COVID-19 infodemic on Facebook: a social network analysis in Thai context

ABSTRACT

Many studies on the COVID-19 infodemic cover a fairly short period and focus on the West. This research aims at filling this knowledge gap by examining the infodemic on Facebook in the context of Thailand, covering a more extended period (19 months). The objectives are to gain insights into how COVID-19 information pollution is propagated and how well the counternarratives penetrate the users, as well as to spell out prevalent types of information pollution and trends. The network analysis result shows that both debunking/fact-checked and information pollution networks are similar in terms of structures and spread patterns, reflecting a disposition of echo chambers. The attempt to distribute counternarratives to empower the users largely could not penetrate those who usually interact with the information pollution or vice versa. Claims about herbal medicines form the largest proportion of the dataset, and this highlights the uniqueness of contextual influence over the infodemic. The lessons learned from this study could contribute to policymaking concerning pandemic communication and media and information literacy. An understanding of the problem in its context could lead to the development of appropriate and effective responses as well as means to tackle the current and future phenomena of the infodemic.

KEYWORDS:

• infodemic
• disinformation
• COVID-19
• social network analysis
• Facebook
• Thailand

Acknowledgements

This paper is a part of a Ph.D. thesis entitled COVID-19 Infodemic and Social Media Platforms in Thailand.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data not available due to [ethical/legal/commercial] restrictions

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.

Notes

1 Aengus Bridgman et al., “Infodemic Pathways: Evaluating the Role That Traditional and Social Media Play in Cross-National Information Transfer,” Frontiers in Political Science 3, no. 20 (2021), doi:10.3389/fpos.2021.648646, https://doi.org/10.3389/fpos.2021.648646.

2 Naja Bentzen and Thomas Smith, The Evolving Consequences of the Coronavirus ‘Infodemic’ (U.K.: European Parliamentary Research Service, 2020), https://www.europarl.europa.eu/RegData/etudes/BRIE/2020/652083/EPRS_BRI(2020)652083_EN.pdf; Alistair Coleman, “‘Hundreds Dead’ Because of Covid-19 Misinformation,” BBC, August 12, 2020, https://www.bbc.com/news/world-53755067 (accessed March 24, 2022); Jack Guy, “East Asian Student Assaulted in ‘Racist’ Coronavirus Attack in London,” CNN, March 4, 2020, https://edition.cnn.com/2020/03/03/uk/coronavirus-assault-student-london-scli-intl-gbr/index.html (accessed August 12, 2020); Marianna Spring, “Coronavirus: The Human Cost of Virus Misinformation,” BBC, May 27, 2020, https://www.bbc.com/news/stories-52731624 (accessed August 12, 2020); Craig Timberg and Allyson Chiu, “As the Coronavirus Spreads, So Does Online Racism Targeting Asians, New Research Shows,” Washington Post, April 9, 2020, https://www.washingtonpost.com/technology/2020/04/08/coronavirus-spreads-so-does-online-racism-targeting-asians-new-research-shows/ (accessed August 12, 2020).

3 World Health Organization, An Ad Hoc Who Technical Consultation Managing the Covid-19 Infodemic: Call for Action (Switzerland: World Health Organization, 2020), https://www.who.int/publications/i/item/9789240010314 (accessed November 12, 2020).

4 Adam M. Enders, Joseph E. Uscinski, Casey Klofstad, and Justin Stoler, “The Different Forms of COVID-19 Misinformation and Their Consequences,” The Harvard Kennedy School Misinformation Review 1, no. 8 (2020): 1–21, doi:10.37016/mr-2020-48, https://doi.org/10.37016/mr-2020-48.

5 Pakpoom Mookdarsanit, and Lawankorn Mookdarsanit, “The Covid-19 Fake News Detection in Thai Social Texts,” Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 2021): 988–98, doi:10.11591/eei.v10i2.2745, https://doi.org/10.11591/eei.v10i2.2745.

6 Wentao Xu and Kazutoshi Sasahara, “Characterizing the Roles of Bots on Twitter During the Covid-19 Infodemic,” Journal of Computational Social Science (2021), doi:10.1007/s42001-021-00139-3, https://doi.org/10.1007/s42001-021-00139-3.

7 Matteo Cinelli et al., “The Echo Chamber Effect on Social Media,” Proceedings of the National Academy of Sciences 118, no. 9 (2021): e2023301118, doi:10.1073/pnas.2023301118, https://doi.org/10.1073/pnas.2023301118.

