Abstract
Personal users on Twitter frequently post observations about their immediate environment as part of the 500 million tweets posted everyday. These observations and their implicitly associated time and location data are a valuable source of information for monitoring objects and events, such as earthquake, hailstorm, and shooting incidents. However, given the informal and uncertain expressions used in personal Twitter messages, and the various type of accounts existing on Twitter, capturing personal observations of objects and events is challenging. In contrast to the existing supervised approaches, which require significant efforts for annotating examples, in this paper, we propose an unsupervised approach for filtering personal observations. Our approach employs lexical analysis, user profiling and classification components to significantly improve filtering precision. To identify personal accounts, we define and compute a mean user profile for a dataset and employ distance metrics to evaluate the similarity of the user profiles under analysis to the mean. Our extensive experiments with real Twitter data show that our approach consistently improves filtering precision of personal observations by around 22 %.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Carroll, T.Z.J.: Unsupervised classification of sentiment and objectivity in Chinese text. In: Third International Joint Conference on Natural Language Processing, p. 304 (2008)
Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International World Wide Web Conference, pp. 675–684 (2011)
Chung, D.S., Nah, S.: Media credibility and journalistic role conceptions: views on citizen and professional journalists among citizen contributors. J. Mass Media Ethics 28(4), 271–288 (2013)
Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Heidelberg (2010)
Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: Proceedings of 13th International Conference on Data Mining, pp. 1103–1108 (2013)
Li, R., Lei, K.H., Khadiwala, R., Chang, K.-C.: TEDAS: a Twitter-based event detection and analysis system. In: Proceedings of 28th International Conference on Data Engineering, pp. 1273–1276 (2012)
Lingad, J., Karimi, S., Yin, J.: Location extraction from disaster-related microblogs. In: Proceedings of the 22nd International World Wide Web Conference Companion, pp. 1017–1020 (2013)
Maddock, J., Starbird, K., Al-Hassani, H., Sandoval, D.E., Orand, M., Mason, R.M.: Characterizing online rumoring behavior using multi-dimensional signatures. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 228–241 (2015)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
Mukherjee, S., Weikum, G., Danescu-Niculescu-Mizil, C.: People on drugs: credibility of user statements in health communities. In: Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining, pp. 65–74 (2014)
Olteanu, A., Castillo, C., Diaz, F., Vieweg, S.: CrisisLex: a lexicon for collecting and filtering microblogged communications in crises. In: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, pp. 376–385 (2014)
Sakaki, T., Okazaki, M., Matsuo, Y.: Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans. Knowl. Data Eng. 25(4), 919–931 (2013)
Santorini, B.: Part-of-speech tagging guidelines for the penn treebank project (3rd revision). Technical report MS-CIS-90-47, University of Pennsylvania Department of Computer and Information Science Technical (1990)
Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: 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, pp. 841–842 (2010)
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)
Unankard, S., Li, X., Sharaf, M., Zhong, J., Li, X.: Predicting elections from social networks based on sub-event detection and sentiment analysis. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014. LNCS, vol. 8787, pp. 1–16. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11746-1_1
Unankard, S., Li, X., Sharaf, M.A.: Emerging event detection in social networks with location sensitivity. World Wide Web Journal (2015, in press)
Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says what to whom on Twitter. In: Proceedings of the 20th International World Wide Web Conference, pp. 705–714 (2011)
Zhang, Y., Szabo, C., Sheng, Q.Z.: Sense and focus: towards effective location inference and event detection on Twitter. In: The Proceedings of the 16th International Conference on Web Information Systems Engineering (2015)
Zhang, Y., Szabo, C., Sheng, Q.Z., Fang, X.S.: Classifying perspectives on Twitter: immediate observation, affection, and speculation. In: The Proceedings of the 16th International Conference on Web Information Systems Engineering (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Zhang, Y., Szabo, C., Sheng, Q.Z. (2016). Improving Object and Event Monitoring on Twitter Through Lexical Analysis and User Profiling. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-48743-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48742-7
Online ISBN: 978-3-319-48743-4
eBook Packages: Computer ScienceComputer Science (R0)