Weibo Rumor Recognition Based on Communication and Stacking Ensemble Learning

Research on the identification of rumors in cyberspace helps to discover social issues that are of concern to the public and are not easily found, and it also can help to purify cyberspace and to maintain social stability. However, the real complexity of rumors makes it difficult for its recognition technology to bridge the semantic gap between qualitative description and quantitative calculation of rumors. Firstly, the existing rumor definitions are mostly qualitative descriptions, so we propose a technical definition of Internet rumors to facilitate quantitative calculations. Secondly, since the feature set used in rumor recognition research is not effective, by combining with communication, we construct a more suitable feature set for rumor recognition. Thirdly, aiming at the problem that traditional classification algorithms are not suitable for complex rumor information recognition, a rumor recognition method based on Stacking ensemble learning is proposed. Our experiment results show that the proposed method has higher accuracy, less algorithm execution time, and better practical application effect.