Reference: | [1] K. S. Adewole, N. B. Anuar, A. Kamsin, K. D. Varathan, and S. A. Razak, Malicious Accounts: Dark of the Social Networks. Journal of Network and Computer Applications, Vol. 79, 2017. [2] A. Atanasov, G. D. F. Morales, and P. Nakov, Predicting the Role of Political Trolls in Social Media. Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019. [3] A. E. Azab, A. M. Idrees, M. A. Mahmoud, and H. Hefny, Fake Accounts Detection in Twitter Based on Minimum Weighted Feature. International Scholarly and Scientific Research and Innovation, Vol. 10, No. 1, 2016. [4] N. Barbieri, F. Bonchi, E. Galimberti, and F. Gullo, Efficient and Effective Community Search. Data Mining and Knowledge Discovery, Vol. 29, No. 5, 2015. [5] S. Y. Bhat, and M. Abulaish, Community-based Features for Identifying Spammers in Online Social Networks. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013. [6] P. Biyani, K. Tsioutsiouliklis, and J. Blackmer, “8 Amazing Secrets for Getting More Clicks”: Detecting Clickbaits in News Streams Using Article Informality. Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016. [7] Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro, Aiding the Detection of Fake Accounts in Large Scale Social Online Services. Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, 2012. [8] T. Chakraborty, A. Dalmia, A. Mukherjee, and N. Ganguly, Metrics for Community Analysis: A Survey. ACM Computing Survey, Vol. 50, No. 4, 2017. [9] N. Chavoshi, H. Hamooni, and A. Mueen, Identifying Correlated Bots in Twitter. Proceedings of the 8th International Conference on Social Informatics, 2016. [10] A. Clauset, Finding Local Community Structure in Networks. Physical Review E, Vol. 72, Iss. 2, 2005. [11] W. Cui, Y. Xiao, H. Wang, and W. Wang, Local Search of Communities in Large Graphs. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, 2014. [12] Y. Fang, X. Huang, L. Qin, Y. Zhang, W. Zhang, R. Cheng, and X. Lin, A Survey of Community Search over Big Graphs. The VLDB Journal, Vol. 29, 2019. [13] G. W. Flake, S. Lawrence, and C. L. Giles. Efficient, Identification of Web Communities. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000. [14] C. Giatsidis, D. M. Thilikos, and M. Vazirgiannis, D-cores: Measuring Collaboration of Directed Graphs Based on Degeneracy. Proceedings of the IEEE International Conference on Data Mining, 2011. [15] S. Gokalp, M. Temkit, H. Davulcu, and I.H. Toroslu, Partitioning and Scaling Signed Bipartite Graphs for Polarized Political Blogosphere. Proceedings of the 2013 IEEE International Conference on Social Computing, 2013. [16] S. Gupta, P. Kumaraguru, and T. Chakraborty, MalReG: Detecting and Analyzing Malicious Retweeter Groups. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2019. [17] A. Kadian, V. Singh, and A. Bhattacherjee, Detecting Clickbait Using User Emotions and Behaviors on Social Media. Proceedings of the 39th International Conference on Information Systems, 2018. [18] S. Kumar, and N. Shah, False Information on Web and Social Media: A Survey. Social Media Analytics: Advances and Applications, CRC Press, 2018. [19] Y. Li, C. Sha, X. Huang, and Y. Zhang, Community Detection in Attributed Graphs: An Embedding Approach. Proceedings of the 32th AAAI Conference on Artificial Intelligence, 2018. [20] T. Liu, W. Wei, and X. Wan, Learning to Explain Ambiguous Headlines of Online News. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018. [21] F. Luo, J. Z. Wang, and E. Promislow, Exploring Local Community Structures in Large Networks. Web Intelligent and Agent Systems, Vol. 6, No. 4, 2008. [22] U. V. Luxburg, A Tutorial on Spectral Clustering. Statistics and Computing, Vol. 17, No. 4, 2007. [23] T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space. Proceedings of the International Conference on Learning Representations, 2013. [24] A. Minnich, N. Chavoshi, D. Koutra and A. Mueen, BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017. [25] M. E. Newman, Fast Algorithm for Detecting Community Structure in Networks. Physical Review E, Vol. 69, No. 6, 2004. [26] B. Perozzi, R. Al-Rfou, and S. Skiena, DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014. [27] B. Saha, A. Hoch, S. Khuller, L. Raschid, and X.-N. Zhang, Dense Subgraphs with Restrictions and Applications to Gene Annotation Graphs. Proceedings of the 14th Annual International Conference on Research in Computational Molecular Biology, 2010. [28] K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, Vol. 109, No. 1, 2017. [29] M. Sozio, and A. Gionis, The Community-search Problem and How to Plan a Successful Cocktail Party. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010. [30] L. Sun, X. Huang, R.-H. Li, and J. Xu, Fast Algorithms for Intimate-Core Group Search in Weighted Graphs. Proceedings of the International Conference on Web Information Systems Engineering, 2019. [31] N. Vo, K. Lee, C. Cao, T. Tran, and H. Choi, Revealing and Detecting Malicious Retweeter Groups. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017. [32] M.-H. Wang, N.-L. Nguyen, S.-c. Dai, P.-W. Chi, and C.-R. Dow, Understanding Potential Cyber-Armies in Elections: A Study of Taiwan. Sustainability, Vol. 12, No. 6, 2020. [33] M.-H. Wang, N.-L. Nguyen, and C.-R. Dow, Detecting Potential Cyber Armies of Election Campaigns Based on Behavioral Analysis. Proceedings of the International Conference on Complex Networks and their Applications, 2018. [34] Y. Wang, C. Wu1, K. Zheng, and X. Wang, Social Bot Detection Using Tweets Similarity. Security and Privacy in Communication Networks, 2018. [35] Y. Wu, R. Jin, J. Li, and X. Zhang, Robust Local Community Detection: On Free Rider Effect and Its Elimination. PVLDB, Vol. 8, No. 7, 2015. [36] S. Yoon, K. Park, J. Shin, H. Lim, S. Won, M. Cha, and K. Jung, Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019. [37] H. Zha, Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002. [38] W. Zhao, and F. Zhang, Node Embedding With a CN-Based Random Walk for Community Search. IEEE Access, Vol. 7, 2019. [39] D. Zheng, J. Liu, R.-H. Li, C. Aslay, Y.-C. Chen, and X. Huang, Querying Intimate-Core Groups in Weighted Graphs. Proceedings of the IEEE International Conference on Semantic Computing, 2017. [40] 林佳賢, 〈【獨家分析】跟著資料記者追網軍,「假外國人」如何在PTT鼓吹韓流〉.《天下雜誌》671期《假新聞黑洞!輿論戰爭@台灣》, 2019. [41] 陳郁雯, 以深度學習探勘社群網路異常使用者的協作行為. 國立政治大學資訊科學系, 碩士論文, 2020. |