Abstract The research of text similarity, especially for rumor texts, which constructed the calculation model by known rumors and calculated its similarity. From which, people can recognize the rumor in advance, and improve their vigilance to effectively block and control rumors dissemination. Based on the Bayesian network, the similarity calculation model of microblog rumor texts was built. At the same time, taking into account not only the rumor texts have similar characters, but also the rumor producers have similar characters, and therefore the similarity calculation model of rumor texts makers was constructed. Then, the similarity between the text and the user was integrated, and the microblog similarity calculation model was established. Finally, also experimentally studied the performance of the proposed model on the microblog rumor text and the user data set. The experimental results indicated that the similarity algorithm proposed in this paper could be used to identify the rumors of texts and predict the characters of users more accurately and effectively.

References

[1] Cheng A. and Xia C., “On the Linguistic Features of Microblog Rumor,” Southeast Communication, no. 11, pp. 98-100, 2014.

[2] Chengcheng L., An Z., Qingwen Q., and Huimin S., “Grid-based Location Microblog Data Fetching and Human Information Extraction,” Science of Surveying and Mapping, vol. 42, no. 2, pp. 125-129, 2017.

[3] Chun Z., Xiao X., Zhu Z., “A Personalized Search Algorithm by Using Content-Based Filtering,” Journal of Software, vol. 14, no. 5, pp. 999-1004, 2003.

[4] Dong C., Qiang D., Yang G., and Da L., “Research on the Evolution of Micro-blog User Information Personalized Recommendation Model Based on LDA,” Information Science, no. 8, pp. 3-10, 2017.

[5] Ding Y., Liu F., and Tang B., “Context-Sensitive Trust Computing in Distributed Environments,” Knowledge-Based Systems, no. 28, pp. 105-114, 2012.

[6] Fan B., Wang J., Ge Y., and Ma M., “Research on the Spatial Structure of Chengdu-Chongqing City Group Based on Weibo Sign-in Data and Its Inter-city Population Flow,” Journal of Earth Information Science, vol. 21, no. 1, pp. 68-76, 2019.

[7] Guo Q., Li Y., and Tang Y., “Research on Text Similarity Calculation Based on VSM,” Application Research of Computers, no. 11, pp. 3256-3258, 2008.

[8] Han Y. and Lu J., “A Summary of Weibo Proverbs Communication Model,” Network Security Technology and Applications, no. 11, pp. 41-43, 2018.

[9] Hu M., Cai S., and Zhang Y., “Research on Personalized Recommendation-oriented Contextual User Profile,” Journal of Intelligence, vol. 29, no. 10, pp. 157-162, 2010.

[10] Huang C., Yin H., and Hou Y., “A Text Similarity Measurement Method Combining Term Semantic Information and TF-IDF Method,” Chinese Journal of Computers, vol. 34, no. 5, pp. 856-864, 2011.

[11] Hassan M. and Shoaib M., “Opinion within Opinion: Segmentation Approach for urdu Sentiment Analysis,” The International Arab Journal of Information Technology, vol. 15, no. 1, pp. 21-28, 2018.

[12] Lawrence R., Almasi G., Kotlyar V., Viveros M., and Duri S., “Personalization of Supermarket Product Recommendations,” Data Mining and Knowledge Discovery, vol. 5, pp. 11-32, 2001.

[13] Li F., Hou J., Zeng R., and Li C., “Research on Multi-Feature Sentence Similarity Computing Method with Word Embedding,” Journal of Frontiers of Computer Science and Technology, vol. 11, no. 4, pp. 608-618, 2017.

[14] Li L., Zhu A., and Su T., “Research and Implementation of an Improved Vector Space Text Similarity Algorithm,” Computer Applications and Software, vol. 29, no. 5, pp. 282-284, 2012.

[15] Li Q. and Gu J., “Activity Driven Modelling of Online Social Network,” Journal of Systems Engineering, vol. 30, no. 1, pp. 9-15, 2015.

[16] Li X., Cao H., Ding C., and Huang L., “Short text Classification Based on HowNet and Domain Keyword Set Extension,” New Technology of Library and Information Service, no. 2, pp. 31- 38, 2015.

[17] Li X., Liu K., Ding C., and Liao X., “Title Information Classification Based on Hownet Semantics Feature Extension,” Library Journal, no. 2, pp. 11-19, 2017.

[18] Liao H., Wang Y., and Guang P., “Topic Mining and Viewpoint Recognition of Different Communicators in Weibo Public Opinion Communication Cycle,” Library and Information Work, vol. 62, no. 19, pp. 77-85, 2018.

[19] Ling H., “Research Overview on Bayesian Network,” Natural Sciences Edition, vol. 23, no. 1, pp. 33-40, 2013.

