Difference between revisions of "Controversial events detection"
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*[[RelatedPaper::Rodriguez et al. KDD 2010|Gomez Rodriguez, M., J. Leskovec, and A. Krause. 2010. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 1019–1028]]. This paper addresses the problem of inferring underlying networks in the diffusion process of social networks, which is related to the faction discovery problem we study in this project. | *[[RelatedPaper::Rodriguez et al. KDD 2010|Gomez Rodriguez, M., J. Leskovec, and A. Krause. 2010. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 1019–1028]]. This paper addresses the problem of inferring underlying networks in the diffusion process of social networks, which is related to the faction discovery problem we study in this project. | ||
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| + | *[[RelatedPaper::Cosley et al 2010|Cosley, D., D. Huttenlocher, J. Kleinberg, X. Lan, and S. Suri. 2010. Sequential Influence Models in Social Networks, In Proc. 4th International Conference on Weblogs and Social | ||
| + | Media]]. In this paper the authors study the temporal dynamics of information diffusion in social networks. The results found could give us some insights into the design of our model. | ||
== Related materials == | == Related materials == | ||
Revision as of 01:46, 4 October 2012
Contents
Controversial event detection
Team members
Project idea
In our project, we propose to jointly detect events and the controversy surrounding it in the context of social media. For example, Christmas day is an event that receives the most attention around December 25th, while the Presidential debates once every four years. Controversy-wise, Christmas day is relatively one sided, with most of the text mentioning it being relatively homogeneous. In contrast, the Presidential debates event will have obvious sides (supporting the different candidates).
Our goal is not only to detect controversial events, but also to discover what the different sides are - both grouping the individuals associated with each faction and describing how each faction talks about the event differently.
We propose to use a probabilistic graphical model to achieve our goals of learning these latent structures from the data without labeled training data.
Data
Our main data source will be Twitter, and as a start we intend to use tweets over a three month period in year 2012 (the exact date range to be decided). Some possibly controversial events that have occurred this year are the republican primaries, Grammy awards, weekly football games during the NFL season, etc. In addition to the textual content, the timestamps, locations (partially observed) and identities (of the user posting a tweet) could be useful features for our model.
Related work
- A study on retrospective and online event detection. Yang et al, SIGIR 98 This paper addresses the problems of detecting events in news stories. They used clustering with a vector space model to group temporally close events together.
- Temporal and information flow based event detection from social text streams. Zhao et al, AAAI 07 The authors proposes a method for detecting events from social text stream by exploiting more than just the textual content, but also exploring the temporal and social dimensions of their data.
- Automatic Detection and Classification of Social Events. Agarwal and Rambow, ACL 10 This is one of the few works we found relating to controversial events in social media. The authors aims at detecting and classifying social events using Tree kernels.
- Gomez Rodriguez, M., J. Leskovec, and A. Krause. 2010. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 1019–1028. This paper addresses the problem of inferring underlying networks in the diffusion process of social networks, which is related to the faction discovery problem we study in this project.
- Cosley, D., D. Huttenlocher, J. Kleinberg, X. Lan, and S. Suri. 2010. Sequential Influence Models in Social Networks, In Proc. 4th International Conference on Weblogs and Social Media. In this paper the authors study the temporal dynamics of information diffusion in social networks. The results found could give us some insights into the design of our model.
