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Reseach Article
A Data Science Approach to Social Media Analytics for Discovering Topic-Specific Influential patterns from Tweets
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 11 |
| Year of Publication: 2022 |
| Authors: Suneetha Dwarapu, Shashi Mogalla |
10.5120/ijca2022922088
|
Suneetha Dwarapu, Shashi Mogalla . A Data Science Approach to Social Media Analytics for Discovering Topic-Specific Influential patterns from Tweets. International Journal of Computer Applications. 184, 11 ( May 2022), 33-40. DOI=10.5120/ijca2022922088
Abstract
Social media services like Twitter facilitate communication among people beyond geographical boundaries on the topics / events of their current interest which might differ from time to time. Topic detection from Twitter data would be helpful to find trendy topics / products and current events for target marketing in recommender systems. The information associated with each tweet should be analyzed in three different perspectives involving textual content, social context and the temporal aspects to discover high quality clusters representing the topics and accordingly generate topic-specific influential patterns relating the users. This paper proposes a new framework for discovering topic specific influential patterns and maintaining them as snapshots along the time line. It involves topic identification through tweet clustering based on the textual content, followed by their refinement based on social context that involves information inherent in the tweeting behavior of users in addition to the timing of the tweet. Finally the framework proceeds to find the topic-specific influential patterns relating the users interested in a common topic. Dynamically finding patterns of influence enables tracking of information diffusion from influential users to their potential followers.
References
- Cha, Meeyoung, Hamed Haddadi, Fabricio Benevenuto, and Krishna P. Gummadi. ”Measuring user influence in twitter: The million follower fallacy.” In fourth international AAAI conference on weblogs and social media. 2010.
- Hurtado, J.L., Agarwal, A. and Zhu, X., 2016. Topic discovery and future trend forecasting for texts. Journal of Big Data, 3(1), p.7.
- Cai, Hongyun, Zi Huang, Divesh Srivastava, and Qing Zhang. ”Indexing evolving events from tweet streams.” IEEE Transactions on Knowledge and Data Engineering 27, no. 11 (2015): 3001-3015.
- Wang, Xufei, Lei Tang, Huan Liu, and Lei Wang. ”Learning with multi-resolution overlapping communities.” Knowledge and information systems 36, no. 2 (2013): 517-535.
- Wang, Chi, Jie Tang, Jimeng Sun, and Jiawei Han. ”Dynamic social influence analysis through time-dependent factor graphs.” In 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 239-246. IEEE, 2011.
- D.Suneetha, M.Shashi, “Discovering Trendy Topics and their Influence Patterns Relating Users of Social Media”, Journal of Advanced Research and dynamic Communication Systems JARDCS, Vol.11,No.2,ISSN :1943-023X, 2019.
- Dwarapu Suneetha, Mogalla Shashi, “Hybrid Clustering for Identification of Distinct Topics of a Domain Using User Influence Pattern”, International Journal of Innovative Technology and Exploring Engineering IJITEE, Vol.8,Issue- 2S2, ISSN: 2278-3075,2018.
- D.Suneetha, M.Shashi, “Keeping Track of evolution of Trendy Topics in Social Media”, International journal of computer sciences and engineering IJCSE, Vol.8, Issue.5, ISSN:2347-2693, May 2020.
- Culotta, Aron. ”Training a text classifier with a single word using Twitter Lists and domain adaptation.” Social Network Analysis and Mining 6.1 (2016): 8.
- Wang, Z., Shou, L., Chen, K., Chen, G. and Mehrotra, S., 2014. On summarization and timeline generation for evolutionary tweet streams. IEEE Transactions on Knowledge and Data Engineering, 27(5), pp.1301-1315.
- Lo, Yi-Chen, et al. ”What distinguish one from its peers in social networks?.” Data mining and knowledge discovery 27.3 (2013): 396-420.
- Tang, Lei, Huan Liu, and Jianping Zhang. ”Identifying evolving groups in dynamic multimode networks.” IEEE Transactions on Knowledge and Data Engineering 24.1 (2011): 72-85.