8 Elia Gabarron, Sunday Oluwafemi Oyeyemi, and Rolf Wynn, “Covid-19-Related Misinformation on Social Media: A Systematic Review,” Bulletin of the World Health Organization 99, no. 6 (June 2021): 455-63a, doi:10.2471/BLT.20.276782, https://doi.org/10.2471/BLT.20.276782.

9 Hoang Linh Dang, “Social Media, Fake News, and the COVID-19 Pandemic: Sketching the Case of Southeast Asia,” Austrian Journal of South-East Asian Studies 14, no. 1 (2021): 37-58, doi:10.14764/10.ASEAS-0054, https://doi.org/10.14764/10.ASEAS-0054; Karen Lee and Andreyka Natalegawa, “Amid COVID-19, Fake News Crackdowns Do Damage Across Southeast Asia,” Diplomat, June 16, 2021, https://thediplomat.com/2021/06/amid-covid-19-fake-news-crackdowns-do-damage-across-southeast-asia/; Victor V. Ramraj, ed., Covid-19 in Asia: Law and Policy Contexts (New York: Oxford University Press, 2021), 65.

10 Sirakij Pornbanggird, “UN Praises Thailand’s Management of Covid-19 Threat,” National News Bureau of Thailand (NNT), June 17, 2020, https://thainews.prd.go.th/en/news/detail/TCATG200617131619377 (accessed February 12, 2021); Alwin Issac et al., “An Examination of Thailand’s Health Care System and Strategies during the Management of the COVID-19 Pandemic,” Journal of Global Health 11 (2021): 03002-03002, doi:10.7189/jogh.11.03002, https://doi.org/10.7189/jogh.11.03002.

11 ETDA, “Thailand Internet User Behavior 2019,” ETDA, May 2020, https://www.etda.or.th/th/Useful-Resource/publications/Thailand-Internet-User-Behavior-2019_EN.aspx (accessed July 10, 2021); ETDA, “รายงานผลการสำรวจพฤติกรรมผู้ใช้อินเทอร์เน็ตในประเทศไทย ปี 2563 Thailand Internet User Behavior 2020,” ETDA, https://www.etda.or.th/th/Useful-Resource/publications/Thailand-Internet-User-Behavior-2020.aspx (accessed July 10, 2021); ETDA, “’งานแถลงผลการสำรวจพฤติกรรมผู้ใช้อินเทอร์เน็ตในประเทศไทย ปี 2564 Thailand Internet User Behavior 2021,” ETDA, https://www.etda.or.th/th/Useful-Resource/publications/Thailand-Internet-User-Behavior-2021_Slides.aspx (accessed March 24, 2022); National Health Commission Office, “รู้ทันข่าวปลอม-ป้องกัน FAKE NEWS ท่ามกลางสถานการณ์โควิด-19 [fake news literacy during COVID-19],” National Health Commission Office (NHCO), https://infocenter.nationalhealth.or.th/node/28170 (accessed March 24, 2022); Royal Thai Government, “ข้อมูลไทยชนะ.คอม ใช้ช่วยสอบสวนควบคุมโรค [Data from Thaichana.Com Used for Investigation and Disease Control],” Royal Thai Government, May 26, 2020, https://www.thaigov.go.th/news/contents/details/31550 (accessed February 12, 2021).ETDA, “Thailand Internet User Behavior 2019,” ETDA, May 2020, https://www.etda.or.th/th/Useful-Resource/publications/Thailand-Internet-User-Behavior-2019_EN.aspx (accessed July 10, 2021); ETDA, “รายงานผลการสำรวจพฤติกรรมผู้ใช้อินเทอร์เน็ตในประเทศไทย ปี 2563 Thailand Internet User Behavior 2020,” ETDA, https://www.etda.or.th/th/Useful-Resource/publications/Thailand-Internet-User-Behavior-2020.aspx (accessed July 10, 2021); ETDA, “’งานแถลงผลการสำรวจพฤติกรรมผู้ใช้อินเทอร์เน็ตในประเทศไทย ปี 2564 Thailand Internet User Behavior 2021,” ETDA, https://www.etda.or.th/th/Useful-Resource/publications/Thailand-Internet-User-Behavior-2021_Slides.aspx (accessed March 24, 2022); National Health Commission Office, “รู้ทันข่าวปลอม-ป้องกัน FAKE NEWS ท่ามกลางสถานการณ์โควิด-19 [fake news literacy during COVID-19],” National Health Commission Office (NHCO), https://infocenter.nationalhealth.or.th/node/28170 (accessed March 24, 2022); Royal Thai Government, “ข้อมูลไทยชนะ.คอม ใช้ช่วยสอบสวนควบคุมโรค [Data from Thaichana.Com Used for Investigation and Disease Control],” Royal Thai Government, May 26, 2020, https://www.thaigov.go.th/news/contents/details/31550 (accessed February 12, 2021).

