TDT_CC: A Hot Topic Detection and Tracking Algorithm Based on Chain of Causes
With the development and application of Web3.0, it has become a common social phenomenon that users discuss hot topics on social networks, making them to aggregate into user groups based on the topics, rapidly. The hot topic detection and tracking is helpful for social public opinion supervision and guidance, in addition, it contribute to the user’s behavior mining and analysis. However, users’ interest in some topics often changes as new event occurs, causing the center of hot topics to change over time. For tracking the heat of topic in real-time, we proposed an effective algorithm to detect and track hot topic based on chain of causes (TDT_CC). Firstly, we treat the events as attributes of topic and add them to the structure of the social networks. Secondly, the subgraphs that induced by specific attributes are mined based on the correlation of event-heat-changing attributes and attribute-extended social network structure.
KeywordsHot topic Social networks Chain of causes Attributes correlation Subgraph mining
This work was supported by the National Natural Science Foundation of China (No. 61701104), and by the Science Research of Education Department of Jilin Province (No. JJKH20180422KJ).
- 1.Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)Google Scholar
- 2.Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, pp. 591–600 (2010)Google Scholar
- 3.Raza, U.M., Saqib, M.N., Javed, F., Ikram, M.U.L., Basit, S., Philippe, F.V.: Early detection of controversial Urdu speeches from social media. Data Sci. Pattern Recognit. 1(2), 26–42 (2017)Google Scholar
- 4.Daniel, M.L., Cristian, G.G., Vicente, G.D., Cristina, P.G.B., Nestor, G.F.: SenseQ: replying questions of social networks users by using a wireless sensor network based on sensor relationships. Data Sci. Pattern Recognit. 1(1), 1–12 (2017)Google Scholar
- 7.Rapaport, J.A., Rapaport, S., Smith, K.A., Beattie, J., Gimlan, G.: U.S. Patent No. 8,539,359. U.S. Patent and Trademark Office, Washington, DC (2013)Google Scholar
- 8.Wang, L., Hu, G., Zhou, T.: Semantic analysis of learners’ emotional tendencies on online MOOC education. Sustainability 10(6), 1–19 (2018)Google Scholar
- 11.Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187 (2009)Google Scholar
- 14.Kim, H.G., Lee, S., Kyeong, S.: Discovering hot topics using Twitter streaming data social topic detection and geographic clustering. In: Advances in Social Networks Analysis and Mining (ASONAM), pp. 1215–1220 (2013)Google Scholar
- 15.Zhe, G., Dong, L., Qi, L., Jianyi, Z., Yang, X., Xinxin, N.: An online hot topics detection approach using the improved ant colony text clustering algorithm. JCIT 2, 243–252 (2012)Google Scholar
- 16.Chen, Y., Amiri, H., Li, Z., Chua, T.S.: Emerging topic detection for organizations from microblogs. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52 (2013)Google Scholar