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Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets
Information Systems Frontiers ( IF 5.9 ) Pub Date : 2020-08-12 , DOI: 10.1007/s10796-020-10040-5
Jyoti Prakash Singh , Abhinav Kumar , Nripendra P. Rana , Yogesh K. Dwivedi

Twitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.



中文翻译:

基于注意力的LSTM网络,用于推文的谣言准确性评估

Twitter已成为谣言的沃土,因为信息可以立即传播给大量人。谣言可能会误导公众舆论,削弱社会秩序,降低政府的合法性并导致对社会稳定的重大威胁。因此,迫切需要及时发现和揭穿谣言。在这项工作中,我们提出了一个基于注意力的长期记忆(LSTM)网络,该网络使用具有13种不同语言和用户功能的推文文本来区分谣言和非谣言推文。所提出的基于注意力的LSTM模型的性能与几种常规的机器模型和深度学习模型进行了比较。提议的基于注意力的LSTM模型达到F 1-将谣言和非谣言推文分类的得分为0.88,这比最新结果更好。所提出的系统可以减少谣言对社会的影响,并减少生命,金钱的损失,并通过社交媒体平台建立用户的牢固信任。

更新日期:2020-08-14
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