Yang et al, SIGIR 98
This Paper is relevant to our project on detecting controversial events in Twitter.
Contents
Citation
Yiming Yang, Thomas Pierce, and Jaime Carbonell. A study on retrospective and online event detection. In Proc. ACM SIGIR, pages 28–36, Melbourne, 1998.
Online version
A study on retrospective and online event detection
Summary
This paper addresses the problems of detecting events in news stories. They present solutions for retrospective event detection and online event detection using clustering techniques: group average clustering and single pass clustering. 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. Furthermore, they use a time window to limit the search space for similar news events to the last m received stories. They also tried reweighting similarity scores according to the temporal proximity of two documents.
They experimented with the Topic Detection and Tracking corpus.
Evaluation
They evaluated the ability of their systems to recover news events retrspectively, and also in an online setting. They compared their system's performance to human judgements for two specific events to analyse the behaviour of their algorithm.
Discussion
This paper presents a bag-of-words clustering approach to detecting new events in a news corpus. They showed how online detection is a more difficult problem than retrospective detection. This paper poses two important social problems related to bipartite social graphs and explained how those problems can be solved efficiently using random walks.
Related papers
There has been a lot of work on event detection.
- 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.
- Popular_Event_Tracking A method that take both interest and network structure into account.
- Automatic_Detection_and_Classification_of_Social_Events This paper aims at detecting and classifying social events using Tree kernels.
