Fractures and Cohesion: Using Systemic Functional Linguistics to Detect and Analyse Hate Speech in an Online Environment

Language acts as the lynchpin of cohesion maintaining electronic conversations across social networking sites. Analysis of that cohesion facilitates the detection of linguistic patterns that initiate and compound hate speech in online environments. This research reports on analysis of hate speech in videos and asynchronous conversation using Systemic Functional Linguistics (SFL) within social networking site. Focussing on the architecture of language and the influence of social context, SFL facilitates the analysis of language in its temporal and contextual use. In applying SFL to the chosen corpus of texts the research team are building reference dictionaries of offensive words and reference catalogues for the clausal structures in which these words are used. The team is exploring the detection of and analysis of hate speech across conversation, that is across texts within social networking sites (SNS). In accumulating data that allows the expansion of dictionaries and clausal catalogues, the team is enabling the building of an automated alert system that scans texts as they develop independently and in their engagement with other texts across time. Overall, this paper outlines the application of SFL to texts accrued from SNS that exhibit aspects of hate speech associated with dehumanisation, details the analysis of visualisations of hate speech within developing texts and demonstrates the building of an automated alert system using SFL to detect hate speech across texts