video1.0<iframe src="https://www.loom.com/embed/07c22bc59175461d95c6a03390593632" frameborder="0" width="1728" height="1296" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12961728Loomhttps://www.loom.com12961728https://cdn.loom.com/sessions/thumbnails/07c22bc59175461d95c6a03390593632-3eb3de0c02bd1069.gif74.726In this video, I discuss how we leverage Neo4j's graph network logic alongside Tivili and Utori's advanced research tools to identify and fact-check time-sensitive data from Twitter. We analyze connections between posts, highlighting a network of suspicious accounts that exhibit high term frequency and questionable behavior, such as posting every few minutes. I emphasize the importance of flagging these potential bad actors to maintain the integrity of information. Please keep an eye out for similar patterns in your own research and report any suspicious activity.