TL;DR
This paper introduces MTLTS, an end-to-end multi-task framework that jointly assesses credibility and generates trustworthy summaries of crisis-related microblogs, improving accuracy and generalization across domains.
Contribution
It presents the first integrated solution combining credibility verification and summarization for crisis microblogs, utilizing hierarchical multi-task learning and structural conversation analysis.
Findings
Outperforms existing baselines in verification accuracy.
Achieves 21-35% higher verified ratio of summary tweets.
Attains 16-20% improvements in ROUGE1-F1 scores.
Abstract
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, the first end-to-end solution for the task that jointly determines the credibility and summary-worthiness of tweets. Our credibility verifier is designed to recursively learn the structural properties of a Twitter conversation cascade, along with the stances of replies towards the source tweet. We then take a hierarchical multi-task learning approach, where the verifier is trained at a lower layer, and the summarizer is trained at a deeper layer where it utilizes the verifier predictions to determine the salience of a tweet. Different from existing…
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