- Tang, Jiliang, Huiji Gao, and Huan Liu. ”mTrust: discerning multi-faceted trust in a connected world.” Proceedings of the fifth ACM international conference on Web search and data mining. 2012.
- Volkova, Svitlana, Theresa Wilson, and David Yarowsky. ”Exploring demographic language variations to improve multilingual sentiment analysis in social media.” In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1815-1827. 2013.
- Tang, Lei, and Huan Liu. ”Leveraging social media networks for classification.” Data Mining and Knowledge Discovery 23, no. 3 (2011): 447-478.
- Riquelme, Fabia´n, and Pablo Gonza´lez-Cantergiani. ”Measuring user influence on Twitter: A survey.” Information processing management 52.5 (2016): 949-975.
- Pennacchiotti, Marco, and Ana-Maria Popescu. ”A machine learning approach to twitter user classification.” Fifth international AAAI conference on weblogs and social media. 2011.
- Mart´ın EG, Lavesson N, Doroud M. Hashtags and followers. Social Network Analysis and Mining. 2016 Dec 1;6(1):12.
- Zhao WX, Li S, He Y, Chang EY, Wen JR, Li X. Connecting social media to e-commerce: Cold-start product recommendation using microblogging information. IEEE Transactions on Knowledge and Data Engineering. 2015 Dec 17;28(5):1147-59.
- Gromov, Vasilii A., and Anton S. Konev. ”Precocious identification of popular topics on Twitter with the employment of predictive clustering.” Neural Computing and Applications 28, no. 11 (2017): 3317-3322.
- Liu, H. and Tang, L., 2010. Toward Collective Behavior Prediction via Social Dimension Extraction. IEEE Intelligent Systems, 25(4), pp.19-25.
- Wang, Chieh-Jen, Yung-Wei Lin, Ming-Feng Tsai, and Hsin-Hsi Chen. ”Mining subtopics from different aspects for diversifying search results.” Information retrieval 16, no. 4 (2013): 452-483.
- Liu, Lu, Jie Tang, Jiawei Han, Meng Jiang, and Shiqiang Yang. ”Mining topic-level influence in heterogeneous networks.” In Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 199-208. 2010.
- Cataldi, Mario, Luigi Di Caro, and Claudio Schifanella. ”Emerging topic detection on twitter based on temporal and social terms evaluation.” In Proceedings of the tenth international workshop on multimedia data mining, pp. 1-10. 2010.
- Alshahrani, Mohammed, Fuxi Zhu, Lin Zheng, Soufiana Mekouar, and Sheng Huang. ”Selection of top-k influential users based on radius-neighborhood degree, multi-hops distance and selection threshold.” Journal of Big Data 5, no. 1 (2018): 1-20.
- Khan MA, Bollegala D, Liu G, Sezaki K. Multi-tweet summarization of real-time events. In2013 International Conference on Social Computing 2013 Sep 8 (pp. 128-133). IEEE.
- Wang, Xufei, Lei Tang, Huan Liu, and Lei Wang. ”Learning with multi-resolution overlapping communities.” Knowledge and information systems 36, no. 2 (2013): 517-535.
- Zhao WX, Li S, He Y, Chang EY, Wen JR, Li X. Connecting social media to e-commerce: Cold-start product recommendation using microblogging information. IEEE Transactions on Knowledge and Data Engineering. 2015 Dec 17;28(5):1147-59.
- Cai, Hongyun, Zi Huang, Divesh Srivastava, and Qing Zhang. ”Indexing evolving events from tweet streams.” IEEE Transactions on Knowledge and Data Engineering 27, no. 11 (2015): 3001-3015.
- Cordeiro, Ma´rio, Rui P. Sarmento, Pavel Brazdil, and Joa˜o Gama. ”Evolving networks and social network analysis methods and techniques.” Social Media and Journalism: Trends, Connections, Implications (2018): 101.
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