ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (9): 1939-1952.doi: 10.7544/issn1000-1239.2019.20180624

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Evaluation of Content Credibility in Social Media

Liu Bo1,3, Li Yang2,3, Meng Qing1, Tang Xiaohu1, Cao Jiuxin2   

  1. 1(School of Computer Science and Engineering, Southeast University, Nanjing 211189); 2(School of Cyber Science and Engineering, Southeast University, Nanjing 211189); 3(Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 211189)
  • Online:2019-09-10
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2017YFB1003000), the National Natural Science Foundation of China (61370208, 61472081, 61320106007, 61272531), the National High Technology Research and Development Program of China (863 Program) (2013AA013503), the Jiangsu Provincial Key Laboratory of Network and Information Security Foundation (BM2003201), and the Jiangsu Provincial Key Laboratory of Computer Network Technology Foundation (BE2018706).

Abstract: With the rapid development of social media in recent years, the access to information has been broadened, but the spreading of incredible information has been facilitated at the same time, which brings a series of negative impacts to cyber security. Compared with the traditional online media, the information in social media is more open and complicated, giving rise to great challenges to judge online information credibility for individuals. How to filter the incredible information becomes an urgent problem. In the existing research on the assessment of information credibility in social media, lots of effort has been involved in extracting the useful factors for credibility assessment, but the processing of noisy data is neglected, and a large number of useless tweets can be included in the evaluation process, resulting in the deviation of the information credibility assessment. So it is particularly important to select the significant tweets for information credibility assessment. This paper takes the topic factor and conformity of users into consideration to relieve the impact of noisy data, such as conformity retweeting, on information credibility assessment, and uses Bayesian network to establish an evaluation model for information credibility in social media. Then we verify the effectiveness of our model using a real dataset.

Key words: social media, content credibility, topic factor, conformity, probabilistic graphical model

CLC Number: