Files for the Task 8: RumourEval of ACL SEMEval 2017 (Determining rumour veracity and support for rumours).

The cue_word_list.txt contains the list of cue word features used in the model submitted by team UWaterloo. The system has been described in the paper "Detecting Stance towards Rumours with Topic Independent Features"

Paper Abstract

This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017. Identifying rumours, especially for breaking news events as they unfold, is a challenging task due to the absence of sufficient information about the exact rumour stories circulating on social media. Determining the stance of Twitter users towards rumourous messages could provide an indirect way of identifying potential rumours. The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier. With an accuracy of 0.78, our system achieved the second best performance on subtask-A of RumourEval.

Further details about the competition can be found here.

Citation

If you use the resources presented in this repo for your research, please cite:

@inproceedings{bahuleyan-vechtomova2017semeval,
  author    = {Bahuleyan, Hareesh  and  Vechtomova, Olga},
  title     = {UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {461--464},
  url       = {http://www.aclweb.org/anthology/S17-2080}
}