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Exploiting Twitter for the Semantic Enrichment of Telecommunication Alarms

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Languages, Applications and Technologies (SLATE 2015)

Abstract

Everyday, several different alarms are triggered in a telecommunications network. Inspired by works that mine useful information from Twitter, we aim at exploiting this resource for semantically-enriching those alarms. We assume that, during the alarms, Twitter users would mention potential causes, and also that network customers would tweet to complain about the quality of their service. For this purpose, we explored a set of alarms and tweets from the same period of time and came to the conclusion that tweets on potential causes of the alarms are hard to find. The most significant findings are that, during an alarm, there are more tweets related to rain events, or those swearing and thus a sign of complaint.

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Notes

  1. 1.

    Numbers according to https://about.twitter.com/company.

  2. 2.

    https://dev.twitter.com/streaming/public.

  3. 3.

    http://mallet.cs.umass.edu/.

  4. 4.

    Although we could not find a specific study on the usage of Twitter in Portugal, our country is never listed in the top countries in terms of percentage of Twitter users. Also, in the World Map in http://www.beevolve.com/twitter-statistics#b1 (retrieved on March 2015) Portugal had one of the lightest shades of blue, which corresponds to the countries with less Twitter users.

References

  1. Alonso, O., Shiells, K.: Timelines as summaries of popular scheduled events. In: Proceedings of 22nd International Conference on World Wide Web Conference, Companion, WWW 2013, pp. 1037–1044. WWW/ACM, Geneva (2013)

    Google Scholar 

  2. Avvenuti, M., Cresci, S., Marchetti, A., Meletti, C., Tesconi, M.: Ears (earthquake alert and report system): a real time decision support system for earthquake crisis management. In: Proceedings of 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014 pp. 1749–1758. ACM, New York (2014)

    Google Scholar 

  3. Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: Proceedings of 5th International Conference on Weblogs and Social Media, ICWSM 2011. AAAI Press (2011)

    Google Scholar 

  4. Chua, F.C.T., Asur, S.: Automatic summarization of events from social media. In: Proceedings of 7th International Conference on Weblogs and Social Media, ICWSM 2013 (2013)

    Google Scholar 

  5. Culotta, A.: Lightweight methods to estimate influenza rates and alcohol sales volume from Twitter messages. Lang. Resour. Eval. 47(1), 217–238 (2013)

    Article  Google Scholar 

  6. Ghahremanlou, L., Sherchan, W., Thom, J.A.: Geotagging twitter messages in crisis management. Comput. J. 58(9), 1937–1954 (2015). doi:10.1093/comjnl/bxu034

    Article  Google Scholar 

  7. Guy, M., Earle, P., Ostrum, C., Gruchalla, K., Horvath, S.: Integration and dissemination of citizen reported and seismically derived earthquake information via social network technologies. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds.) IDA 2010. LNCS, vol. 6065, pp. 42–53. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  8. Li, C., Weng, J., He, Q., Yao, Y., Datta, A., Sun, A., Lee, B.S.: TwiNER: named entity recognition in targeted Twitter stream. In: Proceedings of 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 721–730. ACM, New York (2012)

    Google Scholar 

  9. Longueville, B.D., Smith, R.S., Luraschi, G.: “OMG, from here, i can see the flames!”: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: Zhou, X., Xie, X. (eds.) Proceedings of 2009 International Workshop on Location Based Social Networks (GIS-LBSN), pp. 73–80. ACM (2009)

    Google Scholar 

  10. Mao, H., Shuai, X., Kapadia, A.: Loose tweets: an analysis of privacy leaks on Twitter. In: Proceedings of 10th Annual ACM Workshop on Privacy in the Electronic Society, WPES 2011, pp. 1–12. ACM (2011)

    Google Scholar 

  11. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of 7th International Conference on Language Resources and Evaluation, LREC 2010, ELRA, Valletta, Malta, May 2010

    Google Scholar 

  12. Ritter, A., Mausam, Etzioni, O., Clark, S.: Open domain event extraction from Twitter. In: Proceedings of 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, pp. 1104–1112. ACM (2012)

    Google Scholar 

  13. Rosa, H., Carvalho, J.P., Batista, F.: Detecting a tweet’s topic within a large number of Portuguese Twitter trends. In: Proceedings of 3rd Symposium on Languages. Applications and Technologies, pp. 185–199. OASICS, Schloss Dagstuhl, June 2014

    Google Scholar 

  14. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of 19th International Conference on World Wide Web, WWW 2010, pp. 851–860. ACM, New York (2010)

    Google Scholar 

  15. Santos, J.C., Matos, S.: Predicting flu incidence from Portuguese tweets. In: Proceedings of International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2013, Copicentro Editorial, pp. 11–18 (2013)

    Google Scholar 

  16. Souza, M., Vieira, R.: Sentiment analysis on twitter data for Portuguese language. In: Caseli, H., Villavicencio, A., Teixeira, A., Perdigão, F. (eds.) PROPOR 2012. LNCS, vol. 7243, pp. 241–247. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Tanev, H., Ehrmann, M., Piskorski, J., Zavarella, V.: Enhancing event descriptions through twitter mining. In: Breslin, J.G., Ellison, N.B., Shanahan, J.G., Tufekci, Z. (eds.) Proceedings of 6th International Conference on Weblogs and Social Media, ICWSM 2012. AAAI Press (2012)

    Google Scholar 

  18. Wang, X., Gerber, M.S., Brown, D.E.: Automatic crime prediction using events extracted from Twitter posts. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 231–238. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Wang, Y., Xie, L., Sundaram, H.: Social event detection with clustering and filtering. In: Working Notes Proceedings of MediaEval 2011 Workshop, vol. 807. CEUR-WS.org (2011)

    Google Scholar 

  20. Zhang, R., Li, W., Gao, D., You, O.: Automatic twitter topic summarization with speech acts. IEEE Trans. Audio Speech Lang. Process. 21(3), 649–658 (2013)

    Article  Google Scholar 

  21. Zielinski, A., Middleton, S.E., Tokarchuk, L.N., Wang, X.: Social media text mining and network analysis for decision support in natural crisis management. In: Proceedings of 10th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2013, Karlsruher Institut fur Technologie, pp. 840–845 (2013)

    Google Scholar 

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Acknowledgements

This work was developed in the scope of a project funded by Portugal Telecom Inovação e Sistemas, under the cooperation and innovation programme between PT and academic organisations.

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Correspondence to Hugo Gonçalo Oliveira .

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Gonçalo Oliveira, H., Marques, J., Cortesão, L. (2015). Exploiting Twitter for the Semantic Enrichment of Telecommunication Alarms. In: Sierra-Rodríguez, JL., Leal, JP., Simões, A. (eds) Languages, Applications and Technologies. SLATE 2015. Communications in Computer and Information Science, vol 563. Springer, Cham. https://doi.org/10.1007/978-3-319-27653-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-27653-3_3

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