Detecting False Rumors from Retweet Dynamics on Social Media

TL;DR

This paper introduces a probabilistic mixture model called MMHM that detects false rumors on social media by analyzing retweet dynamics, outperforming existing methods while being fully interpretable.

Contribution

The paper develops the first mixture marked Hawkes model that formalizes the self-exciting nature of true and false retweeting processes for rumor detection.

Findings

Achieves 64.97% balanced accuracy in false rumor detection

Outperforms state-of-the-art neural and feature-based baselines

Uses retweet spreading process as an implicit quality signal

Abstract

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.