Abstract
In this new era of Web 2.0, people around the world are expressing their feelings, sentiments, thoughts, daily activities, and local and global events happening around them in different social networking sites like Twitter, Facebook, etc. This generates vast amount of data in social media by registered users which are geographical and temporal information-oriented. This rich data could be potentially useful information and is being extensively used nowadays for different applications like user’s sentiment analysis, product or service reviews, real-time information extraction like traffic, disaster reporting, personalized message or user recommendation, and other areas. Extracting topic distribution from social media in spatial and temporal dimensions is an important research area. Hence, our focus of this study is on discussing various topical modeling techniques and their uses in different recent research works. This chapter gives a brief overview of the recent updates of spatiotemporal topical analysis using Twitter data. This study categorizes a large number of recent studies and articles in relevant area to get a summarized view of the state of the art in this field. This survey will help researchers, who are new to the domain, and provide a quick baseline for further research.
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References
W. Ahmed, Challenges of using twitter as a data source: an overview of current resources, http://blogs.lse.ac.uk/impactofsocialsciences/2015/09/28/challenges-of-using-twitter-as-a-data-source-resources/. Accessed 3 Apr 2018
S. Gaglio, G.L. Re, M. Morana, A framework for real-time twitter data analysis. Comput. Commun. 73, 236–242 (2016)
M. Hasan, M.A. Orgun, R. Schwitter, A survey on real-time event detection from the twitter data stream. J. Inf. Sci. 44(4), 443–463 (2017)
A. Java, X. Song, T. Finin, B. Tseng, 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 (ACM, 2007), pp. 56–65
K. Lee, D. Palsetia, R. Narayanan, M.M.A. Patwary, A. Agrawal, A. Choudhary, Twitter trending topic classification, in 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW) (IEEE, 2011), pp. 251–258
R. Li, K.H. Lei, R. Khadiwala, K.C.C. Chang, Tedas: a twitter-based event detection and analysis system, in 2012 IEEE 28th International Conference on Data Engineering (ICDE) (IEEE, 2012), pp. 1273–1276
R. Long, H. Wang, Y. Chen, O. Jin, Y. Yu, Towards effective event detection, tracking and summarization on microblog data, in International Conference on Web-Age Information Management (Springer, 2011), pp. 652–663
S. Mandal, G. Maji, Integrating telecom CDR and customer data from different operational databases and data warehouses into a central data warehouse for business analysis. Int. J. Eng. Res. Technol. 5(2), 516–523 (2016)
S. Petrović, M. Osborne, V. Lavrenko, Streaming first story detection with application to twitter, in Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Association for Computational Linguistics, 2010), pp. 181–189
P. Sharma, A. Agrawal, L. Alai, A. Garg, Challenges and techniques in preprocessing for twitter data. Int. J. Eng. Sci. Comput. 7(4), 6611–6613 (2017)
A. Soto, C. Ryan, F. Peña Silva, T. Das, J. Wolkowicz, E. Milios, S. Brooks, Data quality challenges in twitter content analysis for informing policy making in health care (2018)
B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, M. Demirbas, Short text classification in twitter to improve information filtering, in Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, 2010), pp. 841–842
E. Steiger, B. Resch, A. Zipf, Exploration of spatiotemporal and semantic clusters of twitter data using unsupervised neural networks. Int. J. Geogr. Inf. Sci. 30(9), 1694–1716 (2016)
S. Stieglitz, M. Mirbabaie, B. Ross, C. Neuberger, Social media analytics-challenges in topic discovery, data collection, and data preparation. Int. J. Inf. Manage. 39, 156–168 (2018)
G. Stilo, P. Velardi, Efficient temporal mining of micro-blog texts and its application to event discovery. Data Mining Knowl. Discov. 30(2), 372–402 (2016)
Twitter, Twitter developer platform, https://developer.twitter.com/en/docs/basics. Accessed 29 Mar 2018
W. Xie, F. Zhu, J. Jiang, E.P. Lim, K. Wang, Topicsketch: real-time bursty topic detection from twitter. IEEE Trans. Knowl. Data Eng. 28(8), 2216–2229 (2016)
G. Yadav, M. Joshi, R. Sasikala, Twitter data analysis: temporal and term frequency analysis with real-time event, in IOP Conference Series: Materials Science and Engineering, vol. 263 (IOP Publishing, 2017), p. 042081
W.X. Zhao, J. Jiang, J. He, Y. Song, P. Achananuparp, E.P. Lim, X. Li, Topical keyphrase extraction from twitter, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Association for Computational Linguistics, 2011), pp. 379–388
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Dutta, L., Maji, G., Sen, S. (2020). A Study on Spatiotemporal Topical Analysis of Twitter Data. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-7403-6_61
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DOI: https://doi.org/10.1007/978-981-13-7403-6_61
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