A Novel Approach for Early Rumour Detection in Social Media Using ALBERT
Keywords:
ALBERT, Rumour detection, social media, Natural language processing, Deep learningAbstract
Rumours and misinformation can spread rapidly in social media platforms, leading to negative consequences such as panic, public confusion, and harm to reputations. Early detection and intervention are crucial to mitigate the impact of such events. In this paper, we propose a novel approach for early rumour detection in social media using ALBERT, a state-of-the-art transformer-based language model. Our method utilizes the pre-trained language representation of ALBERT to extract informative features from social media posts and detect rumours at an early stage, even before the rumour has gained widespread attention. Specifically, we fine-tune the ALBERT model on a large-scale dataset of social media posts annotated with rumour labels, using a binary classification task. We also experiment with different types of input representations, including plain text, hashtags, and user mentions, to investigate their effect on the performance. Our experiments show that our approach outperforms several baseline models, achieving an F1-score of 0.85 and an accuracy of 0.92 on a test set of social media posts from different platforms. We also conduct a detailed analysis of the learned representations and investigate the most informative features and patterns for early rumour detection. Our work provides a promising direction for early detection of rumours and misinformation in social media, which can help prevent their spread and mitigate their negative impact.
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