Studying human behavior at the intersection of mobile sensing and complex networks (Keynote abstract)

Inferring Complex Human Behavior Using a Non-obtrusive Mobile Sensing Platform

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Mobile Computing, Applications, and Services ◽

10.1007/978-3-642-29336-8_18 ◽

2012 ◽

pp. 306-310

Author(s):

Bruce DeBruhl ◽

Michele Cossalter ◽

Roy Want ◽

Ole Mengshoel ◽

Pei Zhang

Keyword(s):

Human Behavior ◽

Mobile Sensing ◽

Sensing Platform

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Mobile Sensing: Leveraging Machine Learning for Efficient Human Behavior Modeling

2020 Systems and Information Engineering Design Symposium (SIEDS) ◽

10.1109/sieds49339.2020.9106648 ◽

2020 ◽

Author(s):

Erin K. Barrett ◽

Cameron M. Fard ◽

Hannah N. Katinas ◽

Charles V. Moens ◽

Lauren E. Perry ◽

...

Keyword(s):

Machine Learning ◽

Human Behavior ◽

Mobile Sensing ◽

Behavior Modeling ◽

Human Behavior Modeling

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Experience Sampling Method

10.33767/osf.io/5u9ds ◽

2020 ◽

Author(s):

Lisa Rhee ◽

Joseph Bayer ◽

Alexander Hedstrom

Keyword(s):

Human Behavior ◽

Ecological Validity ◽

Contextual Information ◽

Experience Sampling ◽

Mobile Sensing ◽

Social Scientists ◽

The Social ◽

Self Reports ◽

Ecological Momentary

The use of experience sampling methodology (ESM), also known as ecological momentary assessment (EMA), is firmly established in the social sciences. The family of ESM methods employs in vivo self-reports, providing opportunities for social scientists to study human behavior with high ecological validity. More recently, researchers across disciplines have begun to conduct studies that directly combine ESM with mobile sensing, thereby blending the benefits of subjective self-reports with more objective traces of human behavior. This combination of ESM and mobile sensing offers new opportunities, such as predetermined digital events that initiate ESM survey notifications based on contextual information. Altogether, ESM is evolving in ways that offer new opportunities and challenges for researchers.

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Computational Personality Assessment - An Overview and Perspective

10.31234/osf.io/ck2bj ◽

2021 ◽

Author(s):

Clemens Stachl ◽

Ryan L. Boyd ◽

Kai Tobias Horstmann ◽

Poruz Khambatta ◽

Sandra Matz ◽

...

Keyword(s):

Social Media ◽

Human Behavior ◽

Personality Assessment ◽

Mobile Sensing ◽

Digital Data ◽

Objective Measures ◽

Digital Revolution ◽

Personality Research ◽

Online Social Media ◽

Practical Implications

Computational methods for the representation and analysis of data have drastically increased the objectivity, reliability, and the practical implications of research conducted throughout most scientific pursuits. Our rapidly-emerging potential to transform digital data into objective measures of human behavior, thoughts, and feelings has perfectly positioned personality science as a critical discipline that will benefit from today’s ongoing digital revolution. Here, we review and discuss some of the most promising approaches to computational personality assessment based on data from experience sampling, natural language, online social media, mobile sensing, and images. We present a concise overview of key findings, discuss the potential and promise ofcomputational personality assessment, and highlight important remaining questions in their development and application. We conclude with an optimistic outlook on how computational assessment could fuel the transition from personality research to personality science.

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