Ensemble Rumor Text Classification Model Applied to Different Tweet Features

Amit Kumar Sharma; Rakshith Alaham Gangeya; Harshit Kumar; Sandeep Chaurasia; Devesh Kumar Srivastava

doi:10.5391/IJFIS.2022.22.3.325

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자료유형: 학술저널

저자정보: Amit Kumar Sharma (Manipal University) Rakshith Alaham Gangeya (Manipal University) Harshit Kumar (Manipal University) Sandeep Chaurasia (Manipal University) Devesh Kumar Srivastava (Manipal University)

저널정보: 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.22 No.3

발행연도: 2022.9

수록면: 325 - 338 (14page)

DOI: 10.5391/IJFIS.2022.22.3.325

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Today, social media has evolved into user-friendly and useful platforms to spread messages and receive information about different activities. Thus, social media users’ daily approaches to billions of messages, and checking the credibility of such information is a challenging task. False information or rumors are spread to misguide people. Previous approaches utilized the help of users or third parties to flag suspect information, but this was highly inefficient and redundant. Moreover, previous studies have focused on rumor classification using state-of-the-art and deep learning methods with different tweet features. This analysis focused on combining three different feature models of tweets and suggested an ensemble model for classifying tweets as rumor and non-rumor. In this study, the experimental results of four different rumor and non-rumor classification models, which were based on a neural network model, were compared. The strength of this research is that the experiments were performed on different tweet features, such as word vectors, user metadata features, reaction features, and ensemble model probabilistic features, and the results were classified using layered neural network architectures. The correlations of the different features determine the importance and selection of useful features for experimental purposes. These findings suggest that the ensemble model performed well, and provided better validation accuracy and better results for unseen data.

#Rumor #Word embedding #User metadata features #Reaction features #Ensemble probabilistic features #Feature scaling #Correlation #Bi-LSTM #Neural network

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