12 Human Rights Watch, “Thailand: Covid-19 Clampdown on Free Speech,” Human Rights Watch, March 25, 2020, https://www.hrw.org/news/2020/03/25/thailand-covid-19-clampdown-free-speech (accessed February 12, 2021).

13 Robert Smith and Mark Perry, “‘Fake News’ Legislation in Thailand: The Good, the Bad and the Ugly,” Athens Journal of Law 6, no. 3 (2020): 243–64, doi:10.30958/ajl.6-3-3, https://doi.org/10.30958/ajl.6-3-3.

14 Supakit Sirilak, “COVID-19 Infodemic Management: Thailand Experience” (Power-Point Presentation, The 75th Session of the General Assembly of the United Nations, Ministry of Public Health of Thailand, September 23, 2020), https://www.who.int/docs/default-source/coronaviruse/risk-comms-updates/thailand-unga-presentation-infodemic-thailand-21sep2020-final.pdf?sfvrsn=d757509e_6 (accessed July 10, 2021).

15 ETDA, “Thailand Internet User Behavior 2019”; ETDA, “Thailand Internet User Behavior 2020”; ETDA, “Thailand Internet User Behavior 2021”.

16 Robin Goodwin et al., “Anxiety and Public Responses to Covid-19: Early Data from Thailand,” Journal of Psychiatric Research 129 (2020): 118–21, doi:10.1016/j.jpsychires.2021.01.025, https://doi.org/10.1016/j.jpsychires.2021.01.025; Wijitbusaba Marome and Rajib Shaw, “Covid-19 Response in Thailand and Its Implications on Future Preparedness,” International Journal of Environmental Research and Public Health 18, no. 3 (2021), doi:10.3390/ijerph18031089, https://doi.org/10.3390/ijerph18031089; Rapeephan R. Maude et al., “Improving Knowledge, Attitudes and Practice to Prevent Covid-19 Transmission in Healthcare Workers and the Public in Thailand,” BMC Public Health 21, no. 1 (2021): 749, doi:10.1186/s12889-021-10768-y, https://doi.org/10.1186/s12889-021-10768-y.

17 Edson C. Tandoc Jr. et al., “Defining “Fake News”,” Digital Journalism 6, no. 2 (2018): 137–53, doi:10.1080/21670811.2017.1360143, https://doi.org/10.1080/21670811.2017.1360143.

18 Claire Wardle, “Information Disorder: ‘The Techniques We Saw in 2016 Have Evolved’,” First Draft, October 21, 2019, https://firstdraftnews.org/latest/information-disorder-the-techniques-we-saw-in-2016-have-evolved/ (accessed February 12, 2021).

19 Ramona Bran et al., “Learning from Each Other—A Bibliometric Review of Research on Information Disorders,” Sustainability 13, no. 18 (2021): 10094, doi:10.3390/su131810094, https://doi.org/10.3390/su131810094.

20 J. Scott Brennen et al., “Types, Sources, and Claims of Covid-19 Misinformation,” Reuters Institute for the Study of Journalism, April 7, 2020, https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation (accessed May 12, 2020); Liliana María Gutiérrez-Coba, Patricia Coba-Gutiérrez, and Javier Andrés Gómez-Díaz, “Fake News About Covid-19: A Comparative Analysis of Six Ibero-American Countries,” Revista Latina de Comunicación Social 78 (2020): 237–64, doi:10.4185/RLCS-2020-1476, https://www.doi.org/10.4185/RLCS-2020-1476.

21 Kalina Bontcheva et al., Balancing Act: Countering Digital Disinformation While Respecting Freedom of Expression (France: ITU and UNESCO, 2020), https://www.broadbandcommission.org/Documents/working-groups/FoE_Disinfo_Report.pdf (accessed February 12, 2021).

