@inproceedings{727c80e9fda9411d80f2d05730516265,
title = "Observer Effect in Social Media Use",
abstract = "While social media data is a valuable source for inferring human behavior, its in-practice utility hinges on extraneous factors. Notable is the “observer effect,” where awareness of being monitored can alter people's social media use. We present a causal-inference study to examine this phenomenon on the longitudinal Facebook use of 300+ participants who voluntarily shared their data spanning an average of 82 months before and 5 months after study enrollment. We measured deviation from participants' expected social media use through time series analyses. Individuals with high cognitive ability and low neuroticism decreased posting immediately after enrollment, and those with high openness increased posting. The sharing of self-focused content decreased, while diverse topics emerged. We situate the findings within theories of self-presentation and self-consciousness. We discuss the implications of correcting observer effect in social media data-driven measurements, and how this phenomenon shines light on the ethics of these measurements.",
keywords = "causal-inference, hawthorne effect, human behavior, language, observer effect, self-presentation, social media",
author = "Koustuv Saha and Pranshu Gupta and Gloria Mark and Emre Kıcıman and {De Choudhury}, Munmun",
note = "This research is supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA Contract No. 2017-17042800007. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. We thank Nick Jaffe, Jordyn Seybolt, Chris Martin, and the members of the Tesserae team and SocWeB lab for contributing to and providing feedback on this work.; 2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024 ; Conference date: 11-05-2024 Through 16-05-2024",
year = "2024",
month = may,
day = "11",
doi = "10.1145/3613904.3642078",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
booktitle = "CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems",
address = "United States",
}