Details

Autor(en) / Beteiligte

• Saha, Koustuv
• Bayraktaroglu, Ayse E.
• Campbell, Andrew T.
• Chawla, Nitesh V.
• De Choudhury, Munmun
• D'Mello, Sidney K.
• Dey, Anind K.
• Gao, Ge
• Gregg, Julie M.
• Jagannath, Krithika
• Mark, Gloria
• Martinez, Gonzalo J.
• Mattingly, Stephen M.
• Moskal, Edward
• Sirigiri, Anusha
• Striegel, Aaron
• Yoo, Dong Whi

Titel

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

Ist Teil von

• Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, p.1-8

Ort / Verlag

New York, NY, USA: ACM

Erscheinungsjahr

2019

Link zum Volltext

Quelle

ACM Digital Library Complete

Beschreibungen/Notizen

• 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.