A Survey on: Personality Prediction from Multimedia through Machine Learning

BACKGROUND Traditional personality assessment tools, such as questionnaires, were prone to social desirability bias and response distortions, leading to inaccurate measurements and misdiagnosis. These issues undermined the effectiveness of interventions and treatments, necessitating the development of more reliable assessment methods. OBJECTIVE This study aimed to develop and validate a gamified assessment tool (ASP-ECD-G) based on Evidence-Centered Design (ECD) to measure antisocial personality traits through participant behavior in a virtual workplace environment. METHODS The ASP-ECD-G was designed using game elements like narrative, immersion, and feedback to assess antisocial traits. It was based on the DSM-5’s alternative model for diagnosing antisocial personality disorder, which includes traits such as Machiavellism, callousness, deceitfulness, hostility, risk-taking, impulsivity, and irresponsibility. The tool’s development was validated through three sub-studies: Study 1 focused on creating the assessment ontology, Study 2 constructed the assessment model, and Study 3 validated the tool’s characteristics using a 2×2 mixed experimental design. RESULTS The validity tests showed that ASP-ECD-G effectively reflected individual antisocial traits with high robustness in content validity, construct validity, and criterion validity. The tool demonstrated strong resistance to manipulation, preventing participants from altering responses in high-risk scenarios. CONCLUSIONS ASP-ECD-G proved to be a reliable and effective tool for measuring antisocial personality traits in psychological research and organizational contexts, including recruitment and employee management. Its applicability and validity in different cultural contexts and scenarios were also explored.

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The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

Alrehili

Yafooz

Alsaeedi

et al. 2022

Applied Sciences

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The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.

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A Survey on: Personality Prediction from Multimedia through Machine Learning

Cited by 4 publications

References 22 publications

Personality Prediction Using Deep Learning

Personality Prediction Using Deep Learning

A Gamified assessment Tool for Antisocial Personality Traits Based on Evidence-Centered Design (ASP-ECD-G) (Preprint)

The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social Media

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