Open Access

ARTICLE

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

Lifang Fu¹, Huanxin Peng^2,*, Changjin Ma², Yuhan Liu²

1 College of Arts and Sciences, Northeast Agricultural University, Harbin, 150030, China
2 College of Engineering, Northeast Agricultural University, Harbin, 150030, China

* Corresponding Author: Huanxin Peng. Email:

(This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)

Computers, Materials & Continua 2024, 78(3), 4399-4416. https://doi.org/10.32604/cmc.2024.047053

Received 23 October 2023; Accepted 24 January 2024; Issue published 26 March 2024

Abstract

In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation. To reduce the irrelevant social structure’s information interference, we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features. A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information. In addition, TD-MMC employs a new multi-model loss to improve the model’s generalization ability. Extensive experiments have been conducted on two public real-world English and Chinese datasets, and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

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Keywords

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