Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis

被引:10
作者
Al-Rawi, Ahmed [1 ,2 ]
Fakida, Abdelrahman [1 ]
Grounds, Kelly [1 ]
机构
[1] Simon Fraser Univ, Sch Commun, Burnaby, BC, Canada
[2] Simon Fraser Univ, Schrum Sci Ctr, Sch Commun, K 9653, Burnaby, BC V5A 1S6, Canada
来源
JMIR INFODEMIOLOGY | 2022年 / 2卷 / 02期
关键词
COVID-19; Arab world; Twitter; misinformation; vaccination; infodemiology; vaccine hesitancy; infoveillance; health information; social media; social media content; content analysis; Twitter analysis; FAKE NEWS;
D O I
10.2196/37007
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: The COVID-19 pandemic has been occurring concurrently with an infodemic of misinformation about the virus. Spreading primarily on social media, there has been a significant academic effort to understand the English side of this infodemic. However, much less attention has been paid to the Arabic side. Objective: There is an urgent need to examine the scale of Arabic COVID-19 disinformation. This study empirically examines how Arabic speakers use specific hashtags on Twitter to express antivaccine and antipandemic views to uncover trends in their social media usage. By exploring this topic, we aim to fill a gap in the literature that can help understand conspiracies in Arabic around COVID-19. Methods: This study used content analysis to understand how 13 popular Arabic hashtags were used in antivaccine communities. We used Twitter Academic API v2 to search for the hashtags from the beginning of August 1, 2006, until October 10, 2021. After downloading a large data set from Twitter, we identified major categories or topics in the sample data set using emergent coding. Emergent coding was chosen because of its ability to inductively identify the themes that repeatedly emerged from the data set. Then, after revising the coding scheme, we coded the rest of the tweets and examined the results. In the second attempt and with a modified codebook, an acceptable intercoder agreement was reached (Krippendorff alpha >=.774). Results: In total, we found 476,048 tweets, mostly posted in 2021. First, the topic of infringing on civil liberties (n=483, 41.1%) covers ways that governments have allegedly infringed on civil liberties during the pandemic and unfair restrictions that have been imposed on unvaccinated individuals. Users here focus on topics concerning their civil liberties and freedoms, claiming that governments violated such rights following the pandemic. Notably, users denounce government efforts to force them to take any of the COVID-19 vaccines for different reasons. This was followed by vaccine-related conspiracies (n=476, 40.5%), including a Deep State dictating pandemic policies, mistrusting vaccine efficacy, and discussing unproven treatments. Although users tweeted about a range of different conspiracy theories, mistrusting the vaccine's efficacy, false or exaggerated claims about vaccine risks and vaccine-related diseases, and governments and pharmaceutical companies profiting from vaccines and intentionally risking the general public health appeared the most. Finally, calls for action (n=149, 12.6%) encourage individuals to participate in civil demonstrations. These calls range from protesting to encouraging other users to take action about the vaccine mandate. For each of these categories, we also attempted to trace the logic behind the different categories by exploring different types of conspiracy theories for each category. Conclusions: Based on our findings, we were able to identify 3 prominent topics that were prevalent amongst Arabic speakers on Twitter. These categories focused on violations of civil liberties by governments, conspiracy theories about the vaccines, and calls for action. Our findings also highlight the need for more research to better understand the impact of COVID-19 disinformation on the Arab world. (JMIR Infodemiology 2022;2(2):e37007) doi: 10.2196/37007
引用
收藏
页数:10
相关论文
共 50 条
[31]   Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification [J].
Turner, Jason ;
Kantardzic, Mehmed ;
Vickers-Smith, Rachel ;
Brown, Andrew G. .
JMIR INFODEMIOLOGY, 2023, 3 (01)
[32]   Covid-19 vaccine hesitancy on English-language Twitter [J].
Thelwall, Mike ;
Kousha, Kayvan ;
Thelwall, Saheeda .
PROFESIONAL DE LA INFORMACION, 2021, 30 (02)
[33]   An Infodemiology and Infoveillance Study on COVID-19: Analysis of Twitter and Google Trends [J].
Alshahrani, Reem ;
Babour, Amal .
SUSTAINABILITY, 2021, 13 (15)
[34]   Virality, only the tip of the iceberg: ways of spread and interaction around COVID-19 misinformation in Twitter [J].
Villar-Rodriguez, Guillermo ;
Souto-Rico, Monica ;
Martin, Alejandro .
COMMUNICATION & SOCIETY-SPAIN, 2022, 35 (02) :239-256
[35]   COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework [J].
Griffith, Janessa ;
Marani, Husayn ;
Monkman, Helen .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2021, 23 (04)
[36]   Prevalence and source analysis of COVID-19 misinformation in 138 countries [J].
Al-Zaman, Md Sayeed .
IFLA JOURNAL-INTERNATIONAL FEDERATION OF LIBRARY ASSOCIATIONS, 2022, 48 (01) :189-204
[37]   Sex Workers' Lived Experiences With COVID-19 on Social Media: Content Analysis of Twitter Posts [J].
Al-Rawi, Ahmed ;
Zemenchik, Kiana .
JMIR FORMATIVE RESEARCH, 2022, 6 (07)
[38]   Dissemination of Misinformation About COVID-19 on TikTok: A Multimodal Analysis [J].
Patel, Kesha A. ;
Thakur, Nirmalya .
HCI INTERNATIONAL 2024 POSTERS, PT VI, HCII 2024, 2024, 2119 :109-120
[39]   Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study [J].
Strydom, Irene Francesca ;
Grobler, Jacomine .
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2022, PT I, 2023, 13810 :197-210
[40]   Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter [J].
Wang Y. ;
Ling C. ;
Stringhini G. .
Proceedings of the ACM on Human-Computer Interaction, 2023, 7 (CSCW1)