AI-based MultiModal to Identify State-linked Social Media Accounts in the Middle East: A Study on Twitter

State-linked propaganda on Social Media poses a new challenge at the geopolitical level for the United States and other countries. The widespread of social media platforms makes it easier for adversaries to spread disinformation, conspiracy theories and social-cyber attacks at a scale that was not possible without such networks. Not only English content on social media represents such a challenge since some of those cyber-mediated attacks are initiated by agents who target other languages. In this paper, we proposed a Multimodal AI approach to detect statelinked accounts on twitter. As opposed to previous efforts, we focus our research on the Middle East and the Anti-USA content on Twitter. We trained AI Multimodal approaches on data with categorical, textual and numerical features. The study utilized experimental Twitter data connected to numerous suspected state-linked accounts on the platform. Additionally, we collected data to represent the negative samples. The findings indicate that the significance of textual modalities and AI language models in identifying state-linked accounts was limited. Our study demonstrated the crucial significance of account metadata and other modalities to detect state-linked propaganda and the associated accounts effectively.