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

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 (BERT) and Text Convolutional Neural… 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

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… More >

Muhammad Usman Ghani Khan¹, Abid Mehmood², Mourad Elhadef², Shehzad Ashraf Chaudhry^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2225-2249, 2023, DOI:10.32604/cmc.2023.038303

Abstract The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that… 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

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 misinformation from social media data… 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

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 the Fake News Classification using… 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

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 ML approaches were applied, and… More >

Zhiping Liang^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4519-4534, 2023, DOI:10.32604/cmc.2023.037035

Abstract In view of the various adverse effects, fake news detection has become an extremely important task. So far, many detection methods have been proposed, but these methods still have some limitations. For example, only two independently encoded unimodal information are concatenated together, but not integrated with multimodal information to complete the complementary information, and to obtain the correlated information in the news content. This simple fusion approach may lead to the omission of some information and bring some interference to the model. To solve the above problems, this paper proposes the Fake News Detection model based on BLIP (FNDB). First,… More >

Hala J. Alshahrani¹, Abdulkhaleq Q. A. Hassan², Khaled Tarmissi³, Amal S. Mehanna⁴, Abdelwahed Motwakel^5,*, Ishfaq Yaseen⁵, Amgad Atta Abdelmageed⁵, Mohamed I. Eldesouki⁶

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4255-4272, 2023, DOI:10.32604/cmc.2023.034821

Abstract Nowadays, the usage of social media platforms is rapidly increasing, and rumours or false information are also rising, especially among Arab nations. This false information is harmful to society and individuals. Blocking and detecting the spread of fake news in Arabic becomes critical. Several artificial intelligence (AI) methods, including contemporary transformer techniques, BERT, were used to detect fake news. Thus, fake news in Arabic is identified by utilizing AI approaches. This article develops a new hunter-prey optimization with hybrid deep learning-based fake news detection (HPOHDL-FND) model on the Arabic corpus. The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform… More >

Muhammad Asfand-e-Yar^1,*, Qadeer Hashir^1,*, Syed Hassan Tanvir¹, Wajeeha Khalil²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2921-2938, 2023, DOI:10.32604/cmc.2023.034741

Abstract The growth of the internet and technology has had a significant effect on social interactions. False information has become an important research topic due to the massive amount of misinformed content on social networks. It is very easy for any user to spread misinformation through the media. Therefore, misinformation is a problem for professionals, organizers, and societies. Hence, it is essential to observe the credibility and validity of the News articles being shared on social media. The core challenge is to distinguish the difference between accurate and false information. Recent studies focus on News article content, such as News titles… More >

Ashit Kumar Dutta^1,*, Basit Qureshi², Yasser Albagory³, Majed Alsanea⁴, Manal Al Faraj¹, Abdul Rahaman Wahab Sait⁵

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2395-2409, 2023, DOI:10.32604/csse.2023.027502

Abstract Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is… More >