Social Media, Personality, and Leadership as Predictors of Job Performance

A thorough assessment of privacy concerns, reviewer bias, and applicant computer familiarity informs this longitudinal study incorporating features derived from social media, personality, leadership, traditional selection methodology, and objective measures of employee performance to build an empirical foundation for future research. To date, limited research has embarked upon an in-depth examination of the organizational implications of using social media data to assess job applicants. This dissertation addresses the question of whether social media data matters in the practical context of talent selection. I begin with a review of pertinent online communication theories, including media richness, cues filtered out, and social information processing theories before applying their concepts to social media. I review accumulated evidence that signals from social media use can predict personality and explore less-studied links between social media and full leadership behavior, with a focus on transformational leadership. The review also integrates privacy behavior. A survey covering personality, leadership, and privacy behavior, was completed by 107 call center agents who were subsequently invited to share their public Facebook profile. Of those, 48 volunteered to share quantitative and qualitative data from their public profile. A group of trained raters further coded profiles. The participants\u27 employer also provided performance and retention data. This study found mixed support for previously reported links between social media use and personality. An interaction of conscientiousness and computer skills predicted privacy skills and profile completeness, such that participants either high in both or low in both were more likely to have higher self-rated privacy skills and completed social media profiles. Raters were easily able to deduce demographic information from social media profiles, including gender, age, and ethnicity. Worryingly, evidence of bias in pass rates was detected based on raters\u27 hire vs no-hire recommendations, though the degree of bias varied by pass rate threshold. Finally, the various predictors were combined alongside scores from participants\u27 original pre-hire selection assessments to determine whether there was incremental value in including them as part of a holistic selection process. Some support was found for the incremental utility of the entire battery, as personality, social media activity, human ratings of social media profiles, and self-reported transformational leadership behavior uniquely contributed to a Cox regression model predicting retention. Support for the battery approach was much weaker when predicting efficiency (average handle time) as only transformational leadership provided statistically significant predictiveness beyond the pre-hire assessment. Altogether, this dissertation underscores the importance of relying on defensible selection methods to predict retention and performance outcomes. If social media is used in screening, it is best done in the context of other selection methods and should be based on computer-based automated screening rather than individual human ratings to reduce bias. This dissertation demonstrates that social media and leadership can add incremental prediction to selection decisions for entry-level jobs and makes recommendations for further research

Study of Approaches to Predict Personality Using Digital Twin

With a growing proportion of online activities on social networking sites on different mediums like Facebook, Instagram, Twitter, LinkedIn the requirement for personality prediction associated with this online mediated behavior has also increased significantly. The user generated content on social media can be effectively leveraged to record, analyze and predict personality through different psychological approaches like MBTI, Big Five, and DISC. Predicting personality has displayed an intrinsic influence in multifarious domains like career choice, political influence, brand inclination, customized advertising, improvising learning outcomes, recommender system algorithms and so on. The objective of this paper is to stipulate an overview of different strategies used by researchers to predict personality based on the social media usage and user generated content across prominent social media platforms. It was observed that the personality traits can be accurately inferred from user behavior reflected on social media through attributes like status posted, pictures uploaded, number of friends, groups joined, network density, liked content. As of now, Facebook followed by Twitter are the most prominent social media platforms for conducting the study however, the use other social media platforms like Instagram, LinkedIn are expected to increase exponentially for carrying out personality prediction study

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Problematic social media use in the Covid-19 era: The role of personality and trait emotional intelligence

According to previous research, social media use among students has increased during the COVID-19 pandemic due to lockdown and transition to distance learning. Many studies have demonstrated a link between personality traits and problematic social media use (PSMU). In addition to basic personality traits, trait emotional intelligence (EI) has shown to be a protective factor against various behavioral problems: higher trait EI is likely to be related to decreased PSMU. The present study explored the role of basic personality traits and trait EI in predicting PSMU during the pandemic. Subjects in this online research were female students (N=259) from the University of Belgrade who completed: (1) Bergen Social Media Scale (BSMAS), measuring problematic social media use according to the core components of addiction (salience, mood modification, tolerance, withdraw symptoms, conflict, and relapse), (2) HEXACO Personality Inventory Revised comprising traits Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to experience, and (3) the TEIQue, examining trait EI factors - Well-Being, Self-control, Emotionality, and Sociability. Hierarchical regression model comprised of the HEXACO personality traits (entered 1st) and trait EI factors (entered 2nd) as predictors, and PSMU as a criterion variable was tested. HEXACO personality traits explained 23% of variance (F(6, 252)=13.682, Adj.R2=.228, p<.001) with Honesty-Humility (β=-.289, p<.001), Emotionality (β=.190, p<.001), Extraversion (β=-.116, p<.05), Conscientiousness (β=-.275, p<.001) and Openness to experience (β=-.116, p<.05) as significant predictors. In the second step, PSMU was predicted (F(10, 248)=8.990, Adj.R2=.236, p<.001) negatively by HEXACO Honesty-Humility (β=-.287, p<.001), Conscientiousness (β=-.171, p<.05) and by trait EI factor Self-control (β=-.202, p<.05). Trait EI factors offered no incremental increase in predicting PSMU. The current data confirmed relations between PSMU and most of HEXACO personality traits. The results also indicate significant role of Self-control as a trait EI factor in predicting PSMU in the COVID era

