论文标题
在社交媒体上检测转发动态的谣言
Detecting False Rumors from Retweet Dynamics on Social Media
论文作者
论文摘要
众所周知，虚假的谣言对社会有害影响。为了防止谣言传播，Twitter等社交媒体平台必须尽早发现它们。在这项工作中，我们开发了一种新颖的概率混合模型，该模型根据基础扩散过程对真实谣言进行了分类。具体而言，我们的模型是第一个正式化真实与虚假转发过程的自我兴奋性质的模型。这导致了标记霍克斯模型（MMHM）的新型混合物。因此，我们的模型消除了功能工程的需求；取而代之的是，它直接建模传播过程，以推断在线谣言是否不正确。我们的评估是基于13,650个True的转发级联。与Twitter的虚假谣言。我们的模型以64.97％的平衡准确性认识到虚假谣言，而AUC为69.46％。它的表现优于最先进的基线（神经工程和功能工程），但虽然是完全可解释的。我们的工作对从业者有直接影响：它利用扩散过程作为隐性质量信号，并基于它检测到错误的内容。
False rumors are known to have detrimental effects on society. To prevent the spread of false rumors, social media platforms such as Twitter must detect them early. In this work, we develop a novel probabilistic mixture model that classifies true vs. false rumors based on the underlying spreading process. Specifically, our model is the first to formalize the self-exciting nature of true vs. false retweeting processes. This results in a novel mixture marked Hawkes model (MMHM). Owing to this, our model obviates the need for feature engineering; instead, it directly models the spreading process in order to make inferences of whether online rumors are incorrect. Our evaluation is based on 13,650 retweet cascades of both true. vs. false rumors from Twitter. Our model recognizes false rumors with a balanced accuracy of 64.97% and an AUC of 69.46%. It outperforms state-of-the-art baselines (both neural and feature engineering) by a considerable margin but while being fully interpretable. Our work has direct implications for practitioners: it leverages the spreading process as an implicit quality signal and, based on it, detects false content.
