Elsevier

Future Generation Computer Systems

Volume 136, November 2022, Pages 322-325
Future Generation Computer Systems

Editorial
Future-generation personality prediction from digital footprints

https://doi.org/10.1016/j.future.2022.06.011Get rights and content

Introduction

Perceiving the personality of others is an intuitive task that humans perform on a daily basis. However, it is more difficult to formalize how this process happens exactly and it is particularly challenging to teach machines to do so. Personality traits are conceptualized as characteristic patterns of thoughts, feelings, and behaviors [1]. Personality traits have repeatedly been related to a wide range of important life outcomes at personal (e. g., well-being, psychopathology), interpersonal (e. g., relationship satisfaction), and social-institutional levels (e. g., occupational choices, job success) [2], [3]. Hence, knowing someones personality can be extremely informative to anticipate behaviors, thoughts, and feelings across different situations and to decide who to hire, what to recommend [4], to befriend, to marry, or to open a company with. Related, there has been a massive surge in the development of computational models which use digital data on human behavior and preferences (i. e., digital footprints) to infer personality trait levels of an individual [5], [6], [7]. This surge has largely been enabled by the digitization of global societies, recent advances in artificial intelligence and computing technologies. Consumer electronics (e. g., smartphones, wearables) and the subsequent development of mobile computing technologies have facilitated the collection of fine-grained, high-dimensional data on behaviors and situations through mobile applications and sensors [8], [9]. Online environments and social media in particular allow users to consume endless streams of digital content, to build an online profile, to communicate and interact with others, and to post content such as text, images, or links to according platforms [10]. The information that is left behind as a byproduct of these human activities in both online and offline environments reflects their patterns of thinking, feeling, and behaving — their personality traits [11].

The promise of computational personality assessment [12] or short personality prediction is to automatically predict peoples’ personality from their digital footprints without the requirement for manual input (e. g., via a questionnaire). In that sense, personality prediction can be important for practical applications ranging from recommendation systems, computational advertising [13], marketing [14], or job screening to aiding in psychological counseling, intervention and therapy, and enhanced human-AI interaction [15].

The unreflected and unregulated application of personality prediction has also raised concern about research in this area. The complexity and the digital persistence of models potentially interferes with the protection of individual privacy and the conception of informed consent [16]. While the performance of most models is not high enough to allow for the precise distinction or assessment of people, predictions can still be ‘right’ on average and be utilized for digital mass persuasion and targeting efforts in marketing and political campaigns [17].

This special issue brings together diverse, novel and impactful work on personality prediction in one place, to foster a development towards a common understanding of how personality can be conceptualized and how it can measured in a state-of-the art way. The rest of this article is structured so that in Section 2, we briefly present the six articles (selected from 25) that are included in this special issue, and in Section 3, we conclude with a short discussion and outlook perspective.

Section snippets

Predicting time preference from social media behavior

In this paper, Kurz and König investigated whether they could predict individuals’ time preferences from their Facebook data. Time preference is an individual’s tendency or preference in choosing between immediate and future rewards. Their method of analysis included building topic features from individual’s Facebook likes using LDA topic modeling and then feeding these features into automated machine learning (AutoML) [18]. Through using this novel method, they found that time preference could

Discussion and conclusions

Research in personality prediction has gone through remarkable developments in the recent years with results suggesting the possibility to predict individual personality trait levels from a range of digital behaviors [15], [19], [20]. The recent years have also shown that personality theory and prediction largely happen in two different disciplines — personality psychology and computer science. Here, we want to iterate on three key issues that currently stand in the way of a more collaborative

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The Guest Editors wish to thank all authors for sharing the outcomes of their research, which make this special issue possible. The Editors are grateful to the reviewers for their thoughtful and constructive comments and suggestions. The Guest Editors wish to express their gratitude to Erik Cambria, the Special Issue Editor and Michela Taufer, the Editor in Chief. The Guest Editors are also grateful to the members of the Journal Editorial Office, in particular Mohammed Samiullah who provided

Yash Mehta is currently a research engineer at HHMI Janelia Research Campus working at the intersection of deep learning and neuroscience. One of his most representative works in personality detection is the literature survey, ‘Recent Trends in Deep Learning-Based Personality Detection’. He has been fortunate to have gotten the opportunity to have worked with brilliant researchers on diverse, exciting topics such as neural architecture search with Frank Hutter (ELLIS Fellow, Uni Freiburg),

