Online textual information has been increased tremendously over the years, leading to the demand of information verification. As a result, Natural Language Processing (NLP) research on tasks such as stance detection (Derczynski et al., 2017) and fact verification (Thorne et al., 2018) is gaining momentum, as an attempt to automatically identify misinformation over the social networks (e.g., Mastodon and Twitter).
To that end, within the scope of EUNOMIA a stance classification model was trained, which involves identifying the attitude of EUNOMIA-consent Mastodon users towards the truthfulness of the rumour they are discussing. In particular, transfer learning was applied to fine tune the RoBERTa (Robustly optimized BERT) model (Liu et al., 2019) using the public available dataset SemEval 2019 Subtask 7A (Gorrell et al., 2019). This dataset contains Twitter threads and each tweet (e.g., Hostage-taker in supermarket siege killed, reports say. #ParisAttacks –LINK) in the tree-structured thread is categorised into one of the following four categories:
- Support: the author of the response supports the veracity of the rumour they are responding to (e.g., I’ve heard that also).
- Deny: the author of the response denies the veracity of the rumour they are responding to (e.g., That’s a lie).
- Query: the author of the response asks for additional evidence in relation to the veracity of the rumour they are responding to (e.g., Really?).
- Comment: the author of the response makes their own comment without a clear contribution to assessing the veracity of the rumour they are responding to (e.g., True tragedy).
Our model achieved 85.1% accuracy and 62.75 % F1-score macro. Due to the fact that this dataset includes posts using arbitrary ways of language (e.g., OMG that aint right ☹) the obtained scores are not spectacular, but even so, our approach surpasses the state-of-the-art results (i.e., 81.79% accuracy and 61.87% F1-score) for this dataset (Yang et al., 2019).
The service has been containerized and will be soon integrated with the rest of the EUNOMIA platform as another useful trustworthiness indicator for the users.
Derczynski, L., Bontcheva, K., Liakata, M., Procter, R., Hoi, G.W., & Zubiaga, A. (2017). SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours. SemEval@ACL.
Gorrell, G., Bontcheva, K., Derczynski, L., Kochkina, E., Liakata, M., & Zubiaga, A. (2019). SemEval-2019 Task 7: RumourEval: Determining rumour veracity and support for rumours. In Proceedings of SemEval. ACL.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. ArXiv, abs/1907.11692.
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018). FEVER: a large-scale dataset for Fact Extraction and VERification. ArXiv, abs/1803.05355.
Yang, R., Xie, W., Liu, C., & Yu, D. (2019). BLCU_NLP at SemEval-2019 Task 7: An Inference Chain-based GPT Model for Rumour Evaluation. SemEval@NAACL-HLT.