@inproceedings{bahuleyan-vechtomova-2017-uwaterloo,
title = "{UW}aterloo at {S}em{E}val-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features",
author = "Bahuleyan, Hareesh and
Vechtomova, Olga",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2080",
doi = "10.18653/v1/S17-2080",
pages = "461--464",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features
%A Bahuleyan, Hareesh
%A Vechtomova, Olga
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 aug
%I Association for Computational Linguistics
%C Vancouver, Canada
%F bahuleyan-vechtomova-2017-uwaterloo
%X 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.
%R 10.18653/v1/S17-2080
%U https://aclanthology.org/S17-2080
%U https://doi.org/10.18653/v1/S17-2080
%P 461-464
Markdown (Informal)
[UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features](https://aclanthology.org/S17-2080) (Bahuleyan & Vechtomova, SemEval 2017)
ACL