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Cited by 258Computational and Text Analysis MethodsFocus Groups and Qualitative MethodsData Analysis and Archiving

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Using APIs for Data Collection on Social Media

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Lomborg, Stine, Bechmann, Anja·2014·The Information Society
DOI 10.1080/01972243.2014.915276
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Abstract

This article discusses how social media research may benefit from social media companies making data available to researchers through their application programming interfaces (APIs). An API is a back-end interface through which third-party developers may connect new add-ons to an existing service. The API is also an interface for researchers to collect data off a given social media service for empirical analysis. Presenting a critical methodological discussion of the opportunities and challenges associated with quantitative and qualitative social media research based on APIs, this article highlights a number of general methodological issues to be dealt with when collecting and assessing data through APIs. The article further discusses the legal and ethical implications of empirical research using APIs for data collection.

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Research works printed but not linked: 53

  1. 10.1080/1369118X.2012.756050 · DOI
  2. Backstrom, L., P. Boldi, M. Rosa, J. Ugander, and S. Vigna. 2011. Four degrees of separation. http://arxiv.org/abs/1111.4570 (accessed April 10, 2013). · arXiv
  3. Bakshy, E., I. Rosenn, C. Marlow, C. Rosenn, and L. Adamic. 2012. The role of social networks in information diffusion. In WWW ‘12 Proceedings of the 21st International Conference on World Wide Web, 519–28. New York, NY: ACM
  4. 10.1093/acprof:oso/9780195313055.001.0001 · DOI · Paper page
  5. 10.4324/9781315856667 · DOI
  6. 10.1080/16522354.2014.11073574 · DOI
  7. 10.1177/1461444812462853 · DOI
  8. Bernstein M. E. Bakshy M. Burke and B. Karrer. 2013. Quantifying the invisible audience in social networks. Paper presented at the CHI: ACM Conference on Human Factors in Computing Systems Paris France April–May. · DOI
  9. Berry, D.M. 2011. The computational turn. Thinking about the digital humanities. Culture Machine 12. . http://www.culturemachine.net/index.php/cm/article/view/440/470 (accessed April 3, 2014).
  10. 10.1145/2389176.2389211 · DOI
  11. 10.1080/1369118X.2012.678878 · DOI · Paper page
  12. Brügger, N. 2011. Web archiving: Between past, present, and future. In The Blackwell handbook of Internet studies, ed. M. Consalvo and C.M. Ess, 24–42. Oxford, UK: Wiley-Blackwell. · DOI
  13. 10.1080/1461670X.2012.664428 · DOI
  14. 10.5210/fm.v17i4.3937 · DOI · Paper page
  15. Bollier, D. 2010. The promise and peril of big data. Washington, DC: Aspen Institute. . http://www.thinkbiganalytics.com/uploads/Aspen-Big_Data.pdf (accessed March 1, 2012).
  16. 10.1089/cyber.2010.0225 · DOI
  17. De Smedt, T., and W. Daelemans. 2012. Pattern for Python. Journal of Machine Learning Research 13(1): 2063–67.
  18. Ess, C.M. 2009. Digital media ethics. Cambridge, UK: Polity Press.
  19. Ess, C.M., and AoIR Ethics Working Committee. 2002. Ethical decision-making and Internet research: Recommendations from the AoIR Ethics Working Committee. . http://www.aoir.org/reports/ethics.pdf (accessed June 9, 2012).
  20. European Union [the European Parliament and the Council]. 1995. Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Off. J. L 281(23/11/1995): 31–50.
  21. Fuchs, C., K. Boersma, A. Albrechtslund, and M. Sandoval (Eds.). 2012. Internet and Surveillance: The Challenges of Web 2.0 and Social Media. New York: Routledge.
  22. 10.1080/15228835.2012.743797 · DOI
  23. González-Bailón S. N. Wang A. Rivero J. Borge-Holthoefer and Y. Moreno. 2012. Assessing the bias in communication networks sampled from Twitter. . http://ssrn.com/abstract=2185134 (accessed April 3 2014). · DOI
  24. Hansen, D.L., B. Shneiderman, and M.A. Smith 2011. Analyzing social media with NodeXL: Insights from a connected world. Burlington, MA: Elsevier.
  25. Herring, S.C. 2010. Web content analysis: Expanding the paradigm. In The International handbook of Internet research, ed. J. Hunsinger, M. Allen, and L. Klastrup, 233–50. Berlin, Germany: Springer Verlag.
  26. Honeycutt, C., and Herring, S.C. 2009. Beyond microblogging: Conversation and collaboration via Twitter. In Proceedings of the Forty-Second Hawaii International Conference on System Sciences (HICSS-42), 1–10. Los Alamitos, CA: IEEE Press.
  27. 10.1080/1369118X.2011.649774 · DOI
  28. 10.5210/fm.v14i1.2317 · DOI
  29. Java, A., X. Song, T. Finin, and B. Tseng. 2007. