Detecting and forecasting misinformation via temporal and geometric propagation patterns

Misinformation takes the form of a false claim under the guise of fact. It is necessary to protect social media against misinformation by means of effective misinformation detection and analysis. To this end, we formulate misinformation propagation as a dynamic graph, then extract the temporal evolution patterns and geometric features of the propagation graph based on Temporal Point Processes (TPPs). TPPs provide the appropriate modelling framework for a list of stochastic, discrete events. In this context, that is a sequence of social user engagements. Furthermore, we forecast the cumulative number of engaged users based on a power law. Such forecasting capabilities can be useful in assessing the threat level of misinformation pieces. By jointly considering the geometric and temporal propagation patterns, our model has achieved comparable performance with state-of-the-art baselines on two well …