The Evolution of Rumors on a Closed Social Networking Platform During COVID-19: Algorithm Development and Content Study

Abstract

Background In 2020, the COVID-19 pandemic put the world in a crisis regarding both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it as an infodemic in February 2020. Objective The aim of this study was to examine the propagation patterns and textual transformation of COVID-19–related rumors on a closed social media platform. Methods We obtained a data set of suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE, between January and July 2020. We proposed a classification-based clustering algorithm that could efficiently cluster messages into groups, with each group representing a rumor. For ease of understanding, a group is referred to as a “rumor group.” Messages in a rumor group could be identical or could have limited textual differences between them. Therefore, each message in a rumor group is a form of the rumor. Results A total of 936 rumor groups with at least 10 messages each were discovered among 114,124 text messages collected from LINE. Among 936 rumors, 396 (42.3%) were related to COVID-19. Of the 396 COVID-19–related rumors, 134 (33.8%) had been fact-checked by the International Fact-Checking Network–certified agencies in Taiwan and determined to be false or misleading. By studying the prevalence of simplified Chinese characters or phrases in the messages that originated in China, we found that COVID-19–related messages, compared to non–COVID-19–related messages, were more likely to have been written by non-Taiwanese users. The association was statistically significant, with P<.001, as determined by the chi-square independence test. The qualitative investigations of the three most popular COVID-19 rumors revealed that key authoritative figures, mostly medical personnel, were often misquoted in the messages. In addition, these rumors resurfaced multiple times after being fact-checked, usually preceded by major societal events or textual transformations. Conclusions To fight the infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media has given rise to an unprecedented number of unverified rumors, it also provides a unique opportunity for us to study the propagation of rumors and their interactions with society. Therefore, we must put more effort into these areas.

Complementary, alternative, and integrative medicine-specific COVID-19 misinformation on social media: A scoping review

“…Of those, 116 articles were excluded as they lacked sufficient CAIM for inclusion. This left 28 articles for data extraction within the scoping review 31 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ; A PRISMA flow diagram depicting this process is provided as Fig. 1 .…”

“… Wagner (2020) 64 Canada Qualitative (content analysis) ( n = 28) Prevention Instagram The frequency and importance of posts about "immune boosting" and its portrayal as beneficial or if the concept was critiqued. Wang (2021) 63 Taiwan Qualitative (algorithm development and content study) ( n = 114 124) Prevention LINE Reported messages containing COVID-19-related suspicious content (misinformation) Zhang (2021) 65 China Exploratory ( n = 2754) Prevention/Treatment Q&A platforms, video-sharing platforms, chat platforms, news-sharing, health care platforms Daily number of COVID-19-related posts on Chinese social media and changes in post numbers over time periods. CNR: not reported.…”

“…Many of the studies further specified the types of misinformation reviewed by subgrouping them into different categories. 31 , 43 , 49 , 54 , 57 , 60 , 63 , 64 Three studies aimed to illustrate the most common trends in misinformation and categorized their data accordingly. For example, Kawchuk et al.…”

“… 54 Other studies took a similar approach, breaking down misinformation into the most commonly mentioned misinformation archetypes such as false claims, conspiracy theories, and pseudoscientific health therapies. 31 , 63 Some studies aimed to categorize the false claims based on the specific CAIM therapy the misinformation recommended, giving the number or percentage of tweets that mentioned therapies including herbs, zinc, vitamin C, natural drugs, synthetic drugs, sunbathing, or healthy eating. 49 , 57 , 60 Finally, other studies categorized the misinformation based on descriptive characteristics of the source of misinformation itself.…”

“…Moreover, some studies were conducted based on participant self-reporting which is a method of reporting known to be susceptible to self-biases. 39 , 40 , 41 , 44 , 56 , 63 There are also concerns about the type of misinformation being spread by private users on these platforms, as this subset of information was inaccessible to researchers and is thus excluded from the data. 42 , 50 Other studies had questionable sample characteristics, such as one in which conclusions were based on a sample of only 26 participants, 39 and another which had an imbalance in the number of participants in their comparison groups.…”