Zero-trust management using AI: Untrusting the trusted accounts in social media

Can we trust verified accounts on social media? With the recent exponential inflation of social media in every aspect, tremendous efforts have been directed to comprehend, analyze, relate, predict, secure, privacy-preserve, and validate its content. This included natural language processing, sentiment analysis, opinion mining, fake accounts/news detection, graph mining, recommendations, anonymization, …, etc. However, assessing the trust level of the disseminated content was never addressed. Social media users are left to deal with the content of the verified accounts and decide for themselves whether they are true or fake. This raises a dire concern: how can we evaluate the trust level of the content posed by the verified accounts we follow? In this paper, we investigate the challenges of zero-trust management for content in social media. We draw the community’s attention to go beyond fake accounts/content detection and explore trust assessment of content for the legitimate account, rather than solely checking whether it is verified or not. In this regard, we derive a set of open challenges with AI-based solutions for further investigation to maintain a trustworthy social media ecosystem with trusted content.