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Precise refutation of social media rumors through users’ perspective: Crowd classification based on Believability
Journal article   Peer reviewed

Precise refutation of social media rumors through users’ perspective: Crowd classification based on Believability

Yujie Zhou, Zongmin Li, Yan Tu and Benjamin Lev
Expert systems with applications, v 268, 126107
05 Apr 2025
DOI: https://doi.org/10.1016/j.eswa.2024.126107

Abstract

Believability Crowd classification Extreme Gradient Boosting (XGBoost) model Natural language processing (NLP) Rumor refutation Target Group Index (TGI)
Relying solely on sentiment polarity to express users’ true attitudes towards rumor-refuting information is imprecise, and methods that ignore specific user groups and their influence on refutation will result in ineffective spread of refutation information. In this paper, we investigate three questions: (1) How to measure the users’ attitudes of rumor-refuting information? (2) How can users be more efficiently classified based on their attitudes and behaviors? (3) What are the group features of different crowds? Accordingly, we introduce an innovative classification scheme based on users’ attitudes and interactive behaviors. Subsequently, Natural language processing (NLP) techniques are applied to quantify the sentiment tendency and recent interests of users. Believability as a metric for quantifying users’ attitudes is measured by the proposed RoBERTa-BiLSTM model. Then, five machine learning classification models are established, among which Extreme Gradient Boosting (XGBoost) model has the best performance. Lastly, based on XGBoost and Target Group Index (TGI), four groups are comprehensively evaluated and user profiles are constructed for distinct groups: rumor refuters, rumor spreaders, anti-rumor inactivists and pro-rumor inactivists. The results reveal that the distribution of Believability shows significant polarization among users and there are significant feature differences between various groups. This work makes a notable contribution to refutation by advancing beyond traditional sentiment analysis to precise measurement of users’ attitudes toward rumor-refuting content. By introducing the novel classification scheme, this paper is the first of its kind for applying Believability of rumor refutation and offering a more nuanced understanding of users’ attitudes as a differentiator for crowd classification. It advances the understanding and development of crowd classification and holds the potential to significantly impact the realm of precise rumor governance.

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