SEMI-FND: Stacked ensemble based multimodal inferencing framework for faster fake news detection

Singh, Prabhav; Srivastava, Ridam; Rana, K.P.S.; Kumar, V.

doi:10.1016/j.eswa.2022.119302

Cited by 17 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these models often require complex parameter settings. To address this, Singh et al [35] designed a stacked framework. For text processing, they employed BERT and ELECTRA, while for images, they utilized the efficient NasNet Mobile.…”

Section: Multimodal Detection Methodsmentioning

confidence: 99%

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

Wang,

Zheng,

Yao

et al. 2023

Entropy

View full text Add to dashboard Cite

In the rapidly evolving information era, the dissemination of information has become swifter and more extensive. Fake news, in particular, spreads more rapidly and is produced at a lower cost compared to genuine news. While researchers have developed various methods for the automated detection of fake news, challenges such as the presence of multimodal information in news articles or insufficient multimodal data have hindered their detection efficacy. To address these challenges, we introduce a novel multimodal fusion model (TLFND) based on a three-level feature matching distance approach for fake news detection. TLFND comprises four core components: a two-level text feature extraction module, an image extraction and fusion module, a three-level feature matching score module, and a multimodal integrated recognition module. This model seamlessly combines two levels of text information (headline and body) and image data (multi-image fusion) within news articles. Notably, we introduce the Chebyshev distance metric for the first time to calculate matching scores among these three modalities. Additionally, we design an adaptive evolutionary algorithm for computing the loss functions of the four model components. Our comprehensive experiments on three real-world publicly available datasets validate the effectiveness of our proposed model, with remarkable improvements demonstrated across all four evaluation metrics for the PolitiFact, GossipCop, and Twitter datasets, resulting in an F1 score increase of 6.6%, 2.9%, and 2.3%, respectively.

show abstract

Section: Multimodal Detection Methodsmentioning

confidence: 99%

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

Wang,

Zheng,

Yao

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…a series of powerful, multi-modal AI models" [45]. Some multi-modal models, such as UniMSE [46] for sentiment analysis and SEMI-FND [47] for Fake News Detection, are already emerging as bestin-class as shown in Table 10 below.…”

Section: B Multi-modalmentioning

confidence: 99%

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Fields,

Chovanec,

Madiraju

2024

IEEE Access

View full text Add to dashboard Cite

Text classification is a basic task in natural language processing (NLP) with applications from sentiment analysis to question-answering with chat bots. In recent years, transformer-based models have emerged as the prevailing framework in NLP, demonstrating excellent results across many benchmarks. This paper recommends an expanded taxonomy of applications and provides a review of the performance of different models across these applications. The use of traditional research techniques plus co-citation and bibliographic coupling provides a comprehensive view of the current and past research in this area. The study begins by providing an overview of the history of transformer-based models with an emphasis on recent large language models (LLM). Next, uni-modal (text only) inputs and the emerging area of multi-modal classification are discussed to provide a comparison of current and emerging research in this area. Gaps are highlighted in the use of multi-modal text/numeric/columnar data and recommendations for future research are provided. Finally, the length of text input variables (tokens) is reviewed to explore the evolution from short-text to longer document applications. Furthermore, the accuracy on 358 datasets across 20 applications is reviewed and unexpected results emerge which show that LLMs are not always the most accurate or least expensive option. In addition to model performance, the safety implications of transformer-based models are reviewed, and a summary of issues related to ethics, bias, social implications, and copyright are explored.

show abstract

“…The task involved identifying whether the multimedia items accompanying a tweet reflect reality in the way purported by the tweet. The dataset consists of 17,000 distinct tweets with corresponding photos gathered from some wellknown events or news items [48]. The dataset is split into a training set (consisting of 9,000 tweets) and a test set (2,000 tweets), each with its unique set of 6,000 genuine and 9,000 fake news tweets, respectively.…”

Section: A Fake News Datasetmentioning

confidence: 99%

Combating Fake News on Social Media: A Fusion Approach for Improved Detection and Interpretability

Zamil,

Charkari

2024

IEEE Access

View full text Add to dashboard Cite

The proliferation of fake news on social media prompted research groups to develop statistical and learning methods to combat this menace. Deep learning techniques did not model and improve in terms of adopting multi-transformer topologies, enhancing interpretability, and coping with uncertainty. This article suggests a fusion strategy to create a more reliable fake news detection (FND) model by fusing text and image features. The different combinations of information in single and multi-modalities have been investigated to find optimal conditions. In this paper, we have employed pre-trained models of Electra and XLnet for text feature learning. Furthermore, ELA has been used to highlight the modified image features and EfficientNetB0 for image learning. To enhance the interpretability of the proposed model, the superpixels contributing to its interpretability were identified using the Local Interpretable Model-agnostic Explanations (LIME). Three well-known datasets (Weibo, MediaEval, and CASIA) have been used in this study. The results show that employing ELA and LIME in conjunction with the fusion of text and image features provides a solid and understandable solution to the FND issue in social media compared to other techniques.INDEX TERMS Social media news, fake news detection, Error Level Analysis, efficientNetB0, LIME.

show abstract

SEMI-FND: Stacked ensemble based multimodal inferencing framework for faster fake news detection

Cited by 17 publications

References 29 publications

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

TLFND: A Multimodal Fusion Model Based on Three-Level Feature Matching Distance for Fake News Detection

A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?

Combating Fake News on Social Media: A Fusion Approach for Improved Detection and Interpretability

Contact Info

Product

Resources

About