论文标题
欢迎来到Gab Alt Right Cillses
Welcome to Gab Alt Right Discourses
论文作者
论文摘要
社交媒体已成为不同群体共享信息,讨论政治问题和组织社会运动的重要场所。最近的奖学金表明,社交媒体生态系统会影响政治思维和表达。各个政治领域的个人和团体都广泛地从事这些平台的使用,甚至以各种方式来制定自己的论坛,以换句话说,以追求更自由的言论标准。在这种情况下,GAB社交媒体平台出现了。 GAB是所谓ALT权利的社交媒体平台,大多数流行媒体都介绍了在GAB和类似平台上的话语的主题内容,但是很少的研究很少研究内容本身。本文使用从2016年8月到2019年7月的所有GAB帖子的公开可用数据集,当前的论文探讨了该数据集的5%随机示例,以探索平台上的主题内容。我们使用标准程序运行多个结构性主题模型,以达到最佳的k个主题。最终模型指定了403,469个文档的85个主题。我们将其包括为流行变量,是否已将源帐户标记为机器人和源帐户的关注者数量。结果表明,数据集中最大的主题与大屠杀的真实性,红色药丸的含义以及主流媒体的新闻优点有关。最后,我们讨论了我们的发现对道德内容适度,在线社区发展,政治两极分化以及未来研究的途径的含义。
Social media has become an important venue for diverse groups to share information, discuss political issues, and organize social movements. Recent scholarship has shown that the social media ecosystem can affect political thinking and expression. Individuals and groups across the political spectrum have engaged in the use of these platforms extensively, even creating their own forums with varying approaches to content moderation in pursuit of freer standards of speech. The Gab social media platform arose in this context. Gab is a social media platform for the so-called alt right, and much of the popular press has opined about the thematic content of discourses on Gab and platforms like it, but little research has examined the content itself. Using a publicly available dataset of all Gab posts from August 2016 until July 2019, the current paper explores a five percent random sample of this dataset to explore thematic content on the platform. We run multiple structural topic models, using standard procedures to arrive at an optimal k number of topics. The final model specifies 85 topics for 403,469 documents. We include as prevalence variables whether the source account has been flagged as a bot and the number of followers for the source account. Results suggest the most nodal topics in the dataset pertain to the authenticity of the Holocaust, the meaning of red pill, and the journalistic merit of mainstream media. We conclude by discussing the implications of our findings for work in ethical content moderation, online community development, political polarization, and avenues for future research.