分析印度大选期间用户通过WhatsApp举报的580条谣言，揭示跨语言传播规律与辟谣效率。
WhatsApp Tiplines and Multilingual Claims in the 2021 Indian Assembly Elections
- 通过混合方法分析451名用户提交的580条多语言谣言
- 多数谣言在英语、印地语和泰卢固语间存在内容相似性，需2天左右辟谣
- 用户不跨组织举报，各机构拥有独立受众，适合选举期信息治理参考
WhatsApp举报渠道于2019年首次推出，旨在打击虚假信息，允许用户向事实核查机构提交待验证内容。本研究采用混合方法，分析2021年印度议会选举期间451名用户提交的580条独特谣言（含高资源语言如英语、印地语及低资源语言泰卢固语）。将谣言分为选举类、新冠相关及其他三类，发现不同语言间存在内容相似性。通过高频词分析与神经句嵌入聚类比较内容相似度，同时考察用户跨语言重叠及事实核查机构间的用户分布。结果显示，事实核查机构平均需数日时间驳斥新谣言并通知举报者。值得注意的是，无用户向多个核查机构提交同一内容，表明各机构拥有独立受众。研究提出选举期间使用举报渠道的实践建议，并强调对用户信息伦理的考量。
原文摘要 · Abstract (English)
WhatsApp tiplines, first launched in 2019 to combat misinformation, enable users to interact with fact-checkers to verify misleading content. This study analyzes 580 unique claims (tips) from 451 users, covering both high-resource languages (English, Hindi) and a low-resource language (Telugu) during the 2021 Indian assembly elections using a mixed-method approach. We categorize the claims into three categories, election, COVID-19, and others, and observe variations across languages. We compare content similarity through frequent word analysis and clustering of neural sentence embeddings. We also investigate user overlap across languages and fact-checking organizations. We measure the average time required to debunk claims and inform tipline users. Results reveal similarities in claims across languages, with some users submitting tips in multiple languages to the same fact-checkers. Fact-checkers generally require a couple of days to debunk a new claim and share the results with users. Notably, no user submits claims to multiple fact-checking organizations, indicating that each organization maintains a unique audience. We provide practical recommendations for using tiplines during elections with ethical consideration of users' information.
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