Ontology-based Approach for Identifying the Credibility Domain in Social Big Data

TL;DR

This paper presents an ontology-based semantic analysis method for social media data, specifically Twitter, to identify the credibility domain by extracting and representing entities and concepts for improved knowledge management.

Contribution

The paper introduces a novel ontology-based framework for semantic analysis of social media data at entity and domain levels, demonstrated through Twitter case studies.

Findings

Enhanced entity extraction accuracy

Effective domain-specific concept mapping

Improved semantic understanding of social data

Abstract

A challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academic and industry. To address this challenge, semantic analysis of textual data is focused in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyse the social data at two levels i.e. the entity level and the domain level. We have chosen Twitter as a social channel challenge for a purpose of concept proof. Domain knowledge is captured in ontologies which are then used to enrich the semantics of tweets provided with specific semantic conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.