Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach

TL;DR

This paper presents a weak supervised learning method utilizing Graph Convolutional Networks to identify potential rumor spreaders on Twitter, achieving high accuracy with limited labeled data.

Contribution

It introduces a novel weak supervised approach using GCNs to detect rumor spreaders from Twitter data without requiring extensive labeled datasets.

Findings

GCN outperforms SVM, RF, and LSTM in accuracy

Achieves up to 0.864 F1-Score and 0.720 AUC-ROC

Effective identification of rumor spreaders with limited labels

Abstract

Online Social Media (OSM) platforms such as Twitter, Facebook are extensively exploited by the users of these platforms for spreading the (mis)information to a large audience effortlessly at a rapid pace. It has been observed that the misinformation can cause panic, fear, and financial loss to society. Thus, it is important to detect and control the misinformation in such platforms before it spreads to the masses. In this work, we focus on rumors, which is one type of misinformation (other types are fake news, hoaxes, etc). One way to control the spread of the rumors is by identifying users who are possibly the rumor spreaders, that is, users who are often involved in spreading the rumors. Due to the lack of availability of rumor spreaders labeled dataset (which is an expensive task), we use publicly available PHEME dataset, which contains rumor and non-rumor tweets information, and then apply a weak supervised learning approach to transform the PHEME dataset into rumor spreaders dataset. We utilize three types of features, that is, user, text, and ego-network features, before applying various supervised learning approaches. In particular, to exploit the inherent network property in this dataset (user-user reply graph), we explore Graph Convolutional Network (GCN), a type of Graph Neural Network (GNN) technique. We compare GCN results with the other approaches: SVM, RF, and LSTM. Extensive experiments performed on the rumor spreaders dataset, where we achieve up to 0.864 value for F1-Score and 0.720 value for AUC-ROC, shows the effectiveness of our methodology for identifying possible rumor spreaders using the GCN technique.