Problematic social media use in the Covid-19 era: The role of personality and trait emotional intelligence

According to previous research, social media use among students has increased during the COVID-19 pandemic due to lockdown and transition to distance learning. Many studies have demonstrated a link between personality traits and problematic social media use (PSMU). In addition to basic personality traits, trait emotional intelligence (EI) has shown to be a protective factor against various behavioral problems: higher trait EI is likely to be related to decreased PSMU. The present study explored the role of basic personality traits and trait EI in predicting PSMU during the pandemic. Subjects in this online research were female students (N=259) from the University of Belgrade who completed: (1) Bergen Social Media Scale (BSMAS), measuring problematic social media use according to the core components of addiction (salience, mood modification, tolerance, withdraw symptoms, conflict, and relapse), (2) HEXACO Personality Inventory Revised comprising traits Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to experience, and (3) the TEIQue, examining trait EI factors - Well-Being, Self-control, Emotionality, and Sociability. Hierarchical regression model comprised of the HEXACO personality traits (entered 1st) and trait EI factors (entered 2nd) as predictors, and PSMU as a criterion variable was tested. HEXACO personality traits explained 23% of variance (F(6, 252)=13.682, Adj.R2=.228, p<.001) with Honesty-Humility (β=-.289, p<.001), Emotionality (β=.190, p<.001), Extraversion (β=-.116, p<.05), Conscientiousness (β=-.275, p<.001) and Openness to experience (β=-.116, p<.05) as significant predictors. In the second step, PSMU was predicted (F(10, 248)=8.990, Adj.R2=.236, p<.001) negatively by HEXACO Honesty-Humility (β=-.287, p<.001), Conscientiousness (β=-.171, p<.05) and by trait EI factor Self-control (β=-.202, p<.05). Trait EI factors offered no incremental increase in predicting PSMU. The current data confirmed relations between PSMU and most of HEXACO personality traits. The results also indicate significant role of Self-control as a trait EI factor in predicting PSMU in the COVID era

Predicting the results of the 16-factor R. Cattell test based on the analysis of text posts of social network users

We investigated the possibility of automating the prediction of the 16-factor personality traits by R. Cattell from text posts of social media users. The proposed new method of automating the evaluation of R. Kettell’s 16-factor personality test traits includes language models and neural networks. Implementation of the method involves several steps. At the first step text posts are extracted from user accounts of social media, pre-processed with language model RuBERT and previously trained over a full-connected neural network. The result of this step is a normalized empirical distribution of the posts by the previously introduced classes for each user. Subsequently, based on the distribution of user posts the evaluation of the expression of psychological features of the user is made with the help of support vector machine, random forest and Naive Bayesian classifier. The final data set for model building and further testing their performance was made up of 183 respondents who took the R. Cattell test, with links to their public social media accounts. Classifiers predicting results for six factors (A, B, F, I, N, Q1) of R. Cattells 16-factor personality test were constructed. The results can be used to create a prototype of automated system for predicting the severity of psychological features of social media users. Results of work are useful in the applied and research systems connected with marketing, psychology and sociology, and also in the field of protection of users from social engineering attacks

Problematic social media use in the covid-19 era: the role of personality and trait emotional intelligence

According to previous research, social media use among students has increased during the COVID-19 pandemic due to lockdown and transition to distance learning. Many studies have demonstrated a link between personality traits and problematic social media use (PSMU). In addition to basic personality traits, trait emotional intelligence (EI) has shown to be a protective factor against various behavioral problems: higher trait EI is likely to be related to decreased PSMU. The present study explored the role of basic personality traits and trait EI in predicting PSMU during the pandemic. Subjects in this online research were female students (N = 259) from the University of Belgrade who completed: (1) Bergen Social Media Scale (BSMAS), measuring problematic social media use according to the core components of addiction (salience, mood modification, tolerance, withdraw symptoms, conflict, and relapse), (2) HEXACO Personality Inventory Revised comprising traits Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to experience, and (3) the TEIQue, examining trait EI factors - Well-Being, Self-control, Emotionality, and Sociability. Hierarchical regression model comprised of the HEXACO personality traits (entered 1st) and trait EI factors (entered 2nd) as predictors, and PSMU as a criterion variable was tested. HEXACO personality traits explained almost 23% of variance (F(6, 252) = 13.682, Adj. R2 = .228, p < .001) with Honesty- Humility (β = -.289, p < .001), Emotionality (β = .190, p < .001), Extraversion (β = -.116, p < .05), Conscientiousness (β = -.275, p < .001) and Openness to experience (β = -.116, p < .05) as significant predictors. In the second step, PSMU was predicted (F(10, 248) = 8.990, Adj. R2 = .236, p < .001) negatively by HEXACO Honesty- Humility (β = -.287, p < .001), Conscientiousness (β = -.171, p < .05) and by trait EI factor Self-control (β = -.202, p < .05). Trait EI factors offered no incremental increase in predicting PSMU. The current data confirmed relations between PSMU and most of HEXACO personality traits. The results also indicate significant role of Self-control as a trait EI factor in predicting PSMU in the COVID era.Book of abstracts : 18th International Conference Days of Applied Psychology 2022, Niš, Serbia, September 23rd-24th 2022