Jianxiang Cao¹, Jinyang Wu¹, Wenqian Shang^1,*, Chunhua Wang¹, Kang Song¹, Tong Yi^2,*, Jiajun Cai¹, Haibin Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2659-2675, 2025, DOI:10.32604/cmc.2025.060025 - 16 April 2025

Abstract With the rapid growth of social media, the spread of fake news has become a growing problem, misleading the public and causing significant harm. As social media content is often composed of both images and text, the use of multimodal approaches for fake news detection has gained significant attention. To solve the problems existing in previous multi-modal fake news detection algorithms, such as insufficient feature extraction and insufficient use of semantic relations between modes, this paper proposes the MFFFND-Co (Multimodal Feature Fusion Fake News Detection with Co-Attention Block) model. First, the model deeply explores the More >

Yuanqing Li¹, Mengyao Dai¹, Sanfeng Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 309-333, 2025, DOI:10.32604/cmc.2025.060455 - 26 March 2025

Abstract Social media has significantly accelerated the rapid dissemination of information, but it also boosts propagation of fake news, posing serious challenges to public awareness and social stability. In real-world contexts, the volume of trustable information far exceeds that of rumors, resulting in a class imbalance that leads models to prioritize the majority class during training. This focus diminishes the model’s ability to recognize minority class samples. Furthermore, models may experience overfitting when encountering these minority samples, further compromising their generalization capabilities. Unlike node-level classification tasks, fake news detection in social networks operates on graph-level samples,… More >

Muhammad Ali Ilyas¹, Abdul Rehman², Assad Abbas¹, Dongsun Kim^3,*, Muhammad Tahir Naseem^4,*, Nasro Min Allah⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4525-4549, 2024, DOI:10.32604/cmc.2024.056291 - 19 December 2024

Abstract In an era dominated by information dissemination through various channels like newspapers, social media, radio, and television, the surge in content production, especially on social platforms, has amplified the challenge of distinguishing between truthful and deceptive information. Fake news, a prevalent issue, particularly on social media, complicates the assessment of news credibility. The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources, creating confusion and polarizing opinions. As the volume of information grows, individuals increasingly struggle to discern credible content from false narratives, leading to widespread… More >

Wen Jiang^1,2, Mingshu Zhang^1,2,*, Xu'an Wang^1,3, Wei Bin^1,2, Xiong Zhang^1,2, Kelan Ren^1,2, Facheng Yan^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2161-2179, 2024, DOI:10.32604/cmc.2024.053762 - 15 August 2024

Abstract With the rapid spread of Internet information and the spread of fake news, the detection of fake news becomes more and more important. Traditional detection methods often rely on a single emotional or semantic feature to identify fake news, but these methods have limitations when dealing with news in specific domains. In order to solve the problem of weak feature correlation between data from different domains, a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed. This method makes full use of the attention mechanism, grasps the correlation between different… More >

Fangfang Shan^1,2,*, Mengyao Liu^1,2, Menghan Zhang^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1521-1542, 2024, DOI:10.32604/cmc.2024.053937 - 18 July 2024

Abstract Social media has become increasingly significant in modern society, but it has also turned into a breeding ground for the propagation of misleading information, potentially causing a detrimental impact on public opinion and daily life. Compared to pure text content, multmodal content significantly increases the visibility and share ability of posts. This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection. To effectively address the critical challenge of accurately detecting fake news on social media, this paper proposes a fake… More >

Fangfang Shan^1,2,*, Huifang Sun^1,2, Mengyi Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 581-605, 2024, DOI:10.32604/cmc.2024.046202 - 25 April 2024

Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers… More >

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

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053 - 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… More >

Ammar Saeed^1,*, Eesa Al Solami²

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2079-2096, 2023, DOI:10.32604/cmc.2023.030551 - 29 November 2023

Abstract The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms. Some social media sources contribute to the propagation of fake news that has no real validity, but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity. To sustain the value of social media, such stories must be distinguished from the true ones. As a result, an automated system is required to save time and money. The classification of fake news and… More >

Asma Qaiser¹, Saman Hina¹, Abdul Karim Kazi^1,*, Saad Ahmed², Raheela Asif³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 73-90, 2023, DOI:10.32604/iasc.2023.036784 - 29 April 2023

Abstract In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of More >

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Khaled Tarmissi³, Ayman Yafoz⁴, Amal S. Mehanna⁵, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹, Mohamed I. Eldesouki⁶

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3303-3319, 2023, DOI:10.32604/csse.2023.034823 - 03 April 2023

Abstract The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the… More >