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Social Media as a Passive Sensor in Longitudinal Studies of Human Behavior and Wellbeing

Authors :
Gonzalo J. Martinez
Nitesh V. Chawla
Munmun De Choudhury
Aaron Striegel
Ge Gao
Anusha Sirigiri
Stephen M. Mattingly
Edward Moskal
Gloria Mark
Andrew T. Campbell
Dong Whi Yoo
Sidney K. D'Mello
Krithika Jagannath
Koustuv Saha
Ayse Elvan Bayraktaroglu
Julie M. Gregg
Anind K. Dey
Source :
CHI Extended Abstracts
Publication Year :
2019
Publisher :
ACM, 2019.

Abstract

Social media serves as a platform to share thoughts and connect with others. The ubiquitous use of social media also enables researchers to study human behavior as the data can be collected in an inexpensive and unobtrusive way. Not only does social media provide a passive means to collect historical data at scale, it also functions as a "verbal" sensor, providing rich signals about an individual's social ecological context. This case study introduces an infrastructural framework to illustrate the feasibility of passively collecting social media data at scale in the context of an ongoing multimodal sensing study of workplace performance (N=757). We study our dataset in its relationship with demographic, personality, and wellbeing attributes of individuals. Importantly, as a means to study selection bias, we examine what characterizes individuals who choose to consent to social media data sharing vs. those who do not. Our work provides practical experiences and implications for research in the HCI field who seek to conduct similar longitudinal studies that harness the potential of social media data.

Details

Database :
OpenAIRE
Journal :
Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems
Accession number :
edsair.doi...........c5071336f683fc5dd942b840a7bc94ec
Full Text :
https://doi.org/10.1145/3290607.3299065