scholarly journals Using Machine Learning to Advance Personality Assessment and Theory

2018 ◽  
Vol 23 (2) ◽  
pp. 190-203 ◽  
Author(s):  
Wiebke Bleidorn ◽  
Christopher James Hopwood
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Personality Assessment ◽  
Construct Validation ◽  
Assessment Tools ◽  
Learning Approaches ◽  
Psychological Science ◽  
Validation Framework ◽  
Learning Research ◽  
Applications Of Machine Learning

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

scholarly journals Using Machine Learning to Advance Personality Assessment and Theory

2018 ◽  
Author(s):  
Wiebke Bleidorn ◽  
Christopher James Hopwood
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Personality Assessment ◽  
Construct Validation ◽  
Assessment Tools ◽  
Learning Approaches ◽  
Psychological Science ◽  
Validation Framework ◽  
Learning Research ◽  
Applications Of Machine Learning

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have been focused on the associations between social media and other digital records with established personality measures. The goal of this paper is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation and provide recommendations for how to use machine learning to advance our understanding of personality.

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

2020 ◽  
Vol 34 (5) ◽  
pp. 826-844 ◽  
Author(s):  
Louis Tay ◽  
Sang Eun Woo ◽  
Louis Hickman ◽  
Rachel M. Saef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Text Mining ◽  
Personality Assessment ◽  
Ground Truth ◽  
Psychometric Validation ◽  
Learning Approaches ◽  
Text Data ◽  
Personality Psychology ◽  
Social Media Text

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology

scholarly journals Automatic Personality Identification using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3528-3534
Author(s):  
Aditi Das
Keyword(s):  
Machine Learning ◽  
Personality Assessment ◽  
Large Scale ◽  
Short Term Memory ◽  
Average Score ◽  
Learning Approaches ◽  
Neural Network Learning ◽  
Personality Computing ◽  
Applications Of Machine Learning ◽  
Personality Recognition

Machine Learning has made significant changes in the world making our life more easier and comfortable .One of the most exciting applications is the prediction of Personality automatically using different algorithms. Personality computing and emotive computing, where the popularity of temperament traits is important, have gained increasing interest and a spotlight in several analysis areas recently. These applications can powerfully predict the personality of a Person. The aim of this paper is to use a more rigorous construct Validation system to extend the potential of machine learning approaches to personality assessment. We have reviewed multiple recent applications of Machine Learning to recognize personality, thus providing a broader context of fundamental principles of constructing, validating, and then providing recommendations on how to use Machine Learning to advance the level of our understanding and applying our learnings to develop advanced personality recognition applications. araphrased Text Output text rewrite / rewrite We use deep neural network learning to recognize characteristics independently and, through feature-level fusion of these networks, we obtain final predictions of obvious personalities. We use a previously trained long-term and short-term memory network to integrate time information. We train large-scale models comprised of specific subnetworks- modalities through a two-stage training process. We first train the subnets separately for and then use these trained networks to fit the overall model. We used the ChaLearn First Impressions V2 challenge dataset to evaluate the proposed method. Our method achieves the most effective overall "medium precision" score, with an average score of for 5 personality characteristics, which is compared to the state-of-the-art method.

Interactive Learning with Mutual Explanations in Relational Domains

2021 ◽  
pp. 338-354
Author(s):  
Ute Schmid
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Inductive Logic Programming ◽  
Interactive Learning ◽  
Inductive Logic ◽  
Learning System ◽  
Real World Data ◽  
Relational Domains ◽  
Learning Research ◽  
Applications Of Machine Learning

With the growing number of applications of machine learning in complex real-world domains machine learning research has to meet new requirements to deal with the imperfections of real world data and the legal as well as ethical obligations to make classifier decisions transparent and comprehensible. In this contribution, arguments for interpretable and interactive approaches to machine learning are presented. It is argued that visual explanations are often not expressive enough to grasp critical information which relies on relations between different aspects or sub-concepts. Consequently, inductive logic programming (ILP) and the generation of verbal explanations from Prolog rules is advocated. Interactive learning in the context of ILP is illustrated with the Dare2Del system which helps users to manage their digital clutter. It is shown that verbal explanations overcome the explanatory one-way street from AI system to user. Interactive learning with mutual explanations allows the learning system to take into account not only class corrections but also corrections of explanations to guide learning. We propose mutual explanations as a building-block for human-like computing and an important ingredient for human AI partnership.

