Fake News Detection on Social Media Using A Natural Language Inference Approach

Sadeghi, Fariba; Bidgoly, Amir Jalaly; Amirkhani, Hossein

doi:10.21203/rs.3.rs-107893/v1

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…It is widely used as a benchmark for evaluating Natural Language Understanding (NLU) which plays a key role in many Natural Language Processing tasks such as text summarization, machine translation and sentiment analysis. In addition to serving as a benchmark for NLU, NLI has aided in improving the performance in downstream tasks such as fake news detection (Sadeghi et al, 2022) and fact verification (Martín et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference

Sadat¹,

Caragea²

2022

Preprint

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Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) aims at predicting the relation between a pair of sentences (premise and hypothesis) as entailment, contradiction or semantic independence. Although deep learning models have shown promising performance for NLI in recent years, they rely on large scale expensive human-annotated datasets. Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotation by leveraging unlabeled data for training. However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging. In this paper, we propose a novel way to incorporate unlabeled data in SSL for NLI where we use a conditional language model, BART to generate the hypotheses for the unlabeled sentences (used as premises). Our experiments show that our SSL framework successfully exploits unlabeled data and substantially improves the performance of four NLI datasets in low-resource settings. We release our code at: https:// github.com/msadat3/SSL_for_NLI.

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Section: Introductionmentioning

confidence: 99%

Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference

Sadat¹,

Caragea²

2022

Preprint

View full text Add to dashboard Cite

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A comprehensive survey on machine learning approaches for fake news detection

Alghamdi,

Luo,

Lin

2023

Multimed Tools Appl

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The proliferation of fake news on social media platforms poses significant challenges to society and individuals, leading to negative impacts. As the tactics employed by purveyors of fake news continue to evolve, there is an urgent need for automatic fake news detection (FND) to mitigate its adverse social consequences. Machine learning (ML) and deep learning (DL) techniques have emerged as promising approaches for characterising and identifying fake news content. This paper presents an extensive review of previous studies aiming to understand and combat the dissemination of fake news. The review begins by exploring the definitions of fake news proposed in the literature and delves into related terms and psychological and scientific theories that shed light on why people believe and disseminate fake news. Subsequently, advanced ML and DL techniques for FND are dicussed in detail, focusing on three main feature categories: content-based, context-based, and hybrid-based features. Additionally, the review summarises the characteristics of fake news, commonly used datasets, and the methodologies employed in existing studies. Furthermore, the review identifies the challenges current FND studies encounter and highlights areas that require further investigation in future research. By offering a comprehensive overview of the field, this survey aims to serve as a guide for researchers working on FND, providing valuable insights for developing effective FND mechanisms in the era of technological advancements.

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