Skip to main content
SpringerLink
Log in

TwitterTrends: a spatio-temporal trend detection and related keywords recommendation scheme

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Twitter is a very popular online social networking service that enables its users to post and share text-based messages known as tweets. Even though one tweet may contain at most only 140 characters, the number of tweets generated daily is enormous and hence, collectively, they can give important clues to the resolution of interesting issues such as those associated with public opinion and current trends and the retrieval or recommendation of hot multimedia contents. In this paper, we propose a spatio-temporal trend detection and related keyword recommendation scheme for tweets called TwitterTrends. Our scheme can identify hot keywords and recommend their related keywords at a given location and time by analyzing user tweets and their metadata such as GPS data. The scheme is based on a client–server collaboration model for efficiency. The client on the user device manages user interactions with the Twitter server, such as the writing and uploading of tweets. In addition, it selects candidate trend keywords from tweets by simple filtering, collects user location data from the mobile user device, and sends them to our trend processing (TP) server. Our scheme can show trend keywords and their related keywords intuitively and expand them on the fly by displaying relevant keywords collected from portal sites such as Wikipedia and Google. The TP server collects candidate trend keywords and metadata from all the clients and analyzes them to detect spatio-temporal trend keywords and their related keywords by considering their co-occurrence in tweets. Our scheme is very robust in that it can handle typical input events such as abbreviations and typing errors that occur when writing tweets on mobile devices as well as provide supplementary keywords from portal sites. We implemented a prototype system and performed various experiments to demonstrate that our scheme can achieve satisfactory performance and scalability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Twitter. http://en.wikipedia.org/wiki/Twitter

  2. Kim, D., Rho, S., Hwang, E.: Location-based large-scale landmark image recognition scheme for mobile devices. In: Proceedings of the 3rd FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (MUSIC 2012), Canada, pp. 47–52 (2012)

  3. Wikipedia. http://www.wikipedia.org

  4. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  5. Yokomoto, D., Makita, K., Suzuki, H., Koike, D., Utsuro, T., Kawada, Y., Fukuhara, T.: LDA-based topic modeling in labeling blog posts with Wikipedia entries. In: Wang, H., Zou, L., Huang, G., He, J., Pang, C., Zhang, H., Zhao, D., Yi, Z. (eds.) Web technologies and applications, pp. 114–124. Springer, Berlin (2012)

    Chapter  Google Scholar 

  6. Ramage, D., Dumais, S., Liebling, D.: Characterizing microblogs with topic models. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM 2010), pp. 130–137 (2010)

  7. Hong, L., Davison, B.D.: Empirical study of topic modeling in Twitter. In: Proceedings of the First Workshop on Social Media Analytics, pp. 80–88. ACM, New York (2010)

  8. Si, X., Sun, M.: Tag-LDA for scalable real-time tag recommendation. J. Comput. Inf. Syst. 6, 23–31 (2009)

    Google Scholar 

  9. Quercia, D., Askham, H., Crowcroft, J.: TweetLDA: supervised topic classification and link prediction in Twitter. In: Proceedings of the ACM Web Science 2012, pp. 373–376. ACM, New York (2012)

  10. Zhao, X., JIANG, J., He, J., Song, Y., Achananuparp, P., LIM, E.P., Li, X.: Topical keyphrase extraction from Twitter. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL’11) (2011)

  11. Wu, W., Zhang, B., Ostendorf, M.: Automatic generation of personalized annotation tags for Twitter users. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 689–692. Association for Computational Linguistics, Stroudsburg (2010)

  12. Li, Z., Zhou, D., Juan, Y.-F., Han, J.: Keyword extraction for social snippets. In: Proceedings of the 19th International Conference on World Wide Web, pp. 1143–1144. ACM, New York (2010)

  13. Moon, I.-C., Kim, Y.-M., Lee, H.-J., Oh, A.H.: Temporal issue trend identifications in blogs. International Conference on Computational Science and Engineering (CSE’09), pp. 619–626 (2009)

  14. Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45. ACM, New York (1998)

  15. Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 28–36. ACM, New York (1998)

  16. Kumaran, G., Allan, J.: Text classification and named entities for new event detection. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 297–304. ACM, New York (2004)

  17. Li, Z., Wang, B., Li, M., Ma, W.-Y.: A probabilistic model for retrospective news event detection. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 106–113. ACM, New York (2005)

  18. Cormode, G., Hadjieleftheriou, M.: Finding the frequent items in streams of data. Commun. ACM 52, 97–105 (2009)

    Article  Google Scholar 

  19. Grinev, M., Grineva, M., Boldakov, A., Novak, L., Syssoev, A., Lizorkin, D.: Sifting micro-blogging stream for events of user interest. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 837–837. ACM, New York (2009)

  20. Sayyadi, H., Hurst, M., Maykov, A.: Event detection and tracking in social streams. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2009). AAAI (2009)

  21. Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 291–300. ACM, New York (2010)

  22. Kawamae, N.: Trend analysis model: trend consists of temporal words, topics, and timestamps. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 317–326. ACM, New York (2011)

  23. Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 1155–1158. ACM, New York (2010)

  24. Alvanaki, F., Sebastian, M., Ramamritham, K., Weikum, G.: EnBlogue: emergent topic detection in web 2.0 streams. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 1271–1274. ACM, New York (2011)

  25. Alvanaki, F., Michel, S., Ramamritham, K., Weikum, G.: See what’s enBlogue: real-time emergent topic identification in social media. In: Proceedings of the 15th International Conference on Extending Database Technology, pp. 336–347. ACM, New York (2012)

  26. Bawab, Z. A., Mills, G. H., Crespo, J.: Finding trending local topics in search queries for personalization of a recommendation system. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397–405. ACM, New York (2012)

  27. Google Trends. http://trends.google.com

  28. Jiang, M., Cui, P., Wang, F., Yang, Q., Zhu, W., Yang, S.: Social recommendation across multiple relational domains. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1422–1431. ACM, New York (2012)

  29. Cui, P., Wang, F., Liu, S., Ou, M., Yang, S., Sun, L.: Who should share what? Item-level social influence prediction for users and posts ranking. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 185–194. ACM, New York (2011)

  30. Cui, P., Jin, S., Yu, L., Wang, F., Zhu, W., Yang, S.: Cascading outbreak prediction in networks: a data-driven approach. In: Proceeding of the 19th ACM SIGKDD conference on knowledge discovery and data mining (2013)

  31. Arbor.js—a graph visualization library using web workers and jQuery. http://arborjs.org

  32. Raphaël—JavaScript Library. http://raphaeljs.com/

  33. Hadoop. http://hadoop.apache.org

Download references

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2012627) and the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2013-H0301-13-3006) supervised by the NIPA (National IT Industry Promotion Agency).

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Korea University, Seoul, South Korea

    Daehoon Kim, Daeyong Kim & Eenjun Hwang

  2. Department of Computer Engineering, Mevlana University, Konya, Turkey

    Seungmin Rho

Authors
  1. Daehoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daeyong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eenjun Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seungmin Rho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eenjun Hwang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, D., Kim, D., Hwang, E. et al. TwitterTrends: a spatio-temporal trend detection and related keywords recommendation scheme. Multimedia Systems 21, 73–86 (2015). https://doi.org/10.1007/s00530-013-0342-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-013-0342-0

Keywords

Search

Navigation