Abstract
With the development of the Internet, Big Data analysis of social media has become a hot research topic in recent years. However, a challenging problem in social media analysis is how to intelligently detect event information from massive media data and how to help users quickly understand event content. Therefore, we propose a method to extract important time periods of events from media data to analyze the media event information, and develop an interactive visual system based on the “5W” principle. The system provides an interactive analysis platform for exploring media Big Data (e.g., Twitter data). The system uses a dynamic topic model to extract topics, a Naive Bayesian classifier to distinguish emotions, and explores events based on the evolution of time, topics, and emotions. In terms of visualization, the system allows users to add some annotations based on segmentation of complex information and highlight important events by adding different story cards to the timeline so that users can get a quick overview of events. Users can also interactively modify the story cards during the exploration process. Finally, several case studies show that our system can effectively reduce the time required to understand social media data and allow users to quickly explore the full picture of an event through an interactive approach.
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References
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Coling 2010: Posters, pp. 36–44 (2010)
Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, 25–29 June 2006 (2006)
Chen, S., Li, S., Chen, S., Yuan, X.: R-map: a map metaphor for visualizing information reposting process in social media. IEEE Trans. Visual Comput. Graph. 26(1), 1204–1214 (2020)
Chen, S., Chen, S., Wang, Z., Liang, J., Wu, Y.: D-map: visual analysis of ego-centric information diffusion patterns in social media. In: 2016 IEEE Conference on Visual Analytics Science and Technology (VAST) (2016)
Chen, S., Chen, S., Yuan, X., Lin, L., Zhang, X.L.: E-map: a visual analytics approach for exploring significant event evolutions in social media. In: IEEE Vast (2017)
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)
Dexi, L., Changxuan, W.: Survey on automatic summarization of socialized short text. J. Chin. Comput. Syst. 034(012), 2764–2771 (2013)
Dou, W., Wang, X., Skau, D., Ribarsky, W., Zhou, M.X.: Leadline: interactive visual analysis of text data through event identification and exploration. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST) (2012)
Eccles, R., Kapler, T., Harper, R., Wright, W.: Stories in geotime. Inf. Vis. 7(1), 3–17 (2008)
Guo, S., Xu, K., Zhao, R., Gotz, D., Zha, H., Cao, N.: Eventthread: visual summarization and stage analysis of event sequence data. IEEE Trans. Vis. Comput. Graph. 24(1), 56–65 (2018)
Li, J., Chen, S., Chen, W., Andrienko, G., Andrienko, N.: Semantics-space-time cube. A conceptual framework for systematic analysis of texts in space and time. IEEE Trans. Vis. Comput. Graph. 26(4), 1789–1806 (2018)
Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 227–236 (2011)
Nguyen, P.H., Xu, K., Walker, R., Wong, B.L.W.: Schemaline: timeline visualization for sensemaking. In: 2014 18th International Conference on Information Visualisation (2014)
Ruest, N.: climatemarch tweets April 19–May 3, 2017 (2017). https://doi.org/10.5683/SP/KZZVZW
Tong, C., et al.: Storytelling and visualization: an extended survey. Information 9(3), 65 (2018)
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Zhang, Y., Li, Z., Xi, D. (2022). An Interactive Visual System for Data Analytics of Social Media. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_6
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DOI: https://doi.org/10.1007/978-3-030-95391-1_6
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