Then, we perform an in-depth qualitative study, using thematic analysis, on 222 COVID-19 related videos to assess the content and the connection between the content and the warning labels. Second, we perform a quantitative analysis on the entire dataset to understand the use of warning labels on TikTok. In this work, we analyze the use of warning labels on TikTok, focusing on COVID-19 videos.įirst, we construct a set of 26 COVID-19 related hashtags, and then we collect 41K videos that include those hashtags in their description. Such interventions aim to inform users about the post's content without removing it, hence easing the public's concerns about censorship and freedom of speech.ĭespite the recent popularity of these moderation interventions, as a research community, we lack empirical analyses aiming to uncover how these warning labels are used in the wild, particularly during challenging times like the COVID-19 pandemic.
In an attempt to mitigate this adverse effect, mainstream social media platforms like Facebook, Twitter, and TikTok employed soft moderation interventions (i.e., warning labels) on potentially harmful posts.
Web tik tok viewer software#
Max Planck Institute for Software SystemsĬredibility of online content, Qualitative and quantitative studies of social media, Trust reputation recommendation systems, Web and Social Media Abstractĭuring the COVID-19 pandemic, health-related misinformation and harmful content shared online had a significant adverse effect on society.
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