22 Ibid.

23 Cass R. Sunstein, Republic.com 2.0 (Princeton, NJ: Princeton University Press, 2007), 43–5; Zhuang Liu, “The Internet Echo Chamber and the Misinformation of Judges: The Case of Judges’ Perception of Public Support for the Death Penalty in China,” International Review of Law and Economics 69 (March 2022): 106028, doi:10.1016/j.irle.2021.106028, https://doi.org/10.1016/j.irle.2021.106028.

24 Seth Stephens-Davidowitz, Everybody Lies: What the Internet Can Tell Us About Who We Really Are (London, U.K.: Bloosbury Publishing Plc, 2017).

25 Cinelli et al., “The Echo Chamber Effect.”

26 Petter Törnberg, “Echo Chambers and Viral Misinformation: Modeling Fake News as Complex Contagion,” Plos One 13, no. 9 (2018): e0203958, doi:10.1371/journal.pone.0203958, https://doi.org/10.1371/journal.pone.0203958.

27 Seth Flaxman, Sharad Goel, and Justin M. Rao, “Filter Bubbles, Echo Chambers, and Online News Consumption,” Public Opinion Quarterly 80, no. S1 (2016): 298–320, doi:10.1093/poq/nfw006, https://doi.org/10.1093/poq/nfw006.

28 Daniel Röchert et al., “The Networked Context of Covid-19 Misinformation: Informational Homogeneity on Youtube at the Beginning of the Pandemic,” Online Social Networks and Media 26 (2021): 100164, doi:10.1016/j.osnem.2021.100164, https://doi.org/10.1016/j.osnem.2021.100164.

29 Ludovic Terren and Rosa Borge-Bravo, “Echo Chambers on Social Media: A Systematic Review of the Literature,” Review of Communication Research 9 (2021): 99–118, https://rcommunicationr.org/index.php/rcr/article/view/94.

30 John Scott and Peter J. Carrington, eds., The Sage Handbook of Social Network Analysis (London, 2014), 11–22.

31 Nitin Agarwal, Nima Dokoohaki, and Serpil Tokdemir, eds., Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining (Switzerland: Springer, 2019), 43–65; Monther Aldwairi and Ali Alwahedi, “Detecting Fake News in Social Media Networks,” Procedia Computer Science 141(2018): 215–22, doi:10.1016/j.procs.2018.10.171, https://doi.org/10.1016/j.procs.2018.10.171.

32 Zilong Zhao et al., “Fake News Propagates Differently from Real News Even at Early Stages of Spreading,” EPJ Data Science 9, no. 1 (2020): 7, doi:10.1140/epjds/s13688-020-00224-z, https://doi.org/10.1140/epjds/s13688-020-00224-z.

33 Mingxi Cheng et al., “Deciphering the Laws of Social Network-Transcendent Covid-19 Misinformation Dynamics and Implications for Combating Misinformation Phenomena,” Scientific Reports 11, no. 10424 (2021), doi:10.1038/s41598-021-89202-7, https://doi.org/10.1038/s41598-021-89202-7.

34 James R. Ashford et al., “Understanding the Characteristics of Covid-19 Misinformation Communities through Graphlet Analysis,” Online Social Networks and Media 27 (2022): 100178, doi:10.1016/j.osnem.2021.100178, https://doi.org/10.1016/j.osnem.2021.100178.

35 Dandan Wang and Yuxing Qian, “Echo Chamber Effect in Rumor Rebuttal Discussions About Covid-19 in China: Social Media Content and Network Analysis Study,” Journal of medical Internet research 23, no. 3 (March 2021): e27009, doi:10.2196/27009, https://doi.org/10.2196/27009. Here, SNA is used incorporation with other methods: content analysis and sentiment analysis.

36 Maria Glenski, Svitlana Volkova, and Srijan Kumar, “User Engagement with Digital Deception.” In Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities, eds. Kai Shu et al. (Switzerland: Springer Nature Switzerland AG, 2020).

37 Andrew Duffy, Edson Tandoc, and Rich Ling, “Too Good to Be True, Too Good Not to Share: The Social Utility of Fake News,” Information, Communication & Society 23, no. 13 (2019): 1965–79, doi:10.1080/1369118X.2019.1623904, https://doi.org/10.1080/1369118X.2019.1623904.

