Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Sentiment analysis has become an important task in natural language processing because it
is used in many different areas. This paper gives a detailed review of sentiment analysis …

Previous researches on multimedia fake news detection include a series of complex feature
extraction and fusion networks to gather useful information from the news. However, how …

To manage the rumors in social media to reduce the harm of rumors in society. Many studies
used methods of deep learning to detect rumors in open networks. To comprehensively sort …

Learning from multiple modalities has recently attracted increasing attention in many tasks.
However, deep learning-based multi-modal learning cannot guarantee good generalization …

Social media misinformation harms individuals and societies and is potentialized by fast-
growing multi-modal content (ie, texts and images), which accounts for higher “credibility” …

Next Point-of-Interest (POI) recommendation seeks to recommend locations that users are
most likely to visit next based on their historical trajectories, providing both users and service …

Rumor detection has been a hot issue in current online public opinion governance. Most of
the recent rumor detection methods tend to be supervised learning, while research about …

To reduce the negative impacts of rumors on the real world, rumor detection on social
networks has practical significance. Currently, the research on Chinese rumor detection is …