Violence Identification in Social Media
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A knowledge-based methodology is proposed for the identification of type and level of violence presented implicitly in shared comments on social media. The work was focused on the semantic processing taking into account the content and handling comments as excerpts of knowledge. Our approach implements similarity measures, conceptual distances, graph theory algorithms, knowledge graphs and disambiguation processes.
The methodology is composed for four stages. In the (1) “knowledge base construction” the types and levels of violence are described as well as the knowledge graphs’ administration. Mechanisms of inclusion and extraction were developed for the knowledge base’s handling and content understanding. The (2) “social media data collection” retrieves comments and maps the social graph’s structure. In the (3) “knowledge processing stage” the comments are transformed to formal representations as extracts of knowledge (graphs). Finally in the (4) “violence domain identification” the comments are classified by their type and level of violence. The evaluation was carried out comparing our methodology with the baselines: (1) a dataset with comments labeled by crowdFlower users, (2) news from social network Twitter, (3) a similar research and (4) typical lexical matching.
KeywordsKnowledge engineering Conceptual similarity DBpedia Topic identification Violence Social media
This work was supported in part by Council for Science, Technology and Innovation, “Cross-ministerial Strategic Innovation Promotion Program (SIP), Big-data and AI-enabled Cyberspace Technologies”. (funding agency: NEDO), JSPS KAKENHI Grant Number JP17H01789 and CONACYT.
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