Social Emotional Security Risks Intervention Framework Based on Social Combustion and Machine Learning

The social combustion theory draws a rational analogy between social public sentiment and combustion phenomena. Based on this theory, this study integrates machine learning and natural language processing to devise an early warning and intervention framework. This framework is engineered to meticulously mine the data streams on social media and digital platforms, thereby enabling the precise monitoring of the trajectory of public sentiment. Predicting the spread of social media content can proactively identify potential risks and effectively monitor combustible materials. Upon identifying a potential for significant spread, the system initiates an analysis of negative emotions to quickly determine the presence of any accelerant. In the event of an emerging public opinion risk, specific control measures are promptly implemented to prevent social unrest. The simulation results of the framework highlight the precision and practicality of the proposed monitoring and control approaches, offering a viable strategy for the governance of social public sentiment.