Discourse Recognition on Twitter Using Regulatory Focus Theory (RFC) and Text Mining Techniques

The present  survey aims to extract the patterns of content developed by Twitter users by using the regulatory focus theory (RFT).

This study was conducted based on text mining techniques. The statistical population consisted of the texts published on Twitter from July 2020 to July 2021, and the data were collected by searching special keywords.

The results of the article indicate that a total of 33,192 tweets were extracted as the dataset, 1,967 of which were selected for labeling by RFC.

The authors of the research conclude that the four main discourses extracted from the analyzed tweets were Possession, Non- Possession, abstinence, and exposure. The results revealed the higher frequency of codes related to the discourse “exposure”, indicating the semantic coherence of “exposure” both on Twitter and the whole society. This semantic coherence can direct and mobilize public opinion to call for cultural reforms.