Poster: Semantic Clustering in Credible Human Sensed Event Detection

Twitter is one of the most popular social media platforms, and a widely used data channel for the propagation of information. Since too many open end users access and use the powerful channel for information propagation, it is becoming increasingly difficult to separate reliable information from the overwhelming pool of information. With the advent of social media generated "fake news" and with their growing influence on the society, the issue of detecting authentic information gains utmost importance. The purpose of our work is to measure the reliability or correctness of the information that is being propagated using Twitter. However, to measure the reliability it is important to preprocess the tweets and to find the similar events. For doing this, an effective clustering method is required which will measure the similarity between the tweets using both semantic and syntactic similarity. We also propose an efficient way to compute the credibility of the sources and how information propagates around the network.