Over the past few years, we have observed different media outlets' attempts\nto shift public opinion by framing information to support a narrative that\nfacilitate their goals. Malicious users referred to as "pathogenic social\nmedia" (PSM) accounts are more likely to amplify this phenomena by spreading\nmisinformation to viral proportions. Understanding the spread of misinformation\nfrom account-level perspective is thus a pressing problem. In this work, we aim\nto present a feature-driven approach to detect PSM accounts in social media.\nInspired by the literature, we set out to assess PSMs from three broad\nperspectives: (1) user-related information (e.g., user activity, profile\ncharacteristics), (2) source-related information (i.e., information linked via\nURLs shared by users) and (3) content-related information (e.g., tweets\ncharacteristics). For the user-related information, we investigate malicious\nsignals using causality analysis (i.e., if user is frequently a cause of viral\ncascades) and profile characteristics (e.g., number of followers, etc.). For\nthe source-related information, we explore various malicious properties linked\nto URLs (e.g., URL address, content of the associated website, etc.). Finally,\nfor the content-related information, we examine attributes (e.g., number of\nhashtags, suspicious hashtags, etc.) from tweets posted by users. Experiments\non real-world Twitter data from different countries demonstrate the\neffectiveness of the proposed approach in identifying PSM users.\n