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  • Cited by (0)

    Yash Mehta is currently a research engineer at HHMI Janelia Research Campus working at the intersection of deep learning and neuroscience. One of his most representative works in personality detection is the literature survey, ‘Recent Trends in Deep Learning-Based Personality Detection’. He has been fortunate to have gotten the opportunity to have worked with brilliant researchers on diverse, exciting topics such as neural architecture search with Frank Hutter (ELLIS Fellow, Uni Freiburg), biologically plausible learning with Tim Lillicrap (DeepMind) and Peter Latham (Gatsby, UCL) and personality prediction with Erik Cambria (NTU Singapore). He was previously a software developer at Amazon and enjoys coding and working on challenging algorithmic problems.

    Clemens Stachl is Associate Professor of Behavioral Science at the University of St. Gallen, Switzerland and Director of the Institute of Behavioral Science and Technology. He received his diploma in Psychology from University of Graz and his doctoral degree in Psychology from Ludwig-Maximilians-Universität München (LMU). Prior to joining faculty at St. Gallen, he held postdoctoral positions at the LMU and at Stanford University. He is an active member of the Society for Personality and Social Psychology (SPSP) and of the German Society of Psychology (DGPS). Clemens’ work lies at the intersection of psychology, data science, and computer science. In particular his research deals with the prediction, description, and explanation of psychological phenomena (i.e., personality traits) with methods from the computational sciences. In 2019, he received the prize for digital diagnostics from the German Society of Psychology for his work on personality prediction from digital behavioral data collected with smartphones. Other aspects of his research are focused on the personalization of digital systems with regard to individual differences (i.e., personality traits), ethical implications of technology, and human-centered factors to cybersecurity.

    Konstantin Markov is full Professor of Information Science at the University of Aizu, Japan. He has been a regular visiting Professor at the Institute of Statistical Mathematics, Tokyo, Japan. Prior to becoming a faculty member of the University of Aizu, he was a senior research scientist at the Spoken Language Communication Lab of the Advanced Telecommunication Research (ATR) Institute International, Kyoto, Japan. His research is focused mainly on statistical signal processing, machine and deep learning, and AI in general with applications to audio, natural language and image processing.

    Joseph T. Yun is the artificial intelligence and innovation architect for the University of Pittsburgh. He also is a research professor of electrical and computer engineering in the Swanson School of Engineering, University of Pittsburgh. Yun’s research is primarily focused on novel data science algorithms, user-centric analytics systems, and societal considerations of AI-based advertising and marketing (e.g., privacy, ethics). Yun is the principal investigator of the Social Media Macroscope, which is an open research environment for social media analytics (socialmediamacroscope.org). He is also an affiliate of Pitt Cyber and the Collaboratory Against Hate.

    Björn W. Schuller received his diploma, doctoral degree, habilitation, and Adjunct Teaching Professor in Machine Intelligence and Signal Processing all in EE/IT from TUM in Munich/Germany. He is Full Professor of Artificial Intelligence and the Head of GLAM at Imperial College London/UK, Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany, co-founding CEO and current CSO of audEERING — an Audio Intelligence company based near Munich and in Berlin/Germany, and permanent Visiting Professor at HIT/China amongst other Professorships and Affiliations. Previous stays include Full Professor at the University of Passau/Germany, and Researcher at Joanneum Research in Graz/Austria, and the CNRS-LIMSI in Orsay/France. He is a Fellow of the IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the ISCA, Fellow of the BCS, President-Emeritus and Fellow of the AAAC, and Senior Member of the ACM. He (co-)authored 1200+ publications (45k+ citations, h-index=97), is Field Chief Editor of Frontiers in Digital Health and was Editor in Chief of the IEEE Transactions on Affective Computing, amongst manifold further commitments and service to the community. His 40+ awards include having been honored as one of 40 extraordinary scientists under the age of 40 by the WEF in 2015. He served as Coordinator/PI in 15+ European Projects, is an ERC Starting Grantee, and consultant of companies such as Barclays, GN, Huawei, or Samsung.

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