38 Brennen et al., “Types, Sources, and Claims of Covid-19 Misinformation.”

39 Douglas Rushkoff, David Pescovitz, and Jake Dunagan, The Biology of Disinformation: Memes, Media Viruses, and Cultural Inoculation (Palo Alto, CA: Institute for the Future, 2018), https://www.iftf.org/fileadmin/user_upload/images/ourwork/digintel/IFTF_biology_of_disinformation_062718.pdf (accessed May 12, 2020).

40 Toni G. L. A. Van der Meer, Michael Hameleers, and Anne C. Kroon, “Crafting Our Own Biased Media Diets: The Effects of Confirmation, Source, and Negativity Bias on Selective Attendance to Online News,” Mass Communication and Society 23, no. 6 (2020): 937–67, doi:10.1080/15205436.2020.1782432, https://doi.org/10.1080/15205436.2020.1782432; Norman Vasu, Benjamin Ang, and Shashi Jayakumar, eds., Drums: Distortions, Rumours, Untruths, Misinformation, and Smears (Singapore: World Scientific Publishing, 2019), 21–30.

41 Brennen et al., “Types, Sources, and Claims of Covid-19 Misinformation”; Julie Posetti and Kalina Bontcheva, Disinfodemic: Deciphering Covid-19 Disinformation (France: UNESCO, 2020), https://en.unesco.org/sites/default/files/disinfodemic_deciphering_covid19_disinformation.pdf (accessed February 12, 2021), 6.

42 National Electronics and Computer Technology Center (NECTEC), “S-Sense: Social Sensing,” NECTEC, last modified September 22, 2016. https://www.nectec.or.th/innovation/innovation-software/s-sense.html; National Electronics and Computer Technology Center (NECTEC), “AI for Thai:แพล็ตฟอร์ม AI สัญชาติไทย [AI for Thai: Thai national AI platform],” NECTEC, last modified September 6, 2019, https://www.nectec.or.th/innovation/innovation-software/aiforthai.html; National Electronics and Computer Technology Center (NECTEC), ““AI for Thai” พลิกโฉมดิจิทัลทรานฟอร์เมชันด้วยปัญญาประดิษฐ์ [AI for Thai: Digital Transformation with AI],” NECTEC, last modified September 9, 2019, https://www.nectec.or.th/research/research-project/aiforthai-digitaltransformation.html; National Electronics and Computer Technology Center (NECTEC), “About NECTEC,”NECTEC, last modified January 19, 2022, https://www.nectec.or.th/en/about-nectec/profile.html.

43 Gabe Ignatow and Rada Mihalcea, An Introduction to Text Mining: Research Design, Data Collection, and Analysis (Thousand Oaks, CA: SAGE Publications, 2018), 171–86. Text classification is one of the common supervised approaches—approaches to categorize text into predefined categories based on given examples—used to perform sentiment analyses.

44 Dipanjan Sarkar, Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data (New York: Springer Science+Business Meida New York, 2016), 199–204; Laura Igual and Santi Seguí, Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Cham, Switzerland: Springer Nature, 2017), 72.

45 Filippo Menczer, Santo Fortunato, and Clayton A. Davis, A First Course in Network Science (Cambridge, U.K.: Cambridge University Press, 2020), 36–39.

46 The category of “medical information” is expanded into two following subcategories. “medical info_mask” refers to content containing what seems to be medical information about protective masks, and “medical info_test” refers to content containing medical information about COVID-19 test kits. “Vaccine” is expanded into seven subcategories. “vaccine_celebrities” refers to information concerning vaccines and celebrities or prominent figures, “vaccine_conspiracy theory” refers conspiracy theory concerning vaccine, “vaccine_effects” concerns the adverse side effects of COVID-19 vaccines, “vaccine_foreign” refers to content about vaccine in the foreign contexts, “vaccine_medical info” refers to medical information concerning vaccines, “vaccine_phishing” refers to content tricking users into giving away their information and/or tricking users into visiting a certain website, and “vaccine_politcs” refers to politicized information concerning vaccines.

47 The category “variety” refers to content containing more than one issues, overlapping more than one tag such as CoFact’s report covering 10 pieces of COVID-19 fake news.

48 Fact-checked messages, here, refer to messages containing information from fact-checkers operating in Thai, namely Anti-Fake News Center Thailand, AFP, อ๋อ มันเป็นอย่างนี้นี่เอง by อาจารย์เจษฎ์ (OhISeebyAjarnJess), ชัวร์ก่อนแชร์ (SureAndShare), and CoFact as well as the key health authorities such as the Ministry of Public Health, the Department of Disease Control, and the Knowledge Center for COVID-19.

