Social media graph representation model-based social risk event extraction method

Abstract

The invention discloses a social media graph representation model-based social risk event extraction method. The method comprises the following steps of 1) modeling an event by adopting an HCCG model, defining an entity relationship generation rule, describing event attributes, and performing multi-granularity extraction on the event by utilizing word-level and stream-level contexts; 2) performing similarity calculation by utilizing an information quantity ratio of a maximum common subgraph and a minimum common hypergraph according to an HCCG graph of the extracted event; 3) performing incremental clustering on HCCG through context information of social media, and gradually highlighting event elements of news in a clustering process; and 4) performing event judgment through an HCCG model-based clustering result, and judging whether the clustering result is a true event or not. According to the method, dispersed social media information can be effectively collected; intermediate and final event detection results are expressed in a multi-granularity manner visually by using an entity relationship model; and compared with a conventional social media event extraction method, the social media graph representation model-based social risk event extraction method has better generalization application capability and higher accuracy.

Description

technical field [0001] The invention relates to the field of information query and retrieval, in particular to a method for extracting social risk events based on a social media graph representation model. Background technique [0002] With the rapid increase in the speed of news dissemination in the information society, the influence of public opinion on social risk events has become increasingly prominent, and there are more and more extensive application requirements for real-time monitoring and social analysis of social risk events. In the network environment, the real world and the virtual world have the convenience of mapping each other, and users continue to gather in large numbers on social networking platforms. The real-time data of social media reflects the development trend of events in the real world from the side, which is of great value for the extraction and analysis of social risk events. [0003] The existing research on event perception technology based on...

AI Technical Summary

Problems solved by technology

The above-mentioned characteristics have brought many new challenges to the extraction of social risk events based on social media.

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