Towards the Detection of Fake News on Social Networks Contributing to the Improvement of Trust and Transparency in Recommendation Systems: Trends and Challenges

Abstract

In the age of the digital revolution and the widespread usage of social networks, the modalities of information consumption and production were disrupted by the shift to instantaneous transmission. Sometimes the scoop and exclusivity are just for a few minutes. Information spreads like wildfire throughout the world, with little regard for context or critical thought, resulting in the proliferation of fake news. As a result, it is preferable to have a system that allows consumers to obtain balanced news information. Some researchers attempted to detect false and authentic news using tagged data and had some success. Online social groups propagate digital false news or fake news material in the form of shares, reshares, and repostings. This work aims to detect fake news forms dispatched on social networks to enhance the quality of trust and transparency in the social network recommendation system. It provides an overview of traditional techniques used to detect fake news and modern approaches used for multiclassification using unlabeled data. Many researchers are focusing on detecting fake news, but fewer works highlight this detection’s role in improving the quality of trust in social network recommendation systems. In this research paper, we take an improved approach to assisting users in deciding which information to read by alerting them about the degree of inaccuracy of the news items they are seeing and recommending the many types of fake news that the material represents.

A Multi-tier Model and Filtering Approach to Detect Fake News Using Machine Learning Algorithms

“…Various research work on fake news detection, such as [11], [16], [17], and [18], has been done in recent years. Machine learning assists researchers in determining fake news using features defined by researchers.…”

“…Work by [16] proposed the trust network construction recommendation step to detect fake news and the multiclassification approach using unlabeled data. The datasets were from the News Dataset from GitHub [19] and the Getting Real about Fake News Dataset from Kaggle [20].…”

Toward Trust-Based Recommender Systems for Open Data: A Literature Review

“…For example, Xue et al [41] suggested an iterative methodology for calculating the total trustworthiness of all reviewers in a system and using it to predict the possibility of someone being a review spammer (e.g., people writing fake reviews to either promote or demote certain products or services). Similarly, Stitini et al [42] proposed innovative studies on improving trust and transparency in recommendation systems by detecting fake news on social networks.…”

Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research

“…Therefore, the end users find it hard to trust a system that is not transparent and understandable to them (Stitini et al, 2022;He et al, 2016). This situation resulted in a growing demand for explainability in recommendation systems (AI-based content suggestions) (Gerlings et al, 2020;Gunning et al, 2019).…”