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Detection of conspiracy propagators using psycho-linguistic characteristics
Journal of Information Science ( IF 2.4 ) Pub Date : 2021-01-27 , DOI: 10.1177/0165551520985486
Anastasia Giachanou 1 , Bilal Ghanem 2 , Paolo Rosso 3
Affiliation  

The rise of social media has offered a fast and easy way for the propagation of conspiracy theories and other types of disinformation. Despite the research attention that has received, fake news detection remains an open problem and users keep sharing articles that contain false statements but which they consider real. In this article, we focus on the role of users in the propagation of conspiracy theories that is a specific type of disinformation. First, we compare profile and psycho-linguistic patterns of online users that tend to propagate posts that support conspiracy theories and of those who propagate posts that refute them. To this end, we perform a comparative analysis over various profile, psychological and linguistic characteristics using social media texts of users that share posts about conspiracy theories. Then, we compare the effectiveness of those characteristics for predicting whether a user is a conspiracy propagator or not. In addition, we propose ConspiDetector, a model that is based on a convolutional neural network (CNN) and which combines word embeddings with psycho-linguistic characteristics extracted from the tweets of users to detect conspiracy propagators. The results show that ConspiDetector can improve the performance in detecting conspiracy propagators by 8.82% compared with the CNN baseline with regard to F1-metric.



中文翻译:

使用心理语言特征检测阴谋传播者

社交媒体的兴起为阴谋理论和其他虚假信息的传播提供了一种快速简便的方法。尽管受到了研究的关注,但虚假新闻检测仍然是一个未解决的问题,用户继续共享包含虚假陈述但他们认为是真实的文章。在本文中,我们重点关注用户在阴谋理论传播中的作用,这是一种特殊的虚假信息。首先,我们比较倾向于传播支持阴谋论的帖子的在线用户的个人资料和心理语言模式,以及传播反驳他们的帖子的用户的个人资料和心理语言模式。为此,我们使用共享阴谋理论帖子的用户社交媒体文本对各种个人资料,心理和语言特征进行比较分析。然后,我们比较了这些特征在预测用户是否为阴谋传播者方面的有效性。此外,我们提出了ConspiDetector,这是一个基于卷积神经网络(CNN)的模型,该模型将单词嵌入与从用户推文中提取的心理语言特征相结合,以检测阴谋传播者。结果显示,就F1指标而言,ConspiDetector与CNN基线相比,可以提高检测共谋传播者的性能8.82%。一种基于卷积神经网络(CNN)的模型,该模型将单词嵌入与从用户推文中提取的心理语言特征相结合,以检测阴谋传播者。结果显示,就F1指标而言,ConspiDetector与CNN基线相比,可以提高检测共谋传播者的性能8.82%。一种基于卷积神经网络(CNN)的模型,该模型将单词嵌入与从用户推文中提取的心理语言特征相结合,以检测阴谋传播者。结果显示,就F1指标而言,ConspiDetector与CNN基线相比,可以提高检测共谋传播者的性能8.82%。

更新日期:2021-01-28
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