Detection of Antisocial Personality Based on Social Media Data

Mahmud, S. H.; Rana, Masud; Zahir, Fahim Rubaiyat; Huq, Mohammad Rezwanul

doi:10.1007/978-981-15-8354-4_65

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…For example, Sato et al 44 distinguished between adults with antisocial personality disorder and psychopathy 45 versus healthy controls with 80% accuracy, using voxellevel magnetic resonance imaging (MRI) grey matter volume data. Other researchers have used social media content [46][47][48][49][50][51] , Near Infrared Spectroscopy (NIRS) 52 , speech patterns 53 , videotaped head motion 54 , electroencephalogram (EEG) frequency data 55 , and histories of childhood abuse and caregiving 56 to distinguish adults with psychopathy or antisocial personality disorder from healthy controls (see also 57 ).…”

Section: Data-driven Approachesmentioning

confidence: 99%

The computational psychiatry of antisocial behaviour and psychopathy

Pauli

Lockwood

2023

Neuroscience & Biobehavioral Reviews

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Section: Data-driven Approachesmentioning

confidence: 99%

The computational psychiatry of antisocial behaviour and psychopathy

Pauli

Lockwood

2023

Neuroscience & Biobehavioral Reviews

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“…Masud [11] surveyed a chosen number of Facebook users based on the Short Dark Triad (SD3) model. Random Forest, Support Vector Machine, and Nave Bayes algorithms were used to categorize dark triads like Machiavellianism, Self-Centricity, and Psychopathy in this study.…”

Section: Related Workmentioning

confidence: 99%

Personality Classification Model of Social Network Profiles based on their Activities and Contents

Bakry¹,

Nasr²,

Alsheref³

2022

IJACSA

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Social networks have become an important part of everyday life, especially after the latest technologies such as smartphones, tablets, and laptops have become widespread. Individuals spend a lot of time on social media and express their feelings and opinions through statuses, comments, and updates which could be a way to understand and classify their personalities. The personalities in psychological science are divided into five classes according to the Big-five model (Openness, Extraversion, Consciousness, Agreeableness, and Neurotic). This model shows the key features with their weights for each personality. In this paper, a proposed model is developed for detecting the personality features from users' activities in social networks. In this model, machine learning techniques are used for predicting the personalities with a score for each Big-five model type and sorting them in descending order. The personality classification model will be useful in developing a better understanding of the user profile and specifically targeting users with appropriate advertising. Any social media network user's personality can be predicted by using their posts and status updates to get better accuracy. The experimental results of the model in this study provide an enhancement because it can predict the precise score of one user in each factor of the Big-five. The proposed model was tested on a dataset extracted from Facebook and manually classified by experts, and it achieved 89.37% accuracy.

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