The DOI matches its Crossref record.
Computational social science: CSCW in the social media era
Abstract
With the widespread proliferation of social media tools such as Facebook and Twitter, the CSCW community has seen a growing interest among researchers to turn to records of social behavior from blogs, social media, and social networking sites, to study human social behavior. This nascent area, that has begun to be referred to in various research circles as "computational social science", provides an exciting and promising development in the CSCW community. This panel brings together a host of researchers with varied disciplinary perspectives to investigate the potential and current state-of-the-art, as well as forthcoming challenges, practical and methodological issues that engender this emerging topic.
Overview
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Together, the digital divide and the data divide can be an important source of bias on the questions that are asked and the populations that are chosen for research (boyd and Crawford, 2012; Counts et al., 2014).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Counts et al., 2014
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 8
- 10.1145/2441776.2441937 · DOI
- 10.1145/2145204.2145360 · DOI
- 10.1145/2145204.2145271 · DOI
- 10.1145/1460563.1460572 · DOI
- 10.1145/1718918.1718953 · DOI
- Pew Internet . The Demographics of Social Media users -- 2012 . http://www.pewinternet.org/Reports/ 2013 /Social-media-users.aspx Pew Internet. The Demographics of Social Media users -- 2012. http://www.pewinternet.org/Reports/ 2013 /Social-media-users.aspx
- 10.1145/2145204.2145347 · DOI
- 10.1145/1718918.1718965 · DOI · Paper page
Cited by
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
Metadata
- Authors
- Counts, S., De Choudhury, M., Diesner, J., Gilbert, E., Gonzalez, M., Keegan, B., Naaman, M., & Wallach, H.
- Affiliations
- Microsoft Research, Redmond, WA, USA
- University of Illinois, Champaign, IL, USA
- Georgia Institute of Technology, Atlanta, GA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Northeastern University, Boston, MA, USA
- Cornell Tech, New York City, NY, USA
- University of Massachusetts, Amherst, MA, USA
- Venue
- Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
- Publisher
- ACM
- Published
- 2014-02-15
- Pages
- 105-108
- Cited by
- 11
- References
- 0
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
