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Misinformation-oriented expert finding in social networks

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Abstract

Due to the distributed and decentralized nature of social media, respective content that contains misinformation is usually propagated without any type of moderation, which may mislead the public and have a profound real-world impact. In addition, it is quite challenging to distinguish misinformation with high precision, since the content is often short and lacks of semantics. A promising solution is to utilize the crowdsourcing wisdom that pushes the suspected misinformation to relevant users based on the expertise and collects the assessments to judge the credibility. Even though a lot of expert finding models have been employed, however, these methods cannot effectively deal with the misinformation-oriented expert matching tasks since the data collected from social network is different form traditional text collection. To this end, we focus on how to obtain an appropriate matching between the suspect misinformation and corresponding experts, and propose a multi-topic expert finding method, called LTM (List based Topic Model), to sufficiently utilize crowdsourcing wisdom. Moreover, we optimize the query results with the help of supervised information that extracted from Twitter Lists. Finally, we demonstrate the effectiveness of our work with experiments on real-world data and verify the superiority of our proposed model in accuracy.

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Acknowledgements

This research is partially supported by the Fundamental Research Funds for the Central Universities (HUST: Grants No. 2019kfyXKJC021, 2019kfyXJJS091), and NSFC (Grants No. 61572215).

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Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Guohui Li, Ming Dong, Fuming Yang & Bolong Zheng

  2. Wuhan Fiberhome Technical Services Co.,Ltd, Wuhan, China

    Jun Zeng, Jiansen Yuan & Congyuan Jin

  3. Griffith University, Gold Coast, Australia

    Nguyen Quoc Viet Hung & Phan Thanh Cong

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  1. Guohui Li
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  2. Ming Dong
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  3. Fuming Yang
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  4. Jun Zeng
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  9. Bolong Zheng
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Correspondence to Bolong Zheng.

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This article belongs to the Topical Collection: Special Issue on Graph Data Management in Online Social Networks

Guest Editors: Kai Zheng, Guanfeng Liu, Mehmet A. Orgun, and Junping Du

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Li, G., Dong, M., Yang, F. et al. Misinformation-oriented expert finding in social networks. World Wide Web 23, 693–714 (2020). https://doi.org/10.1007/s11280-019-00717-6

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