Deep learning for misinformation detection on online social networks: a survey and new perspectives

Abstract

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

Aging in an “infodemic”: The role of cognition, affect, and experience on news veracity detection during the COVID-19 pandemic

“…Training interventions could entail fact-checking strategies and nudging to attend to diagnostic features (e.g., news source credibility; Pehlivanoglu et al, under review) and/or to consider news veracity during news consumption. Again, natural-language and imagebased machine-learning (deep learning) feature extraction, as already in place for fake image detection (e.g., deepfake; Islam, Liu, Wang, & Xu, 2020;Westerlund, 2019) could be particularly informative for designing effective decision-supportive intervention. Additionally, "inoculation" against false information (Van Bavel et al, 2020) could be achieved by ensuring that older adults are equipped with solid base knowledge before they encounter fake news that they may be particularly susceptible to.…”

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic

“…(Islam et al , 2020) present a state-of-the-art systematic review of the existing problem solutions and validation of Minimal Infectious Dose (MID) (Fake news detection) in online social networks based on various deep learning techniques. To identify the latest and future trends of MID research, they analyze the key strengths and limitations of the various existing techniques and describe state-of-the-art deep learning as an emerging technique on massive social network data.…”

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

“…It can rather be seen as an integral approach to make decision, combining visualization with DL models, human factors and data analysis [144]. Both visualization and DL helps to get deeper insights from multivariate sectors and enhance further development of human-centered tools using advanced technologies [51]. Nowadays, DL models are most useful for decision making as well as providing as many accurate predictions as possible [42].…”