Prediction and Characterization of Social Media Communication Effects of Emergencies with Multimodal Information

This paper investigates the communication effects on social media, and their influencing factors. A emergency events dataset spanning the period from 2019 to 2023 is constructed, comprising a large volume of textual and image data obtained through web crawling. The communication effects of emergency social media posts during various emergent events are analyzed using a comprehensive paradigm, with the breadth and depth of dissemination measured by the sum of likes and comments, as well as the number of reposts. LightGBM is employed as the classifier, and multidimensional features incorporating visual and textual dimensions are constructed. Experimental results highlight the significant impact of the image modality on dissemination effects, particularly emphasizing the importance of features such as HSV and image content categories. Additionally, the number of followers of the original poster is identified as a crucial factor influencing dissemination effects. The experimental results show that the research method based on feature engineering and machine learning can effectively predict the propagation effect of microblog, and the LightGBM algorithm performs best. The study further found that in the comparison of modal effects, the graphical multimodal has better performance.