SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 1120–1124 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 1120 SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions Miguel Ángel García-Cumbreras1, Salud María Jiménez-Zafra1, Arturo Montejo-Ráez1, Manuel Carlos Díaz-Galiano1, Estela Saquete2 1CEATIC / Universidad de Jaén 1{magc, sjzafra, amontejo, mcdiaz}@ujaen.es 2DLSI / Universidad de Alicante 2stela@dlsi.ua.es Abstract This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in Se- mEval 2019, subtask A: SDQC). SDQC ad- dresses the challenge of rumour stance clas- sification as an indirect way of identifying po- tential rumours. Given a tweet with several re- plies, our system classifies each reply into eit- her supporting, denying, questioning or com- menting on the underlying rumours. We ha- ve applied data augmentation, temporal ex- pressions labeling and transfer learning with a four-layer neural classifier. We achieve an ac- curacy of 0.715 with the official run over reply tweets. 1 Introduction Fake news has been identified as “news stories that have no factual basis but are presented as news” (Allcott and Gentzkow, 2017). On a con- ceptual level, we can define hoaxes or rumours as false information spread across social media with the intention to be picked up by traditional news websites (Rubin et al., 2015). Rumours have been around for millennia, as attested by the ancient (and modern) Greek word ‘pheme’, which means rumour or inaccurate information. We have participated in RumourEval (SemEval 2019 Task 7, Subtask A), named SDQC: Determi- ning support for rumours, with the complementary objective of tracking how other sources orient to the accuracy of the rumourous story by looking at the replies to the tweet that presented the rumou- rous statement (Gorrell et al., 2019). These replies are extracted from Twitter and Reddit, but we have only processed the tweet re- plies, obtaining a good score in terms of accuracy. The rest of the paper is organized as follows. Section 2 is a brief overview of the task. Data analysis is shown in section 3. Section 4 describes the neural network architecture. The experiments and results are analyzed in section 5. Finally, con- clusions and proposals for further experimentation are provided in section 6. 2 Related Work The previous edition of RumorEval was organized as part of the SemEval 2017 workshop. Thirteen systems were presented in that edition. Most of the systems presented face this task as a tweet clas- sification task with four categories. Some partici- pants use neural networks such as LSTM (Kochki- na et al., 2017) and CNN (Chen et al., 2017; Gar- cía Lozano et al., 2017), or SVM machine learning algorithm (Wang et al., 2017; Singh et al., 2017), using as main feature word embeddings. Most sys- tems add lexical, syntactic and semantic features to word embeddings. 3 Data analysis The data provided by the organization consist of a set of tweets and replies. Replies can be origina- ted from two different sources: Reddit or Twitter. We have only …

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