49 Kai Shu et al., “Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements.” In Disinformation, Misinformation, and Fake News, eds. Kai Shu et al. (Cham, Switzerland: Springer Nature Switzerland AG, 2020).

50 Stephen G. Kobourov, “Force-Directed Drawing Algorithms.” In Handbook of Graph Drawing and Visualization, ed. Roberto Tamassia (Boca Raton, FL: CRC Press, 2014), 383.

51 Mathieu Jacomy et al., “ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software,” Plos One 9, no. 6 (2014): 1–12, doi:10.1371/journal.pone.0098679, https://doi.org/10.1371/journal.pone.0098679.

52 Ken Cherven, Network Graph Analysis and Visualization with Gephi (Birmingham, U.K.: Packt Publishing, 2013), 25.

53 Devangana Khokhar, Gephi Cookbook (Birmingham, U.K.: Packt Publishing, 2015), 64–73.

54 Carrington, The Sage Handbook of Social Network, 32, 354.

55 Cherven, Network Graph Analysis, 74.

56 Cherven, Network Graph Analysis, 75; Carrington, The Sage Handbook of Social Network, 366–67.

57 Khokhar, Gephi Cookbook, 128.

58 Subhabhong Rarueysong, “Thai Cabinet Approves Use of Fah Talai Jone to Treat Asymptomatic Covid-19 Cases,” National News Bureau of Thailand (NNT), July 28, 2021, https://thainews.prd.go.th/en/news/detail/TCATG210728114450300.

59 Manit Sanubboon, “Herbal Treatment in High Demand Amid Covid Threat,” Bangkok Post, July 14, 2021, https://www.bangkokpost.com/thailand/general/2148659/herbal-treatment-in-high-demand-amid-covid-threat.

60 Thai PBS World, “Thai Government May Impose 24-Hour Curfew,” Thai PBS World, April 3, 2020, https://www.thaipbsworld.com/thai-government-may-impose-24-hour-curfew/ (accessed February 12, 2021); Saksit Pradabsilp, “นายกรัฐมนตรี ระบุ ประเมินมาตรการเคอร์ฟิว ทุกวัน จนครบ 1 สัปดาห์ หากตัวเลขผู้ป่วยยังเพิ่ม อาจใช้เคอร์ฟิว 24 ชั่วโมง [the Curfew Will Be Assessed on a Daily Basis for the Next Seven Days and If the Number of Confirmed Cases Is High, a 24-Hour Curfew May Be Imposed, Said the PM],” National News Bureau of Thailand (NNT), April 3, 2020, https://nbtworld.prd.go.th/th/news/print_news/TCATG200403101750877 (accessed February 12, 2021).

61 Anti-Fake News Center Thailand, “ข่าวปลอม อย่าแชร์! ❌ ลือ เคอร์ฟิว 24 ชม.เริ่มเสาร์-อาทิตย์นี้ ต้องตุนอาหารและเครื่องดื่ม [Fake News Don’t Share! Rumor 24-Hour Curfew on This Weekend, Stock up on Food and Beverages],” Anti-Fake News Center Thailand, April 9, 2020, https://www.facebook.com/AntiFakeNewsCenter/photos/a.113638500070332/217933346307513 (accessed February 12, 2021); Thai PBS World, “No 24-Hour Curfew in Thailand yet—Government Spokeswoman,” Thai PBS World, April 6, 2020, https://www.thaipbsworld.com/no-24-hour-curfew-in-thailand-yet-government-spokeswoman/ (accessed February 12, 2021).

62 Susan Pennings and Xavier Symons, “Persuasion, not coercion or incentivisation, is the best means of promoting COVID-19 vaccination,” Journal of Medical Ethics 47, no. 10 (2021): 709. https://doi.org/10.1136/medethics-2020-107076.

63 Pattamon Anansaringkarn and Ric Neo, “How Can State Regulations over the Online Sphere Continue to Respect the Freedom of Expression? A Case Study of Contemporary ‘Fake News’ Regulations in Thailand,” Information & Communications Technology Law (2021): 1–21, doi:10.1080/13600834.2020.1857789, https://doi.org/10.1080/13600834.2020.1857789.