Contrastive Language Adaptation for Cross-Lingual Stance Detection

Mohtarami, Mitra; Glass, James; Nakov, Preslav

doi:10.18653/v1/d19-1452

Cited by 30 publications

(23 citation statements)

References 35 publications

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“…Furthermore, we would like to investigate approaches for improving stance detection by extracting the parts of the documents that contain the main stance rather than truncating the documents after the first 512 tokens. Finally, we plan to experiment with cross-domain (Hardalov et al, 2021a) and crosslanguage approaches (Mohtarami et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking

Alhindi¹,

Alabdulkarim²,

Alshehri³

et al. 2021

Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

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With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages. One task of interest is claim veracity prediction, which can be addressed using stance detection with respect to relevant documents retrieved online. To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claimarticle pairs from a diverse set of sources comprising three fact-checking websites and one news website. AraStance covers false and true claims from multiple domains (e.g., politics, sports, health) and several Arab countries, and it is well-balanced between related and unrelated documents with respect to the claims. We benchmark AraStance, along with two other stance detection datasets, using a number of BERT-based models. Our best model achieves an accuracy of 85% and a macro F1 score of 78%, which leaves room for improvement and reflects the challenging nature of AraStance and the task of stance detection in general.

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Section: Discussionmentioning

confidence: 99%

AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking

Alhindi¹,

Alabdulkarim²,

Alshehri³

et al. 2021

Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

Self Cite

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“…Recent efforts in multilingual stance classification have also published datasets including German, French and Italian (Mohtarami et al, 2019;Vamvas & Sennrich, 2020), and English, French, Italian, Spanish and Catalan (Lai et al, 2020), but are still limited in terms of the time frame covered. Longitudinal datasets annotated for stance would enable furthering research in this direction by looking into the temporal dynamics of stance.…”

Section: Core Challengesmentioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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“…A study [31] proposed a set of 10 hand-crafted cross-lingual and cross-platform features for rumour detection by capturing the similarity and agreement between online posts from different social media platforms. Another study [24] introduced a contrastive learningbased model for cross-lingual stance detection using memory networks. Different to these studies, we specifically focus on how to transfer learned knowledge from a source language to a target language for automatic rumour detection.…”

Section: Related Workmentioning

confidence: 99%

Rumour Detection via Zero-shot Cross-lingual Transfer Learning

Lin¹,

Zhang²,

Lau³

2021

Preprint

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Most rumour detection models for social media are designed for one specific language (mostly English). There are over 40 languages on Twitter and most languages lack annotated resources to build rumour detection models. In this paper we propose a zero-shot cross-lingual transfer learning framework that can adapt a rumour detection model trained for a source language to another target language. Our framework utilises pretrained multilingual language models (e.g. multilingual BERT) and a self-training loop to iteratively bootstrap the creation of "silver labels" in the target language to adapt the model from the source language to the target language. We evaluate our methodology on English and Chinese rumour datasets and demonstrate that our model substantially outperforms competitive benchmarks in both source and target language rumour detection.

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Contrastive Language Adaptation for Cross-Lingual Stance Detection

Cited by 30 publications

References 35 publications

AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking

AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking

Capturing stance dynamics in social media: open challenges and research directions

Rumour Detection via Zero-shot Cross-lingual Transfer Learning

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