A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms

Abstract

In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unmoderated nature of social networks and the advent of advanced machine learning techniques, including generative models such as GPT and diffusion models, have facilitated the propagation of rumors, posing challenges to society. Detecting and countering these rumors to mitigate their adverse effects on individuals and society is imperative. Automatic rumor detection, typically framed as a binary classification problem, predominantly relies on supervised machine learning models, necessitating substantial labeled data; yet, the scarcity of labeled datasets due to the high cost of fact-checking and annotation hinders the application of machine learning for rumor detection. In this study, we address this challenge through active learning. We assess various query strategies across different machine learning models and datasets in order to offer a comparative analysis. Our findings reveal that active learning reduces labeling time and costs while achieving comparable rumor detection performance. Furthermore, we advocate for the use of machine learning models with nonlinear classification boundaries on complex environmental datasets for more effective rumor detection.