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.
