Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media

TL;DR

This paper introduces a novel sequential classification approach using Conditional Random Fields to detect rumours in social media during breaking news, leveraging event-specific context for improved accuracy.

Contribution

It presents a new rumour detection method that learns from the reporting dynamics of breaking news without needing query-based tweet analysis, outperforming existing systems.

Findings

Achieved nearly 40% improvement in F1 score over baseline methods.

Demonstrated the classifier's robustness across diverse breaking news events.

Outperformed state-of-the-art rumour detection systems in precision and recall.

Abstract

Breaking news leads to situations of fast-paced reporting in social media, producing all kinds of updates related to news stories, albeit with the caveat that some of those early updates tend to be rumours, i.e., information with an unverified status at the time of posting. Flagging information that is unverified can be helpful to avoid the spread of information that may turn out to be false. Detection of rumours can also feed a rumour tracking system that ultimately determines their veracity. In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories. Using Twitter datasets collected during five breaking news stories, we experiment with Conditional Random Fields as a sequential classifier that leverages context learnt during an event for rumour detection, which we compare with the state-of-the-art rumour detection system as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying a piece of information to deem it a rumour, but instead we detect rumours from the tweet alone by exploiting context learnt during the event. Our classifier achieves competitive performance, beating the state-of-the-art classifier that relies on querying tweets with improved precision and recall, as well as outperforming our best baseline with nearly 40% improvement in terms of F1 score. The scale and diversity of our experiments reinforces the generalisability of our classifier.