Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model

Asghar, Junaid; Akbar, Saima; Asghar, Muhammad Zubair; Ahmad, Bashir; Al-Rakhami, Mabrook; Gumaei, Abdu

doi:10.1155/2021/5512241

Cited by 23 publications

(11 citation statements)

References 27 publications

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“…In recent years, social media has emerged as a valuable data source for health informatics [ 18 ]. Data from online social media networks, such as Google, YouTube, Facebook, and Twitter, permits people to generate a massive amount of health textual content which can be utilized to tackle various medical tasks such as psychopathic class detection [ 19 , 20 ], depression classification [ 21 ], disease detection [ 22 ], and adverse drug reaction detection [ 23 ]. It is the development of Web 2.0 and Health 2.0 that makes a great deal of health-related informative contents available.…”

Section: Introductionmentioning

confidence: 99%

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Yahya

Asiri

Alyami

2022

Computational and Mathematical Methods in Medicine

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Epilepsy is a common neurological disorder worldwide and antiepileptic drug (AED) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom with minimal, if any, adverse drug reactions (ADRs). Too often, AED treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from a pharmacovigilance perspective, detecting the ADRs of AEDs is a task of utmost importance. Typically, this task is accomplished by analyzing relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance activities, the passiveness and high underreporting ratio associated with spontaneous reporting systems have encouraged the consideration of other data sources such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of traditional data sources. Although in the literature some attempts have investigated the validity and utility of social media for ADR detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the detection of AED ADRs. To this end, a dataset of consumer reviews from two online health communities has been collected. The dataset is preprocessed; the unigram, bigram, and trigram are generated; and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADR lexicon. Three widely used measures, namely, proportional reporting ratio, reporting odds ratio, and information component, are used to measure the association between each ADR and AED. The resulting list of signaled ADRs for each AED is validated against a widely used ADR database, called Side Effect Resource, in terms of the precision of ADR detection. The validation results indicate the validity of online health community data for the detection of AED ADRs. Furthermore, the lists of signaled AED ADRs are analyzed to answer questions related to the common ADRs of AEDs and the similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the data from online health communities for AED-related knowledge discovery tasks.

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Section: Introductionmentioning

confidence: 99%

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Yahya

Asiri

Alyami

2022

Computational and Mathematical Methods in Medicine

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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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“…Junaid et al in [16] proposed a model to classify the input text into psychopathic and nonpsychopathic features. The majority of prior work on psychopath identification has been done in the realm of psychology, employing conventional methodologies like the SRP III method and small dataset sizes.…”

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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Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model

Cited by 23 publications

References 27 publications

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs

The computational psychiatry of antisocial behaviour and psychopathy

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

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