Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities

Abdali, Sara

doi:10.48550/arxiv.2203.13883

Cited by 7 publications

(10 citation statements)

References 45 publications

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“…Researchers [49] conducted a detailed review of multimodel false news detection methods. ML and databasebased multimodal fake news detection methods are also discussed.…”

Section: Role Of Datasetsmentioning

confidence: 99%

Fake News Detection Datasets: A Review and Research Opportunities

Dhiman,

Kaur,

Hamid

et al. 2024

IJCDS

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The impact of fake news is far-reaching, affecting journalism, the economy, and democracy. In response, there has been a surge in research focused on detecting and combating fake news, resulting in the development of datasets, techniques, and fact-verification methods. One crucial aspect of this effort is the creation of diverse and representative datasets for training and evaluating machine learning models for fake news detection. This review paper examines the available datasets relevant to detecting fake news, with a particular emphasis on those available in the Indian context, where few resources exist. By identifying research opportunities and highlighting existing corpora, this paper aims to assist researchers in improving their fake news detection studies and contributing to more comprehensive research on the topic. To the best of our knowledge, no survey has specifically focused on accessible corpora in the Indian context, making this review a valuable resource for researchers in the field.

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“…Researchers [49] conducted a detailed review of multimodel false news detection methods. ML and databasebased multimodal fake news detection methods are also discussed.…”

Section: Role Of Datasetsmentioning

confidence: 99%

Fake News Detection Datasets: A Review and Research Opportunities

Dhiman,

Kaur,

Hamid

et al. 2024

IJCDS

View full text Add to dashboard Cite

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“… The previous methods can be distilled into the schema illustrated in the above figures [ 18 , 19 , 20 ]. …”

Section: Figurementioning

confidence: 99%

Multi-Modal Fake News Detection via Bridging the Gap between Modals

Liu

Qian

et al. 2023

Entropy

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Multi-modal fake news detection aims to identify fake information through text and corresponding images. The current methods purely combine images and text scenarios by a vanilla attention module but there exists a semantic gap between different scenarios. To address this issue, we introduce an image caption-based method to enhance the model’s ability to capture semantic information from images. Formally, we integrate image description information into the text to bridge the semantic gap between text and images. Moreover, to optimize image utilization and enhance the semantic interaction between images and text, we combine global and object features from the images for the final representation. Finally, we leverage a transformer to fuse the above multi-modal content. We carried out extensive experiments on two publicly available datasets, and the results show that our proposed method significantly improves performance compared to other existing methods.

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“…Given that claims and evidence can be conveyed through different modalities, interest in AFC with images has increased recently (Nakov et al, 2021a;Cao et al, 2020;Alam et al, 2021;Yao et al, 2022;Sharma et al, 2022). Previous tasks focus mainly on detecting manipulated or fake images rather than on evidence-based claim verification (Blaier et al, 2021;Kiela et al, 2020;Alam et al, 2021;Sharma et al, 2022;Abdali, 2022). Whilst manipulated or fake images can be detected using the image only, claim verification requires understanding the claim and evidence jointly.…”

Section: Automated Fact-checking With Imagesmentioning

confidence: 99%

Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking

Akhtar,

Cocarascu,

Simperl

2023

Findings of the Association for Computational Linguistics: EACL 2023

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Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video. While there is increasing interest in using images for AFC, previous works mostly focus on detecting manipulated or fake images. We propose a novel task, chart-based fact-checking, and introduce ChartBERT as the first model for AFC against chart evidence. ChartBERT leverages textual, structural and visual information of charts to determine the veracity of textual claims. For evaluation, we create ChartFC, a new dataset of 15, 886 charts. We systematically evaluate 75 different vision-language (VL) baselines and show that ChartBERT outperforms VL models, achieving 63.8% accuracy. Our results suggest that the task is complex yet feasible, with many challenges ahead.

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Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities

Cited by 7 publications

References 45 publications

Fake News Detection Datasets: A Review and Research Opportunities

Fake News Detection Datasets: A Review and Research Opportunities

Multi-Modal Fake News Detection via Bridging the Gap between Modals

Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking

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