Graph-Based Rumor Detection on social media Using Posts and Reactions

Abstract

In this article, researchers deliver a novel method that makes use of graph-based contextual and semantic learning to detect rumours on online social media. The basic premise is that social media entities are connected, and if an event takes place, then comparable stories or user responses with shared interests get spread across the network. The method that is being offered makes use of tweets and people's replies to them in order to comprehend the fundamental interaction patterns and make use of the textual and hidden information. The primary emphasis of this effort is developing a reliable graph-based analyzer that can identify rumors spread on social media. The modeling of textual data as a words cooccurrence graph results in the production of two prominent groups of words: significant words and bridge words. Using these words as building pieces, contextual patterns for rumor detection may be constructed and detected using node-level statistical measurements. The identification of unpleasant feelings and inquisitive components in the responses further enriches the contextual patterns. When all is said and done, the patterns are rated, and only the top k check-worthy patterns are selected for feature creation. We employ a word-level Glove embedding that has been trained using a Twitter dataset in order to ensure that the semantic relations are maintained. The suggested method is assessed using the PHEME dataset, which is open to the public, and contrasted with a variety of baselines as well as our suggested approaches. The results of the experiments are encouraging, and the strategy that was suggested seems to be helpful for rumor identification on social media platforms online.