Is Dynamic Rumor Detection on social media Viable? An Unsupervised Perspective

TL;DR

This paper introduces an unsupervised, lightweight framework for real-time rumor detection on social media, leveraging content and social features with clustering techniques to outperform supervised methods.

Contribution

It presents a novel unsupervised approach for dynamic rumor detection that does not require large labeled datasets, making it suitable for real-time online scenarios.

Findings

Outperforms several existing baseline methods

Better than many supervised rumor detection techniques

Lightweight and robust architecture suitable for online use

Abstract

With the growing popularity and ease of access to the internet, the problem of online rumors is escalating. People are relying on social media to gain information readily but fall prey to false information. There is a lack of credibility assessment techniques for online posts to identify rumors as soon as they arrive. Existing studies have formulated several mechanisms to combat online rumors by developing machine learning and deep learning algorithms. The literature so far provides supervised frameworks for rumor classification that rely on huge training datasets. However, in the online scenario where supervised learning is exigent, dynamic rumor identification becomes difficult. Early detection of online rumors is a challenging task, and studies relating to them are relatively few. It is the need of the hour to identify rumors as soon as they appear online. This work proposes a novel framework for unsupervised rumor detection that relies on an online post's content and social features using state-of-the-art clustering techniques. The proposed architecture outperforms several existing baselines and performs better than several supervised techniques. The proposed method, being lightweight, simple, and robust, offers the suitability of being adopted as a tool for online rumor identification.