相关论文: A Multimodal Adaptive Graph-based Intelligent Clas…
Over the last years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have in different segments of our…
With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models…
Multimodal fake news detection often involves modelling heterogeneous data sources, such as vision and language. Existing detection methods typically rely on fusion effectiveness and cross-modal consistency to model the content,…
Fake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is…
Social media in present times has a significant and growing influence. Fake news being spread on these platforms have a disruptive and damaging impact on our lives. Furthermore, as multimedia content improves the visibility of posts more…
The proliferation of social media platforms such as Twitter, Instagram, and Weibo has significantly enhanced the dissemination of false information. This phenomenon grants both individuals and governmental entities the ability to shape…
Existing deepfake detectors face several challenges in achieving robustness and generalization. One of the primary reasons is their limited ability to extract relevant information from forgery videos, especially in the presence of various…
The easy sharing of multimedia content on social media has caused a rapid dissemination of fake news, which threatens society's stability and security. Therefore, fake news detection has garnered extensive research interest in the field of…
The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal…
The popularity of online social networks has enabled rapid dissemination of information. People now can share and consume information much more rapidly than ever before. However, low-quality and/or accidentally/deliberately fake information…
In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society. Existing multimodal fake news detection methods can generally be classified into knowledge-based and semantic-based…
Social media becomes the central way for people to obtain and utilise news, due to its rapidness and inexpensive value of data distribution. Though, such features of social media platforms also present it a root cause of fake news…
Detection of fake news is crucial to ensure the authenticity of information and maintain the news ecosystems reliability. Recently, there has been an increase in fake news content due to the recent proliferation of social media and fake…
A promising tool for addressing fake news detection is Graph Neural Networks (GNNs). However, most existing GNN-based methods rely on binary classification, categorizing news as either real or fake. Additionally, traditional GNN models use…
Fake News on social media platforms has attracted a lot of attention in recent times, primarily for events related to politics (2016 US Presidential elections), healthcare (infodemic during COVID-19), to name a few. Various methods have…
Fake news detection in social media has become increasingly important due to the rapid proliferation of personal media channels and the consequential dissemination of misleading information. Existing methods, which primarily rely on…
Multimodal deepfake detection (MDD) aims to uncover manipulations across visual, textual, and auditory modalities, thereby reinforcing the reliability of modern information systems. Although large vision-language models (LVLMs) exhibit…
Recent years have witnessed the significant damage caused by various types of fake news. Although considerable effort has been applied to address this issue and much progress has been made on detecting fake news, most existing approaches…
Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist…
The rapid advancement of social media platforms has significantly reduced the cost of information dissemination, yet it has also led to a proliferation of fake news, posing a threat to societal trust and credibility. Most of fake news…