  Introduction: This article introduces DisTrack, a methodology and a tool
developed for tracking and analyzing misinformation within Online Social
Networks (OSNs). DisTrack is designed to combat the spread of misinformation
through a combination of Natural Language Processing (NLP) Social Network
Analysis (SNA) and graph visualization. The primary goal is to detect
misinformation, track its propagation, identify its sources, and assess the
influence of various actors within the network.
  Methods: DisTrack's architecture incorporates a variety of methodologies
including keyword search, semantic similarity assessments, and graph generation
techniques. These methods collectively facilitate the monitoring of
misinformation, the categorization of content based on alignment with known
false claims, and the visualization of dissemination cascades through detailed
graphs. The tool is tailored to capture and analyze the dynamic nature of
misinformation spread in digital environments.
  Results: The effectiveness of DisTrack is demonstrated through three case
studies focused on different themes: discredit/hate speech, anti-vaccine
misinformation, and false narratives about the Russia-Ukraine conflict. These
studies show DisTrack's capabilities in distinguishing posts that propagate
falsehoods from those that counteract them, and tracing the evolution of
misinformation from its inception.
  Conclusions: The research confirms that DisTrack is a valuable tool in the
field of misinformation analysis. It effectively distinguishes between
different types of misinformation and traces their development over time. By
providing a comprehensive approach to understanding and combating
misinformation in digital spaces, DisTrack proves to be an essential asset for
researchers and practitioners working to mitigate the impact of false
information in online social environments.
