Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Segmentation and Automated Social Hierarchy Detection through Email Network Analysis
Advances in Web Mining and Web Usage Analysis
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling socio-cultural phenomena in discourse
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Automatic committed belief tagging
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Extracting social power relationships from natural language
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Email formality in the workplace: a case study on the Enron corpus
LSM '11 Proceedings of the Workshop on Languages in Social Media
Predicting overt display of power in written dialogs
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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In my thesis I propose a data-oriented study on how social power relations between participants manifest in the language and structure of online written dialogs. I propose that there are different types of power relations and they are different in the ways they are expressed and revealed in dialog and across different languages, genres and domains. So far, I have defined four types of power and annotated them in corporate email threads in English and found support that they in fact manifest differently in the threads. Using dialog and language features, I have built a system to predict participants possessing these types of power within email threads. I intend to extend this system to other languages, genres and domains and to improve it's performance using deeper linguistic analysis.