A Graph-Theoretic Embedding-Based Approach for Rumor Detection in Twitter

In this paper, we present a graph-theoretic embedding-based approach to model user-generated contents in online social media for rumor detection. Starting with a small set of seed rumor words of four different lexical categories, we generate a words co-occurrence graph and apply centrality-based analysis to identify prominent rumor characterizing words. Thereafter, word embedding is applied to represent each category of seed words as numeric vectors and to train three different classification models for rumor detection. The performance of the proposed approach is empirically evaluated over two versions of a benchmark dataset. The proposed approach is also compared with one of the state-of-the-art methods for rumor detection and performs significantly better.