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| − | This [[Category::Paper]] is relevant to our project on detecting controversial events in Twitter.
| + | #REDIRECT [[Q._Zhao,_P._Mitra,_and_B._Chen._Temporal_and_information_flow_based_event_detection_from_social_text_streams._In_AAAI,_2007]] |
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| − | == Citation ==
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| − | Yiming Yang, Thomas Pierce, and Jaime Carbonell. A study on retrospective and online event detection. In Proc. ACM SIGIR, pages 28–36, Melbourne, 1998.
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| − | == Online version ==
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| − | [http://www.cs.cmu.edu/~jgc/publication/A_Study_Retrospective_Online_ACM_1998.pdf A study on retrospective and online event detection]
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| − | == Summary ==
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| − | This paper addresses the problems of [[AddressesProblem::Event_detection|detecting events]] in news stories.
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| − | They present solutions for retrospective event detection and online event detection using [[UsesMethod::clustering]] techniques: [[UsesMethod::group average clustering]] and [[UsesMethod::single pass clustering]].
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| − | They addressed the problem of the streaming nature of their data by doing incremental IDF, where the IDF values of terms in the corpus is incrementally updated as a new document is observed.
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| − | Furthermore, they use a time window to limit the search space for similar news events to the last m received stories.
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| − | They also tried reweighting similarity scores according to the temporal proximity of two documents.
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| − | They experimented with the [[UsesDataset::Topic Detection and Tracking]] corpus.
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| − | == Evaluation ==
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| − | They evaluated the ability of their systems to recover news events retrspectively, and also in an online setting.
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| − | They compared their system's performance to human judgements for two specific events to analyse the behaviour of their algorithm.
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| − | == Discussion ==
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| − | This paper presents a bag-of-words clustering approach to detecting new events in a news corpus.
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| − | They showed how online detection is a more difficult problem than retrospective detection.
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| − | This paper poses two important social problems related to bipartite social graphs and explained how those problems can be solved efficiently using random walks.
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| − | == Related papers ==
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| − | There has been a lot of work on event detection.
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| − | * [[RelatedPaper::Lin_et_al_KDD_2011|Lin et al, KDD 2011]] This paper address a method to observe and track the popular events or topics that evolve over time in the communities.
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| − | * [[UsesMethod::Popular_Event_Tracking|Popular Event Tracking]] A method that take both interest and network structure into account.
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| − | * [[RelatedPaper::Automatic_Detection_and_Classification_of_Social_Events|Automatic Detection and Classification of Social Events]] This paper aims at detecting and classifying social events using Tree kernels.
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| − | == Study plan ==
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| − | * Article: Group average agglomerative clustering [http://nlp.stanford.edu/IR-book/html/htmledition/group-average-agglomerative-clustering-1.html]
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| − | * Article: Single pass clustering [http://orion.lcg.ufrj.br/Dr.Dobbs/books/book5/chap16.htm]
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