Location Word Embedding: An Approach for Rumor Detection Over Social Internet of Things

With the emergence of online social networks, the dissemination and acquisition of information have experienced dramatic transformations. While social media makes peoples' lives easier, it also speeds up the creation and spread of rumors. Therefore, solving the problem of reliably and effectively recognizing words has become a critical need. The global-local attention network (GLAN)-based rumor detection model has been improved to improve its accuracy. Considering the impact of the positioning relationship among words in text on rumor identification, a new relative positional encoding method is utilized to enhance the original model's local feature extraction module. This method can more precisely extract the semantic and location information of the text in a rumor and aggregate it to provide a better text feature that differentiates rumors from nonrumors. This characteristic is combined with the global element specifying forwarding behavior to increase the effectiveness of word detection. Experimental findings demonstrate that the F 1 value of the suggested method on the Weibo dataset may reach 95%, with a unique detection effect compared to other mainstream detection methods.