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A Method for User Avatar Authenticity Based on Multi-feature Fusion

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Information Retrieval (CCIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11772))

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Abstract

Social media provides users with a platform for information sharing and communication. At the same time, there are a large proportion of users use a fake avatar. We attempt to automatically discriminate the authenticity of the user’s uploaded person avatar based on the machine learning method. In this paper, an avatar authenticity discrimination method based on multi-feature fusion is proposed by combining user-based features, avatar features, and text-based features. We use deep learning, image recognition and topic model techniques to process features. The method is verified on the Sina Weibo data set. The experimental results show that the method can achieve 84.1% accuracy.

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References

  1. Ding, C., He, X., Husbands, P., Zha, H., Simon, H.: Pagerank, hits and a unified framework for link analysis. In: Proceedings of the 2003 SIAM International Conference on Data Mining, pp. 249–253. SIAM (2003)

    Google Scholar 

  2. Laleh, N., Carminati, B., Ferrari, E.: Risk assessment in social networks based on user anomalous behaviors. IEEE Trans. Dependable Secure Comput. 15(2), 295–308 (2016)

    Article  Google Scholar 

  3. Kumar, D., Shaalan, Y., Zhang, X., Chan, J.: Identifying singleton spammers via spammer group detection. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10937, pp. 656–667. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93034-3_52

    Chapter  Google Scholar 

  4. Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., Zadeh, R.: WTF: the who to follow service at Twitter. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 505–514. ACM (2013)

    Google Scholar 

  5. Liang, H., Lu, G., Xu, N.: Analyzing user influence of microblog. In: 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), pp. 15–22. IEEE (2012)

    Google Scholar 

  6. Ott, M., Cardie, C., Hancock, J.T.: Negative deceptive opinion spam. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 497–501 (2013)

    Google Scholar 

  7. Shojaee, S., Murad, M.A.A., Azman, A.B., Sharef, N.M., Nadali, S.: Detecting deceptive reviews using lexical and syntactic features. In: 2013 13th International Conference on Intelligent Systems Design and Applications, pp. 53–58. IEEE (2013)

    Google Scholar 

  8. Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.: What yelp fake review filter might be doing? In: Seventh International AAAI Conference on Weblogs and Social Media (2013)

    Google Scholar 

  9. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  10. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  11. Kubat, M., Matwin, S., et al.: Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, vol. 97, pp. 179–186, Nashville, USA (1997)

    Google Scholar 

  12. Mani, I., Zhang, I.: kNN approach to unbalanced data distributions: a case study involving information extraction. In: Proceedings of Workshop on Learning from Imbalanced Datasets, vol. 126 (2003)

    Google Scholar 

  13. Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsl. 6(1), 20–29 (2004)

    Article  Google Scholar 

  14. Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 27–34. AUAI Press (2009)

    Google Scholar 

  15. Phan, X.-H., Nguyen, L.-M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th International Conference on World Wide Web, pp. 91–100. ACM (2008)

    Google Scholar 

  16. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. Shandong Computer Science Center (National Supercomputer Center in Jinan), Shandong Provincal Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Science), Jinan, 250019, Shandong, China

    Weinan Zhang & Lianhai Wang

  2. School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Science), Jinan, 250014, Shandong, China

    Yongli Zan

Authors
  1. Weinan Zhang
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  2. Lianhai Wang
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  3. Yongli Zan
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Corresponding author

Correspondence to Lianhai Wang .

Editor information

Editors and Affiliations

  1. Fudan University, Shanghai, China

    Qi Zhang

  2. Fuzhou University, Fuzhou, China

    Xiangwen Liao

  3. Shandong University, Qingdao, China

    Zhaochun Ren

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Zhang, W., Wang, L., Zan, Y. (2019). A Method for User Avatar Authenticity Based on Multi-feature Fusion. In: Zhang, Q., Liao, X., Ren, Z. (eds) Information Retrieval. CCIR 2019. Lecture Notes in Computer Science(), vol 11772. Springer, Cham. https://doi.org/10.1007/978-3-030-31624-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-31624-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31623-5

  • Online ISBN: 978-3-030-31624-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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