Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity

Khandelwal, Anant

doi:10.1145/3430984.3431007

Cited by 15 publications

(3 citation statements)

References 35 publications

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“…Fu et al [51] addressed the limitation of relying solely on sentiment information for stance detection by introducing an MTL model that integrated opinion-towards classification as another auxiliary task. Other studies have proposed MTL models to jointly address stance detection and rumor veracity prediction, demonstrating their effectiveness [52][53][54][55][56][57][58]. Additional studies proposed MTL models trained on multiple targets, treating detecting stances toward N targets as a set of N tasks [59][60][61].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Stance Detection through Sequential Weighted Multi-task Learning

Alturayeif

Luqman

Ahmed

2023

Preprint

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The exponential growth of user-generated content on social media platforms, online news outlets, and digital communication has necessitated the development of automated tools for analyzing opinions and attitudes expressed in text. Stance detection, a critical task in Natural Language Processing (NLP), aims to identify the underlying perspective or viewpoint of an individual or group towards a specific topic or target. This paper explores the challenges of stance detection, particularly in the context of social media, where brevity, informality, and limited contextual information prevail. While sentiment analysis focuses on explicit sentiment polarity, stance detection classifies the stance or viewpoint of a text towards a target, often of an abstract nature. This study introduces two multi-task learning (MTL) models that integrate sentiment analysis and sarcasm detection tasks to enhance stance detection performance. Four task weighting techniques are proposed and evaluated, and their effectiveness in the MTL models is demonstrated. Extensive evaluations on English and Arabic benchmark datasets highlight the advantages of the proposed models. Among them, the multi-target sequential MTL model stands out with its hierarchical weighting approach, as it achieves state-of-the-art performance. The study underscores the potential of MTL in improving stance detection and provides insights into the interaction between sentiment and stance, while considering the impact of sarcasm.

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Section: Related Workmentioning

confidence: 99%

Enhancing Stance Detection through Sequential Weighted Multi-task Learning

Alturayeif

Luqman

Ahmed

2023

Preprint

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“…To do so, similar benchmark datasets used previously in stance detection are used to balance SemEval-2019 task 7 training dataset for down-sampled classes. The following datasets [22], [23] are used to balance the distribution of tweets after merging Table 2, given that we could not find more examples in query class. − SRQ-2020 dataset (4 Events) [24]: A human-labeled stance dataset for Twitter conversations (both replies and quotes) with over 5,200 stance labels.…”

Section: Dataset Augmentationmentioning

confidence: 99%

“…They achieve Macro F1-Score of 0.64. Khandelwal [22] proposed a multi-task learning framework for jointly-predicting rumor stance and veracity on the dataset released at SemEval-2019 task 7. The proposed method represents the model averaging based on three different architectures with language-based features trained with varying parameters encoder and learning rate.…”

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confidence: 99%

Enhancing prediction of user stance for social networks rumors

Khaled,

ElKorany,

Ezzat

2023

IJECE

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The spread of social media has led to a massive change in the way information is dispersed. It provides organizations and individuals wider opportunities of collaboration. But it also causes an emergence of malicious users and attention seekers to spread rumors and fake news. Understanding user stances in rumor posts is very important to identify the veracity of the underlying content as news becomes viral in a few seconds which can lead to mass panic and confusion. In this paper, different machine learning techniques were utilized to enhance the user stance prediction through a conversation thread towards a given rumor on Twitter platform. We utilized both conversation thread features as well as features related to users who participated in this conversation, in order to predict the users’ stances, in terms of supporting, denying, querying, or commenting (SDQC), towards the source tweet. Furthermore, different datasets for the stance-prediction task were explored to handle the data imbalance problem and data augmentation for minority classes was applied to enhance the results. The proposed framework outperforms the state-of-the-art results with macro F1-score of 0.7233.

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Evidence-Based Early Rumor Verification in Social Media

Haouari

2022

Lecture Notes in Computer Science

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Fine-Tune Longformer for Jointly Predicting Rumor Stance and Veracity

Cited by 15 publications

References 35 publications

Enhancing Stance Detection through Sequential Weighted Multi-task Learning

Enhancing Stance Detection through Sequential Weighted Multi-task Learning

Enhancing prediction of user stance for social networks rumors

Evidence-Based Early Rumor Verification in Social Media

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