论文标题
LAMBRETTA：学习排名为Twitter软件
LAMBRETTA: Learning to Rank for Twitter Soft Moderation
论文作者
论文摘要
为了遏制虚假信息的问题，Twitter等社交媒体平台开始将警告标签添加到讨论揭穿叙事的内容中，以便为观众提供更多背景。不幸的是，这些标签不统一地应用，并且留下大量的错误内容。本文介绍了Lambretta，该系统自动识别推文，这些推文是使用学习排名（LTR）进行软调节的候选者。我们在Twitter数据上运行LAMBRETTA，以与2020年美国选举有关的中度虚假索赔，发现它的标志超过了Twitter的20倍以上的推文，只有3.93％的假阳性和18.81％的假否定词，超过了基于关键字提取和语义搜索的替代性替代方法。总体而言，Lambretta有助于人类主持人在社交媒体上识别和标记虚假信息。
To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run LAMBRETTA on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.
