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An exploration of the motivations of catfish perpetrators and the emotions and feelings expressed by catfish victims using automated linguistic analysis and thematic analysis.
- Source :
- Discover Data; 5/28/2024, Vol. 2 Issue 1, p1-12, 12p
- Publication Year :
- 2024
-
Abstract
- Catfishing is a form of online deception where an individual presents themselves as an identity that is not their own. The study reported in this article explored the motivations for catfish perpetrators and the impacts on those who had been catfished in terms of the emotions and feelings expressed by victims. Data was collected using the crowd-sourced question and answer website Quora [1] and analysis was conducted on a pre-existing corpus of data which contained participant's answers to questions on catfishing. An automated linguistic analysis using Linguistic Inquiry and Word Count (LIWC-2022 [2]) and a thematic analysis (Braun and Clarke in Qual Res Psychol 3:77–101, 2006 [38]) were conducted on participant's descriptions of their perceptions of the motivations of catfishes and their catfishing experiences. The thematic analysis indicated that the motivations of catfish perpetrators can be linked to entertainment, emulating an ideal self, desiring meaningful interaction, and financial gain. Six emotions and feelings emerged from the accounts of catfishing victims: suspicion, love, depression, anger, embarrassment, and stupidity. These findings contribute to an understanding of what motivates individuals to catfish and suggests further research to explore specific emotions and feelings that catfish victims experience. The LIWC analysis and language style matching analysis showed that the data collected was mostly personal to each individual and that there were similarities in how victims write about their experiences. Therefore, we suggest that LIWC has promise as a method of providing added context to qualitative data analysis methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 27316955
- Volume :
- 2
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Discover Data
- Publication Type :
- Academic Journal
- Accession number :
- 177538153
- Full Text :
- https://doi.org/10.1007/s44248-024-00011-5