论文标题
“了解有关COVID-19的事实”：分析在Tiktok视频上使用警告标签
"Learn the Facts About COVID-19": Analyzing the Use of Warning Labels on TikTok Videos
论文作者
论文摘要
在19日期，与健康有关的错误信息和在线共享的有害内容对社会产生了重大不利影响。为了减轻这种不利影响，主流社交媒体平台采用了软件干预（即警告标签），对潜在有害帖子进行了软件干预措施（即警告标签）。尽管这些节制干预措施最近流行，但我们缺乏旨在揭示这些警告标签如何在野外使用的经验分析，尤其是在诸如Covid-19-19-19的挑战时期。在这项工作中，我们分析了Tiktok上警告标签的使用，重点介绍了Covid-19视频。首先，我们构建了一组26 Covid-19相关的主题标签，然后我们收集了41k个视频，其中包括这些主题标签。其次，我们对整个数据集进行了定量分析，以了解Tiktok上警告标签的使用。然后，我们使用主题分析对222 Covid-19相关视频进行了深入的定性研究，以评估内容和警告标签之间的内容和连接。我们的分析表明，Tiktok广泛地应用了Tiktok视频的警告标签，这可能是根据描述中包含的主题标签的。更令人担忧的是，在视频中增加了COVID-19警告标签，其中其实际内容与COVID-19无关（在与Covid-19无关的143个英语视频样本中的23％的案例中，有23％）。最后，我们对222个视频样本的定性分析表明，有7.7％的视频共享错误信息/有害内容，不包括警告标签，37.3％共享良性信息并包括警告标签，其中35％的视频共享错误的信息/有害内容（并且需要警告标签）可以使您变得有趣。我们的研究表明，有必要开发更准确，更精确的柔和适量系统，尤其是在像Tiktok这样的平台上，在年轻人中非常受欢迎。
During the COVID-19 pandemic, health-related misinformation and harmful content shared online had a significant adverse effect on society. To mitigate this adverse effect, mainstream social media platforms employed soft moderation interventions (i.e., warning labels) on potentially harmful posts. Despite the recent popularity of these moderation interventions, 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 this work, we analyze the use of warning labels on TikTok, focusing on COVID-19 videos. First, we construct a set of 26 COVID-19 related hashtags, then we collect 41K videos that include those hashtags in their description. Second, we perform a quantitative analysis on the entire dataset to understand the use of warning labels on TikTok. 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. Our analysis shows that TikTok broadly applies warning labels on TikTok videos, likely based on hashtags included in the description. More worrying is the addition of COVID-19 warning labels on videos where their actual content is not related to COVID-19 (23% of the cases in a sample of 143 English videos that are not related to COVID-19). Finally, our qualitative analysis on a sample of 222 videos shows that 7.7% of the videos share misinformation/harmful content and do not include warning labels, 37.3% share benign information and include warning labels, and that 35% of the videos that share misinformation/harmful content (and need a warning label) are made for fun. Our study demonstrates the need to develop more accurate and precise soft moderation systems, especially on a platform like TikTok that is extremely popular among people of younger age.
