A Study on Timeframe based Radical Event Detection on Online News Archive
Downloads
In the field of Natural Language Processing, Event detection has been an active area of research. While most of the work emphasizes on detecting all possible events by using datasets comprising of news article belonging from a broad range of time and location (collected either from a single data source or multiple data sources), this work focuses on detecting an event using short timeframes of News Articles extracted from an Online News Archive. Also, this is a non-targeting approach when it comes to detecting the theme or category of event it focuses on but becomes a targeted one as it tries to put all its focus on detecting one major event when applied on a timeframe. While other approaches are based on various techniques like semantic graphs, clustering algorithms, topic detection and tracking for event detection, this approach detects events with the help of a filtering mechanism combined with a novel threshold. This algorithm leverages detection of trending characteristics of an event in a timeframe and filtering news articles which show similar characteristics. And finally, we evaluate our algorithm with precision, recall and the percentage of articles which are actually related to the event in a timeframe termed as dominance percentage.
Yifang Wei, Lisa Singh, Brian Gallagher and David Buttler, “Overlapping Target Event and Story Line Detection of Online Newspaper Articles”. In IEEE International Conference on Data Science and Advanced Analytics (DSAA),2016, pages=222-232
J. Allan, R. Papka, and V. Lavrenko, “On-line new event detection and tracking,” in SIGIR. ACM, 1998, pp. 37–45.
Y. Yang, T. Pierce, and J. Carbonell, “A study of retrospective and online event detection,” in SIGIR. ACM, 1998, pp. 28–36.
T. Brants, F. Chen, and A. Farahat, “A system for new event detection,” in SIGIR. ACM, 2003, pp. 330–337.
G. P. C. Fung, J. X. Yu, P. S. Yu, and H. Lu, “Parameter free bursty events detection in text streams,” in VLDB. VLDB Endowment, 2005, pp. 181–192.
T. Lappas, B. Arai, M. Platakis, D. Kotsakos, and D. Gunopulos, “On burstiness-aware search for document sequences,” in KDD. ACM, 2009, pp. 477–486.
T. Lappas, M. R. Vieira, D. Gunopulos, and V. J. Tsotras, “On the spatiotemporal burstiness of terms,” in VLDB, 2012, pp. 836–847.
J. Weng and B.-S. Lee, “Event detection in twitter.” ICWSM, vol. 11, pp. 401–408, 2011.
T. Sakaki, M. Okazaki, and Y. Matsuo, “Earthquake shakes twitter users: real-time event detection by social sensors,” in WWW. ACM, 2010, pp. 851–860.
N. Ramakrishnan, P. Butler, S. Muthiah, N. Self, R. Khandpur, P. Saraf, W. Wang, J. Cadena, A. Vullikanti, G. Korkmaz et al., “’beating the news’ with embers: Forecasting civil unrest using open source indicators,” in KDD. ACM, 2014, pp. 1799–1808.
J. Leskovec, L. Backstrom, and J. Kleinberg, “Meme- tracking and the dynamics of the news cycle,” in KDD. ACM, 2009, pp. 497–506.
C. Li, A. Sun, and A. Datta, “Twevent: segment-based event detection from tweets,” in CIKM. ACM, 2012, pp. 155–164.
H. Sayyadi, M. Hurst, and A. Maykov, “Event detection and tracking in social streams.” in ICWSM, 2009.
F. Xu, H. Uszkoreit, and H. Li, “Automatic event and relation detection with seeds of varying complexity,” in AAAI workshop event extraction and synthesis, 2006, pp. 12–17.
Y. Nishihara, K. Sato, and W. Sunayama, “Event extraction and visualization for obtaining personal experiences from blogs,” in Human Interface and the Management of Information. Information and Interaction. Springer, 2009, pp. 315–324.
A. Ritter, O. Etzioni, S. Clark et al., “Open domain event extraction from twitter,” in KDD. ACM, 2012, pp. 1104–1112.
K. Lei, R. Khadiwala, and K.-C. T. Chang, “A twitter- based event detection and analysis system,” in ICDE, 2012.
S. Muthiah, B. Huang, J. Arredondo, D. Mares, L. Getoor, G. Katz, and N. Ramakrishnan, “Planned protest modeling in news and social media.” in AAAI, 2015, pp. 3920–3927.
D. Wang and W. Ding, “A hierarchical pattern learning framework for forecasting extreme weather events,” in ICDM. IEEE, 2015, pp. 1021– 1026
