Retweets are essential to boost the popularity of a tweet, and a large number of fake retweeters can contribute heavily to this aspect. We define a fake retweeter as a Twitter account that retweets spammy tweets, retweets an abnormally large amount of tweets in a short period, or misuses a trending hashtag to promote events irrelevant to the topic of discussion. We introduce an up-to-date, temporally diverse, trend-oriented labeled dataset to address the problem of fake retweeter detection. We develop a novel classifier, called HawkesEye which makes predictions based on a temporal window, in contrast to existing approaches which require a graph-like relationship between tweet entities, or the presence of the entire retweeting timeline of a retweeter. HawkesEye utilizes both temporal and textual information using a class-specific topic model and Hawkes processes. Experiments on our curated dataset show significant improvement over four state-of-the-art methods, with precision and recall scores of 0.964 and 0.960 on a balanced dataset, respectively _ HawkesEye beats the best baseline by 6.16% and 25.98% relative improvement in terms of precision and recall, respectively. We also diagnose our model to understand the advantages and pitfalls of the underlying mechanism. We believe that the extent of this study is not restricted to Twitter, but generalizable to other social media systems such as Facebook and Instagram with similar reposting capabilities.

中文翻译：

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HawkesEye：使用 Hawkes 过程和主题建模检测假转发者


转发对于提高推文的受欢迎程度至关重要，大量虚假转发者可以在这方面做出巨大贡献。我们将虚假转发者定义为转发垃圾推文、在短时间内转发异常大量推文或滥用热门标签来宣传与讨论主题无关的事件的 Twitter 帐户。我们引入了一个最新的、时间上多样化的、面向趋势的标记数据集来解决假转发者检测的问题。我们开发了一种新颖的分类器，称为 HawkesEye，它基于时间窗口进行预测，这与需要推文实体之间的类似图形关系或转发者的整个转发时间线的存在的现有方法形成鲜明对比。 HawkesEye 使用特定于类的主题模型和 Hawkes 流程来利用时间和文本信息。在我们策划的数据集上进行的实验表明，与四种最先进的方法相比，有了显着的改进，在平衡数据集上的精确度和召回率分数分别为 0.964 和 0.960 _ HawkesEye 比最佳基线高出了 6.16% 和 25.98% 的相对改进分别是精确率和召回率。我们还诊断我们的模型，以了解底层机制的优点和缺陷。我们认为，这项研究的范围不仅限于 Twitter，还可以推广到其他社交媒体系统，例如具有类似转发功能的 Facebook 和 Instagram。