Ning Wang, Haoran Lyu^*, Yuchen Fu

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.068784 - 10 November 2025

Abstract With the widespread use of social media, the propagation of health-related rumors has become a significant public health threat. Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures, with only a few recent approaches attempting causal inference; however, these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors. In this study, we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts, holding significant potential for health rumor detection. To this end, we… More >

Suqiao Li¹, Taotao Cai², Lingling Li^3,*, Xuezhuan Zhao^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3583-3598, 2025, DOI:10.32604/cmc.2025.066909 - 23 September 2025

Abstract Rumor Control (RC), aimed at minimizing the spread of rumors in social networks, is of paramount importance, as the spread of rumors can lead to significant economic losses, societal disruptions, and even widespread panic. The RC problem has garnered extensive research attention, however, most existing solutions for rumor control face a trade-off between efficiency and effectiveness, which limits their practical application in real-world scenarios. In this light, this paper studies the Truth-spreading-based Rumor Control (TRC) problem, and introduces the Subgraph-based Greedy algorithm Optimized with CELF (SGOC), which employs subgraph techniques and the CELF strategy, as More >

Nanjiang Zhong^*, Xinchen Jiang, Yuan Yao

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4741-4757, 2025, DOI:10.32604/cmc.2025.059536 - 06 March 2025

Abstract The proliferation of rumors on social media has caused serious harm to society. Although previous research has attempted to use deep learning methods for rumor detection, they did not simultaneously consider the two key features of temporal and spatial domains. More importantly, these methods struggle to automatically generate convincing explanations for the detection results, which is crucial for preventing the further spread of rumors. To address these limitations, this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models (LLMs) to automatically generate explanations for the detection results.… More >

N. Kanagavalli^1,*, S. Baghavathi Priya²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 191-205, 2022, DOI:10.32604/iasc.2022.022720 - 05 January 2022

Abstract Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors… More >

Sultan Noman Qasem^1,2, Mohammed Al-Sarem^3,4, Faisal Saeed^3,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1721-1747, 2022, DOI:10.32604/cmc.2022.018972 - 07 September 2021

Abstract Rumors regarding epidemic diseases such as COVID 19, medicines and treatments, diagnostic methods and public emergencies can have harmful impacts on health and political, social and other aspects of people’s lives, especially during emergency situations and health crises. With huge amounts of content being posted to social media every second during these situations, it becomes very difficult to detect fake news (rumors) that poses threats to the stability and sustainability of the healthcare sector. A rumor is defined as a statement for which truthfulness has not been verified. During COVID 19, people found difficulty in… More >

Yonghun Jang, Chang-Hyeon Park, Dong-Gun Lee, Yeong-Seok Seo^*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3563-3579, 2021, DOI:10.32604/cmc.2021.018901 - 24 August 2021

Abstract Following the development of communication techniques and smart devices, the era of Artificial Intelligence (AI) and big data has arrived. The increased connectivity, referred to as hyper-connectivity, has led to the development of smart cities. People in these smart cities can access numerous online contents and are always connected. These developments, however, also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels. Information cannot often be verified, which can confuse the users. The increasing influence of social media has… More >

Lei Guo^*

Journal of New Media, Vol.3, No.3, pp. 99-107, 2021, DOI:10.32604/jnm.2021.019649 - 13 July 2021

Abstract In recent years, with the increasing popularity of social networks, rumors have become more common. At present, the solution to rumors in social networks is mainly through media censorship and manual reporting, but this method requires a lot of manpower and material resources, and the cost is relatively high. Therefore, research on the characteristics of rumors and automatic identification and classification of network message text is of great significance. This paper uses the Naive Bayes algorithm combined with Laplacian smoothing to identify rumors in social network texts. The first is to segment the text and More >