@inproceedings{0a1eca35583745be845d1e93ed034d0a,
title = "Computational and causal examinations of wellbeing in situated contexts by leveraging social media and multimodal data",
abstract = "Assessing wellbeing can be complemented with social and ubiquitous technologies. This dissertation uses social media in concert with multimodal sensing focusing on situated communities. Before incorporating such assessments in practice, we need to account for confounds impacting behavior change. One such confound is 'observer effect', that individuals may self-alter their otherwise normal behavior because of the awareness of being 'monitored'. My proposed work studies this problem on social media behavior. On a multisensor study of 750 participants, I intend to conduct a causal study of modeling behavior change during study participation. This work will provide valuable insights and guide recommendations for correcting biases due to observer effect. This dissertation bears implications for social computing systems and stakeholders to support wellbeing and crisis intervention efforts in situated communities.",
keywords = "Causal inference, College campus, Observer effect, Situated, Social media, Wellbeing, Workplace",
author = "Koustuv Saha",
note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 3rd ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2020 ; Conference date: 17-10-2020 Through 21-10-2020",
year = "2020",
month = oct,
day = "17",
doi = "10.1145/3406865.3418367",
language = "English (US)",
series = "Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW",
publisher = "Association for Computing Machinery",
pages = "147--152",
booktitle = "CSCW 2020 Companion - Conference Companion Publication of the 2020 Computer Supported Cooperative Work and Social Computing",
address = "United States",
}