scholarly journals Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5924
Author(s):  
Yi Ji Bae ◽  
Midan Shim ◽  
Won Hee Lee
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Mental Health Problems ◽  
Negative Emotion ◽  
Supervised Machine Learning ◽  
Control Group ◽  
Learning Approaches ◽  
Linguistic Features ◽  
Media Texts

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

scholarly journals Research Trend of Causal Machine Learning Method: A Literature Review

2020 ◽  
Vol 9 (2) ◽  
pp. 111-118
Author(s):  
Shindy Arti ◽  
Indriana Hidayah ◽  
Sri Suning Kusumawardhani
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Research Trend ◽  
Learning Approaches ◽  
Algorithm Performance ◽  
Combination Technique ◽  
Causal Impact ◽  
Learning Research ◽  
The Relationship ◽  
Selection Of

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.

scholarly journals Machine Learning Approach to Personality Assessment and Its Application to Personnel Selection

2021 ◽  
Vol 34 (2) ◽  
pp. 213-236
Author(s):  
JiSoo Ock ◽  
HyeRyeon An
Keyword(s):  
Machine Learning ◽  
Personality Assessment ◽  
Personnel Selection ◽  
Scientific Evidence ◽  
New Technology ◽  
Empirical Studies ◽  
Assessment Tools ◽  
Personality Testing ◽  
Future Research ◽  
Legal Standards

As we enter the digital age, new methods of personality testing-namely, machine learning-based personality assessment scales-are quickly gaining attraction. Because machine learning-based personality assessments are made based on algorithms that analyze digital footprints of people’s online behaviors, they are supposedly less prone to human biases or cognitive fallacies that are often cited as limitations of traditional personality tests. As a result, machine learning-based assessment tools are becoming increasingly popular in operational settings across the globe with the anticipation that they can effectively overcome the limitations of traditional personality testing. However, the provision of scientific evidence regarding the psychometric soundness and the fairness of machine learning-based assessment tools have lagged behind their use in practice. The current paper provides a brief review of empirical studies that have examined the validity of machine learning-based personality assessment, focusing primarily on social media text mining method. Based on this review, we offer some suggestions about future research directions, particularly regarding the important and immediate need to examine the machine learning-based personality assessment tools’ compliance with the practical and legal standards for use in practice (such as inter-algorithm reliability, test-retest reliability, and differential prediction across demographic groups). Additionally, we emphasize that the goal of machine learning-based personality assessment tools should not be to simply maximize the prediction of personality ratings. Rather, we should explore ways to use this new technology to further develop our fundamental understanding of human personality and to contribute to the development of personality theory.

scholarly journals Investigating Presence of Ethnoracial Bias in Clinical Data using Machine Learning

2021 ◽  
Author(s):  
Bojana Velichkovska ◽  
Hristijan Gjoreski ◽  
Daniel Denkovski ◽  
Marija Kalendar ◽  
Leo Anthnoy Celi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Data ◽  
Vital Signs ◽  
Medical Applications ◽  
Demographic Information ◽  
Complex Issue ◽  
Important Target ◽  
Learning Research ◽  
Applications Of Machine Learning ◽  
Initial Base

AbstractAn important target for machine learning research is obtaining unbiased results, which require addressing bias that might be present in the data as well as the methodology. This is of utmost importance in medical applications of machine learning, where trained models should be unbiased so as to result in systems that are widely applicable, reliable and fair. Since bias can sometimes be introduced through the data itself, in this paper we investigate the presence of ethnoracial bias in patients’ clinical data. We focus primarily on vital signs and demographic information and classify patient ethnoraces in subsets of two from the three ethnoracial groups (African Americans, Caucasians, and Hispanics). Our results show that ethnorace can be identified in two out of three patients, setting the initial base for further investigation of the complex issue of ehtnoracial bias.

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