Multilingual Fake News Detection with Satire

Guibon, Gaël; Ermakova, Liana; Seffih, Hosni; Firsov, Anton; Noé-Bienvenu, Guillaume Le

doi:10.1007/978-3-031-24340-0_29

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Reference [27] proposed a multilingual approach to fake news detection that takes into account the presence of satire. The authors use a dataset of news articles in multiple languages and developed a machine learning model to classify them as real or fake.…”

Section: Literature Reviewmentioning

confidence: 99%

“…G. Guibon, L. Ermakova et al [27] A multilingual dataset containing real and fake news articles, including sarcastic articles, collected from various sources SVM 89% A variety of datasets used by different studies was included in the review, which contained texts expressing different stances on various topics, such as politics and climate change MNB 92% P. Goel, R. Jain et al [30] A dataset of tweets containing sarcastic and non-sarcastic messages BLSTM 81%…”

Section: Cnn 90%mentioning

confidence: 99%

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Deep Learning for Sarcasm Identification in News Headlines

et al. 2023

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Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Cnn 90%mentioning

confidence: 99%

Deep Learning for Sarcasm Identification in News Headlines

et al. 2023

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“…Recently, satire has received more attention due to the commonalities with the undesirable phenomenon of misinformation in social media, and particularly with fake news spreading (Golbeck et al, 2018;Levi et al, 2019;Guibon et al, 2019). In order to reduce the exposure to misinformation in social media, publishers of fake news have begun to masquerade as satire sites to avoid being demoted.…”

Section: Machine Learning Based Approachesmentioning

confidence: 99%

Multi-view informed attention-based model for Irony and Satire detection in Spanish variants

Ortega-Bueno

Rosso

Pagola

2022

Knowledge-Based Systems

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“…Another important task addressed in fake news detection is satire detection, with the methods ranging from convolutional neural networks (CNNs) (Guibon et al, 2019) to adversarial training (McHardy et al, 2019) and BERT-based architectures with long-short-term memory (LSTM) (Pandey and Singh, 2022;Liu and Xie, 2021) and CNN (Kaliyar et al, 2021) layers on top.…”

Section: Introductionmentioning

confidence: 99%

SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification

Wu¹,

Razuvayevskaya²,

Freddy³

et al. 2023

Proceedings of the the 17th International Workshop on Semantic Evaluation (SemEval-2023)

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This paper describes our approach for SemEval-2023 Task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the highest mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPET LARGE and an ensemble of XLM-RoBERTa LARGE with adapters and task adaptive pretraining. For Subtask 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the remaining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques. 1

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Multilingual Fake News Detection with Satire

Cited by 8 publications

References 15 publications

Deep Learning for Sarcasm Identification in News Headlines

Deep Learning for Sarcasm Identification in News Headlines

Multi-view informed attention-based model for Irony and Satire detection in Spanish variants

SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification

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