Framework for the Discovery of Newsworthy Events in Social Media

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Duarte, Fernando José Fradique; Pereira, Óscar Mortágua; Aguiar, Rui L.;
(2019)
  • Publisher: IGI Global
  • Related identifiers: doi: 10.4018/IJOCI.2019070103
  • Subject: Jarvis-Patrick Clustering | Event Detection | Directed Acyclic Graph | Dynamic Programming | Naïve Bayes | Learning | Random Forest | SVM | Machine XGBoost | KNN Neighbors

The new communication paradigm established by social media along with its growing popularity in recent years contributed to attract an increasing interest of several research fields. One such research field is the field of event detection in social media. The contributi... View more
  • References (6)

    Alsaedi, N., Burnap, P., & Rana, O. (2017). Can We Predict a Riot? Disruptive Event Detection Using Twitter. ACM Transactions on Internet Technology, 17(2).10.1145/2996183 Atefeh, F., & Khreich, W. (2015). A Survey of Techniques for Event Detection in Twitter. Computational Intelligence,31(1),132-164.doi:10.1111/coin.12017 Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130,57-68.10.1016/j.techfore.2017.11.002 Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining (pp. 785-794). San Francisco, CA: ACM.

    da Silva, J. F., & Lopes, G. P. (1999). A Local Maxima method and a Fair Dispersion Normalization for extracting multi-word units from corpora. In Proceedings of the 6th Meeting on the Mathematics of Language (pp.369-381).AcademicPress.

    Nicolaos, P., Ioannis, K., & Dimitrios, G. (2016). Detecting Events in Online Social Networks: Definitions, Trends and Challenges. In. Lecture Notes in Computer Science: Vol. 9580. Solving Large Scale Learning Tasks.

    Challenges and Algorithms(pp.42-84).Cham:Springer;doi:10.1007/978-3-319-41706-6_2 Papadopoulos, S., Corney, D., & Aiello, L. M. (2014). SNOW 2014 Data Challenge: Assessing the Performance ofNewsTopicDetectionMethodsinSocialMedia.Proceedings of the SNOW 2014 Data Challenge.

    Pedregosa,F.,Varoquaux,G.,Gramfort,A.,Michel,V.,Thirion,B.,Grisel,O.,&Duchesnay,É.et al.(2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830. doi:10.1007/ s13398-014-0173-7.2 Phuvipadawat, S., & Murata, T. (2010). Breaking news detection and tracking in Twitter. In 2010 IEEE/WIC/ ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 120-123). IEEE. doi:10.1109/WI-IAT.2010.205 Popescu, A.-M., Pennacchiotti, M., & Paranjpe, D. (2011). Extracting events and event descriptions from Twitter. In Proceedings of the 20th International Conference companion on World Wide Web (pp. 105-106). Academic Press.doi:10.1145/1963192.1963246 Qin, Y., Zhang, Y., Zhang, M., & Zheng, D. (2013). Feature-Rich Segment-Based News Event Detection on Twitter. In Sixth International Joint Conference on Natural Language Processing (pp. 302-310). Nagoya, Japan: AsianFederationofNaturalLanguageProcessing.

    Vilaça, A., Antunes, M., & Gomes, D. N. (2015). TVPulse: detecting TV highlights in Social Networks. 10th Conference on Telecommunications.

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