The Tinder Swindler : analyzing public sentiments of romance fraud using machine learning and artificial intelligence

The Tinder Swindler is a Netflix documentary that documents the stories of three women who were swindled of their life savings by a man, Simon Leviev ("Leviev"), who presented himself as the son of a wealthy diamond oligarch. The paper's main objective is to understand the sentiments shared by the public on the microblogging website Twitter on Leviev's actions towards victims. This paper is anchored with Ervin Goffman's (1959) impression management (IM) theory to provide context and theoretical insights into the analysis. IM theory offers a cultural framework to address and understand how people mold their behaviour in social contexts to alter their audience's perceptions of themselves. Data for this project came from Twitter with the hashtag #tinderswindler. In total, 47,876 tweets were collected between February 1st and March 18th. The findings reveal that most people who commented on Twitter had high neutral and positive but low negative sentiments about Leviev's actions in the Tinder Swindler. The neutral findings indicate that a significant proportion of individuals did not support Leviev's action, nor do they support the women who were defrauded. The positive sentiments reveal that Leviev's favourable self-presentation successfully persuaded people that his front-stage performance was real and that the performance resonated with Twitter users. The higher subjectivity for the negative sentiments means that the texts contain more factual information than personal opinion.