论文标题
通过在Twitter上存在的偏见来自动表征有针对性的信息操作
Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter
论文作者
论文摘要
本文考虑了自动表征与新兴信息操作相关的整体态度和偏见的问题。对这些新兴主题的准确分析通常需要专家的费力，手动分析，以注释数百万推文，以识别新主题的偏见。我们介绍了Caliskan等人的嵌入嵌入关联测试的扩展。到一个新领域（Caliskan，2017年）。我们的实用且无监督的方法用于量化在信息操作中促进的偏见。我们使用Twitter透明度报告中的已知信息操作相关推文验证我们的方法。我们对COVID-19大流行进行了一项案例研究，以评估我们在未标记的Twitter数据上的方法，以证明其在新兴域中的可用性。
This paper considers the problem of automatically characterizing overall attitudes and biases that may be associated with emerging information operations via artificial intelligence. Accurate analysis of these emerging topics usually requires laborious, manual analysis by experts to annotate millions of tweets to identify biases in new topics. We introduce extensions of the Word Embedding Association Test from Caliskan et al. to a new domain (Caliskan, 2017). Our practical and unsupervised method is used to quantify biases promoted in information operations. We validate our method using known information operation-related tweets from Twitter's Transparency Report. We perform a case study on the COVID-19 pandemic to evaluate our method's performance on non-labeled Twitter data, demonstrating its usability in emerging domains.
