Spotting Rumors via Novelty Detection

TL;DR

This paper proposes a novel approach for early rumor detection using novelty features and pseudo feedback, leveraging news wire data to identify unconfirmed information as potential rumors before they spread widely.

Contribution

It introduces a new set of novelty-based features and pseudo feedback mechanisms tailored for real-time rumor detection, improving early identification accuracy.

Findings

Novelty features outperform traditional methods in early detection.

Pseudo feedback enhances rumor classification accuracy.

Approach detects rumors immediately after publication.

Abstract

Rumour detection is hard because the most accurate systems operate retrospectively, only recognizing rumours once they have collected repeated signals. By then the rumours might have already spread and caused harm. We introduce a new category of features based on novelty, tailored to detect rumours early on. To compensate for the absence of repeated signals, we make use of news wire as an additional data source. Unconfirmed (novel) information with respect to the news articles is considered as an indication of rumours. Additionally we introduce pseudo feedback, which assumes that documents that are similar to previous rumours, are more likely to also be a rumour. Comparison with other real-time approaches shows that novelty based features in conjunction with pseudo feedback perform significantly better, when detecting rumours instantly after their publication.