Dark Triad detection and analysis from social media text

Morsalina; Fairoz, Fariha; Anjum, Fariha

dc.contributor.author	Morsalina
dc.contributor.author	Fairoz, Fariha
dc.contributor.author	Anjum, Fariha
dc.date.accessioned	2024-08-29T06:57:53Z
dc.date.available	2024-08-29T06:57:53Z
dc.date.issued	2023-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/2142
dc.description	Supervised by Mr. Mohsinul Kabir, Assistant Professor,	en_US
dc.description.abstract	The increased use of social media platform usage such as Facebook has given an oppor tunity to express one’s thoughts and ideas to everyone. Social media posts can be used as a medium for determining different psychological traits, such as dark triad character istics. In our thesis, we used peoples’ social media posts, and using those posts we tried to detect presence of dark triad traits based on the hand crafted features extracted from their posts. We also have shown, the usage of code mixing as a effective feature. We have used traditional machine learning models, ensemble models as well as transformer based language models to find the best possible outcome. For finding linguistic fea tures analysis we have used Polarity of text, subjectivity, lexical density, word tokens, word count, avg word length, word freq dist, stopword count, part of speech, Topic seg mentation and many more. We then compared the performance of all the models.For all the traits, ensemble of 4 traditional machine learning models outperformed all other models.The highest accuracy achieved in narcissist detection was 96.36% , for Machi avelli it was 98.27%, and for psychopathy it was 99.62%.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Social Media; Mental Health; dark triad; narcissist; Machiavelli; code switching; sentiment; hand crafted feature; psychopath; traits	en_US
dc.title	Dark Triad detection and analysis from social media text	en_US
dc.type	Thesis	en_US