@inproceedings{radhakrishnan-etal-2020-little,
title = "{``}A Little Birdie Told Me ... {''} - Inductive Biases for Rumour Stance Detection on Social Media",
author = "Radhakrishnan, Karthik and
Kanakagiri, Tushar and
Chakravarthy, Sharanya and
Balachandran, Vidhisha",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wnut-1.31",
doi = "10.18653/v1/2020.wnut-1.31",
pages = "244--248",
abstract = "The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works, we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media fine-tuning of BERT allow for better language understanding, thus yielding an F1 score of 58.7 on the SemEval 2019 task on rumour stance detection.",
}
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<abstract>The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works, we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media fine-tuning of BERT allow for better language understanding, thus yielding an F1 score of 58.7 on the SemEval 2019 task on rumour stance detection.</abstract>
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%0 Conference Proceedings
%T “A Little Birdie Told Me ... ” - Inductive Biases for Rumour Stance Detection on Social Media
%A Radhakrishnan, Karthik
%A Kanakagiri, Tushar
%A Chakravarthy, Sharanya
%A Balachandran, Vidhisha
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F radhakrishnan-etal-2020-little
%X The rise in the usage of social media has placed it in a central position for news dissemination and consumption. This greatly increases the potential for proliferation of rumours and misinformation. In an effort to mitigate the spread of rumours, we tackle the related task of identifying the stance (Support, Deny, Query, Comment) of a social media post. Unlike previous works, we impose inductive biases that capture platform specific user behavior. These biases, coupled with social media fine-tuning of BERT allow for better language understanding, thus yielding an F1 score of 58.7 on the SemEval 2019 task on rumour stance detection.
%R 10.18653/v1/2020.wnut-1.31
%U https://aclanthology.org/2020.wnut-1.31
%U https://doi.org/10.18653/v1/2020.wnut-1.31
%P 244-248
Markdown (Informal)
[“A Little Birdie Told Me ... ” - Inductive Biases for Rumour Stance Detection on Social Media](https://aclanthology.org/2020.wnut-1.31) (Radhakrishnan et al., WNUT 2020)
ACL