Comparison Das et al WSDM 2011 and Zhao et al AAAI 2007
The papers are
- Anish Das Sarma, A. Jain, C. Yu. Dynamic relationship and event discovery. In Proceedings of the fourth ACM international conference on Web search and data mining, 2011. 
- Qiankun Zhao, Prasenjit Mitra, and Bi Chen. Temporal and information flow based event detection from social text streams. In Proceedings of the 22nd national conference on Artificial intelligence - Volume 2, pages 1501–1506. AAAI Press, 2007. 
Comparative analysis of both papers
On a high level, both papers are interested in discovering events from large amount temporal information sources. Both of them leverage on user generated content, with Das et al using Wikipedia as their dataset, while Zhao et al used the Enron email corpus and Dailykos blogs.
In Das et al, their task was to first discover pairs of entities that were co-bursting in the same time period (of a week). Co-bursting means that both entities are mentioned significantly more than during other time periods. After which, the next step is to discover the relationships between such entities. This forms the foundation for an event, an n-ary relationship between entities that are bursty at the same time period.
Likewise, Zhao et al's task is to discover events, exploiting the temporal burstiness property of entities and text, and also the ``social aspect, where an event is being talked about more than usual by ``social actors.
Performance is measured using precision/recall/fscore of how well events are recovered with their model.
They found that taking temporal and social dimensions into account can increase their f-score significantly. Their approach of integrating these diverse features together in a step-wise manner was also found to perform better than just including features in a standard machine learning framework.
There has been a lot of work on event detection.
- A Statistical Model for Popular Events Tracking in Social Communities. 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.
- Detecting controversial events from Twitter. Popescu and Pennacchiotti, CIKM 10 This paper addresses the task of identifying controversial events using Twitter as a starting point.
- A study on retrospective and online event detection. Yang et al, SIGIR 98 This paper addresses the problems of detecting events in news stories.
- Automatic Detection and Classification of Social Events This paper aims at detecting and classifying social events using Tree kernels.