[20] Liu F., Li X., Ding Y., Zhao H., Liu X., and Ma Y., Tang B., “A Social Network-Based Trust- Aware Propagation Model for P2P Systems,” Text Similarity Computation Model for Identifying Rumor Based on Bayesian Network ... 741 Knowledge-Based Systems, no. 41, pp. 8-15, 2013.

[21] Liu F., Wang L., Gao L., Li H., Zhao H., and Sok Khim Men., “A Web Service Trust Evaluation Model Based on Small-World Networks,” Knowledge-Based Systems, no. 57, pp. 161-167, 2014.

[22] Liu F., Wang L., Johnson H., and Zhao H., “Analysis of Network Trust Dynamics Based on Evolutionary Game,” Transaction E: Industrial Engineering, vol. 22, no. 6, pp. 2548-2557, 2015.

[23] Liu F., Zhu X., Hu Y., Ren L., and Johnson H., “A Cloud Theory-Based Trust Computing Model in Social Networks,” Entropy, vol. 19, no. 1, pp. 1-11, 2017.

[24] Liu M., “Post-Truth Era Microblog Rumors Spread and Governance-Taking the “Henan Eye Cancer Girl Incident” as an Example,” New Media Research, vol. 4, no. 22, pp. 21-23, 2018.

[25] Liu Y., Liang X., and Yang X., “Information Spreading Model of Weibo Network Based on Petri Net,” Chinese Management Science, vol. 26, no. 12, pp. 158-167, 2018.

[26] Liu Z., Zhang L., Tu C., and Sun M., “Statistical and Semantic Analysis of Rumors in Chinese Social Media,” Scientia Sinica Informationis, no. 12, pp. 1536-1546, 2015.

[27] Mepherson M., Smith-Lovin L., and Cook J., “Birds of a Feather: Homophily in Social Networks,” Annual Review of Sociology, pp. 415- 444, 2001.

[28] Qin J., Wang W., Xiao C., Lu Y., Lin X., and Wang H., “Asymmetric Signature Schemes for Efficient Exact Edit Similarity Query Processing,” Acm Transactions on Database Systems, vol. 38, no. 3, pp. 1-44, 2013.

[29] Ren Y., Wang Y., and Wang G., “Research on the Evolution Mechanism of Micro-blog Rumors,” Journal of Intelligence, vol. 31, no. 5, pp. 50-54, 2012.

[30] Resnick P., Iacovou N., Suchak M., Bergstrom P., and Riedl J., “Grouplens: An Open Architecture for Collaborative Filtering of Netnews,” in Proceedings of ACM Conference on Computer Supported Cooperative Work, North Carolina, pp. 175-186, 1994.

[31] Saini J., “Psychoanalysis of Online Behavior and Cyber Conduct of Chatters in Chat Rooms and Messenger Environments,” Advanced Networking and Applications, vol. 6, no. 2, pp. 2214-2221, 2014.

[32] Sun Y. and Li S., “Similarity-Based Community Detection in Social Network of Microblog,” Journal of Computer Research and Development, vol. 51, no. 12, pp. 2797-2807, 2014.

[33] Wang H. and Cai P., “Research on Weibo Proverbs Communication Network,” Library and Information Research, vol. 11, no. 1, pp. 37- 42+49, 2018.

[34] Yang Z. and Huang H., “Graph Based Word Sense Disambiguation Method Using Distance Between Words,” Journal of Software, vol. 23, no. 4, pp. 776-785, 2012.

[35] Yao B., Ni J., Yu P., Li L., and Cao B., “Micro Blog User Recommendation Algorithm Based on Similarity of Multi-Source Information,” Journal of Computer Applications, no. 5, pp. 1382-1386, 2017.

[36] Yin D., Hong L., and Davison B., “Structural Link Analysis and Prediction in Microblogs,” in Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, pp. 1163-1168, 2011.

[37] Zhang B., Zhang Y., and Gao K., “Combining Relation and Content Analysis for Social Tagging Recommendation,” Journal of Software, vol. 23, no. 3, pp. 476-488, 2012.

[38] Zhi-yun Z., Chun-yuan J., Zhen-fei W., and Dun L., “Computing Research of User Similarity Based on Micro-blog,” Computer Science, no. 2, pp. 262-266, 2017.

[39] Zhu X. and Liu F., “Research on Behavior Model of Rumor Maker Based on System Dynamics,” Complexity, pp. 1-9, 2017. Chengcheng Li is a graduate student studying in Shandong Normal University. Her research interests include rumor spreading and governing. Fengming Liu is a professor of the school of business at Shandong Normal University. His research interests include trust and social computing, game theory, and network behaviors dynamics. Pu Li is a graduate student studying in Shandong Normal University. Her research interests include rumor spreading an d governing.