论文标题
信息操纵检测的挑战和机遇：战时俄罗斯媒体的检查
Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media
论文作者
论文摘要
NLP关于公众舆论操纵运动的研究主要集中于检测诸如假新闻和虚假信息之类的明显策略。但是，正在进行的俄罗斯 - 乌克兰战争中的信息操纵体现了政府和媒体如何采用更细微的策略。我们发布了一个新的数据集Voynaslov，其中包含Twitter和Vkontakte上俄罗斯媒体的38M帖子，以及公共活动和回应，紧接在2022年的俄罗斯 - 乌克兰战争之前。我们在Voynaslov上应用标准且最近开发的NLP模型来检查信息操纵的基础设置，框架和启动，并揭示媒体媒体控制，社交媒体平台和时间之间的差异。我们对这些媒体效果的检查以及对当前方法的局限性的广泛讨论鼓励进一步开发NLP模型，以了解新兴危机中的信息操纵以及其他现实世界和跨学科任务。
NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation. However, information manipulation in the ongoing Russia-Ukraine war exemplifies how governments and media also employ more nuanced strategies. We release a new dataset, VoynaSlov, containing 38M+ posts from Russian media outlets on Twitter and VKontakte, as well as public activity and responses, immediately preceding and during the 2022 Russia-Ukraine war. We apply standard and recently-developed NLP models on VoynaSlov to examine agenda setting, framing, and priming, several strategies underlying information manipulation, and reveal variation across media outlet control, social media platform, and time. Our examination of these media effects and extensive discussion of current approaches' limitations encourage further development of NLP models for understanding information manipulation in emerging crises, as well as other real-world and interdisciplinary tasks.
