Determining the Veracity of Rumours on Twitter
Georgios Giasemidis, Colin Singleton, Ioannis Agrafiotis, Jason R.C., Nurse, Alan Pilgrim, Chris Willis, Danica Vukadinovic Greetham

TL;DR
This paper presents a machine learning approach to automatically assess the trustworthiness of rumours on Twitter by analyzing a large dataset of tweets and various trust indicators, improving accuracy over previous methods.
Contribution
The study introduces a comprehensive trustworthiness model using multiple features and demonstrates its effectiveness in real-time rumour verification on Twitter.
Findings
Model outperforms similar studies in accuracy
Identifies key attributes influencing trustworthiness scores
Provides a visual tool for rumour analysis
Abstract
While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users' past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors' profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the…
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