The DOI matches its Crossref record; 74 of the 75 references it cites were found in a registry or an index.
On the Interpretation of Digital Trace Data in Communication and Social Computing Research
Abstract
The widespread availability of analytical tools for Big Data offers enormous opportunities and challenges for communication researchers. In contrast to user-generated texts, digital trace data (evidence of online user activities such as hyperlinks and retweets) represent a new methodological frontier for the field. However, interpretive strategies remain scattered and ad hoc with few best practices to guide them. To help remedy this situation, this article reviews recent scholarship in both communication and social computing research that has incorporated three common types of trace data: hyperlinks, Twitter followers, and retweets. It finds that while researchers in both fields have interpreted each trace in a variety of ways, they have largely declined to explain the validity of their interpretations.
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“Norms are also sensitive to context, as the meaning of the same action or mechanism may change under different circumstances”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Freelon, 2014
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“However, this assumption has been challenged due to concerns with construct validity and stability over time (Freelon, 2014; Lazer, 2015).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Freelon, 2014
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Cites
Social network sites: definition, history, and scholarship
boyd, D. M., & Ellison, N. B. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00393.x · Cited by 16,251
(1999) · Cited by 12,854
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
(2011) · Cited by 5,203
I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience
Marwick, A. E., & boyd, D. (2011) · New Media & Society · doi:10.1177/1461444810365313 · Cited by 4,612
Java, A., Song, X., Finin, T., & Tseng, B. L. (2007) · doi:10.1145/1348549.1348556 · Cited by 2,662
Information credibility on twitter
Castillo, C., Mendoza, M., & Poblete, B. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963500 · Cited by 2,593
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
Boyd, D., Golder, S., & Lotan, G. (2010) · 2010 43rd Hawaii International Conference on System Sciences · doi:10.1109/hicss.2010.412 · Cited by 2,051
Journal of Computer-Mediated Communication
(2018) · Journal of Computer-Mediated Communication · doi:10.1111/(issn)1083-6101 · Cited by 1,894
Weng, J., Lim, E., Jiang, J., & He, Q. (2010) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1718487.1718520 · Cited by 1,780
Research works printed but not linked: 6
- Coddington M., 2012, International Journal of Communication, 6, 2007
- 10.1111/j.1460-2466.2005.tb02682.x · DOI
- Manyika J., 2011, McKinsey Global Institute, 1
- Moe H., 2012, International Journal of Communication, 6, 1222
- Papacharissi Z., 2012, International Journal of Communication, 6, 1989
- Wilson C., 2011, International Journal of Communication, 5
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Freelon, D. (2015) · The International Encyclopedia of Communication · doi:10.1002/9781405186407.wbiecb030
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McCrosky, J. D., Parry, D. A., Sewall, C. J. R., & Orben, A. C. (2023) · Technology Mind and Behavior · doi:10.1037/tmb0000095.supp
Takács, L. (2022) · Pro Publico Bono - Magyar Közigazgatás · doi:10.32575/ppb.2022.4.6
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Cioroianu, I., & Coffé, H. (2024) · Parliamentary Affairs · doi:10.1093/pa/gsae033
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From Digital Trace Data to Theory: Pathways Toward Theory Construction
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Sun, T., Feng, H., Ye, J., Yun, S., Kim, Y., & Hong, S. R. (2026) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3797953
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Drost, E. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26629-4.00201-x
Kayabaşı, E., & Çakmak, E. K. (2026) · Journal of Educational Technology Systems · doi:10.1177/00472395261465914
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Mohammadi, N., Maghsoudi, M., & Bakhtiari, M. (2026) · Geography and sustainability · doi:10.1016/j.geosus.2026.100524
Park, K., & Yang, J. (2026) · Journal of Promotion Management · doi:10.1080/10496491.2026.2714527
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Metadata
- Authors
- Freelon, D.
- Affiliations
- American University (US)
- Corresponding author
- Freelon, Deen
- ORCID
- Freelon, Deen
- Venue
- Journal of Broadcasting & Electronic Media
- Publisher
- Informa UK Limited
- Published
- 2014-01-02
- Volume
- 58
- Issue
- 1
- Pages
- 59-75
- ISSN
- 0883-8151 (ISSN-L)
- 1550-6878
- OpenAlex
- W2074336863
- Cited by
- 132
- References
- 75
- Citations by year
- 2026: 15 · 2025: 5 · 2024: 13 · 2023: 10 · 2022: 8 · 2021: 15 · 2020: 14 · 2019: 12 · 2018: 14 · 2017: 6 · 2016: 8 · 2015: 9 · 2014: 2
- FWCI
- 52.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Broadcasting & Electronic Media (publishedVersion)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Communication › Media and Digital Communication
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Keywords
- digital traces, interpretive strategies, hyperlinks, Twitter followers, retweets, social computing, communication research methods, big data, validity of interpretation
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