-be-used-to-treat-covid-19-within-clinical-trials/
Wynne, B. (2006). Public engagement as a means of restoring public trust in science–hitting the notes, but missing the music?. Public Health Genomics, 9(3), 211-220.
Zhang, Y., Chen, F., & Lukito, J. (2023). Network amplification of politicized information and misinformation about COVID-19 by conservative media and partisan influencers on Twitter. Political Communication, 40(1), 24-47. |
| Description: | 碩士
國立政治大學
傳播學院傳播碩士學位學程
110464035 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110464035 |
| Data Type: | thesis |
| Appears in Collections: | [傳播學院傳播碩士學位學程] 學位論文
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