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Identifying Users with Opposing Opinions in Twitter Debates

机译:在Twitter辩论中识别反对意见的用户

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摘要

In recent times, social media sites such as Twitter have been extensively used for debating politics and public policies. These debates span millions of tweets and numerous topics of public importance. Thus, it is imperative that this vast trove of data is tapped in order to gain insights into public opinion especially on hotly contested issues such as abortion, gun reforms etc. Thus, in our work, we aim to gauge users' stance on such topics in Twitter. We propose ReLP, a semi-supervised framework using a retweet-based label propagation algorithm coupled with a supervised classifier to identify users with differing opinions. In particular, our framework is designed such that it can be easily adopted to different domains with little human supervision while still producing excellent accuracy.
机译:近年来,诸如Twitter之类的社交媒体网站已广泛用于辩论政治和公共政策。这些辩论涉及数百万条推文和众多具有公共重要性的主题。因此,必须利用大量的数据,以便深入了解舆论,尤其是在激烈争论的问题上,如堕胎,枪支改革等。因此,在我们的工作中,我们旨在评估用户在此类话题上的立场在Twitter中。我们提出ReLP,一种半监督框架,该框架使用基于转发的标签传播算法和监督分类器来识别具有不同意见的用户。尤其是,我们的框架经过精心设计,可在不需人工监督的情况下轻松应用于不同领域,同时仍可产生出色的准确性。

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