Sentiment-Aware Fake News Detection on Social Media with Hypergraph Attention Networks

Dong, Diwen; Lin, Fuqiang; Li, Guowei; Liu, Bo

doi:10.1109/smc53654.2022.9945398

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Wasi and Abulaish (2020) proposed a logistic regression-based sentiment classification approach that uses prior domain knowledge extracted from a lexicon and unlabeled domain data. Dong et al (2022) proposed a method for fake news detection based on hypergraph attention networks, which employed two hypergraphs to model news contents and user comments to capture highorder relations between words in a news document and comments with the same sentimental polarity. Haque and Abulaish (2022) proposed a graphbased contextual and semantic learning approach using posts and comments to understand the underlying linguistic patterns that exploited the textual and latent information.…”

Section: Related Workmentioning

confidence: 99%

An Emotion-Enriched and Psycholinguistics Features-Based Approach for Rumor Detection on Online Social Media

Haque,

Abulaish

2023

Proceedings of the 11th International Workshop on Natural Language Processing for Social Media

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Social media platforms and online communication tools, once lauded as sources of information, unity, and global connectivity, are now breeding grounds for the unprecedented spread of false, misleading, and manipulative information. Rumor is one type of such information. It consists of untrue information and deceptive messages and is constructed to manipulate emotions. Consequently, emotions are crucial in determining the veracity of rumors. In this paper, we introduce an emotion-enhanced and psycholinguistic features-based approach for rumors detection on social social media. It entails detecting rumors utilizing lexicons and various linguistic features-based learning approaches, primarily by extracting the psychological association of words with their emotions. Emotional and psycholinguistic features are extracted from both posts and comments to enhance the approach and make rumor detection more effective. Using word-level GloVe embedding, the semantic relationships between a post and its comments and their underlying emotions are preserved. The proposed method is evaluated on the popular PHEME dataset and compared to various baselines and SOTA methods, demonstrating substantially superior performance for rumor detection on social media platforms.

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Section: Related Workmentioning

confidence: 99%

An Emotion-Enriched and Psycholinguistics Features-Based Approach for Rumor Detection on Online Social Media

Haque,

Abulaish

2023

Proceedings of the 11th International Workshop on Natural Language Processing for Social Media

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“…manually. Hence, automatic FND has turned out to be a recent research subject [12]. The FND methods effectively determine false news and are supportive for an administrator to remove fake news from the social media [13].…”

Section: Introductionmentioning

confidence: 99%