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Rumor conversations detection in twitter through extraction of structural features

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Abstract

Twitter is one of the most popular and renowned online social networks spreading information which although dependable could lead to spreading improbable and misleading rumors causing irreversible damage to individuals and society. In the present paper, a novel approach for detecting rumor-based conversations of various world events such as real-world emergencies and breaking news on Twitter is investigated. In this study, three aspects of information dissemination including linguistic style used to express rumors, characteristics of people involved in propagating information and structural features are studied. Structural features include features of reply tree and user graph. Structural features were extracted as new features in order to enhance the efficiency of the rumor conversations detection. These features provide valuable clues on how a source tweet is transmitted and responds over time. Experimental results indicate that the new features are effective in detecting rumors and that the proposed method is better than other methods as F1-score increased by 4%. Implementation of the proposed method was carried out on Twitter datasets collected during five breaking news stories.

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Fig. 1

source tweet and reply tweets of a rumor conversation of the Ottawa Shooting

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Serveh Lotfi & Mitra Mirzarezaee

  2. Iran University of Medical Sciences, Tehran, Iran

    Mehdi Hosseinzadeh

  3. Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Vahid Seydi

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  1. Serveh Lotfi
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  2. Mitra Mirzarezaee
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  3. Mehdi Hosseinzadeh
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  4. Vahid Seydi
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Correspondence to Mitra Mirzarezaee.

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Lotfi, S., Mirzarezaee, M., Hosseinzadeh, M. et al. Rumor conversations detection in twitter through extraction of structural features. Inf Technol Manag 22, 265–279 (2021). https://doi.org/10.1007/s10799-021-00335-7

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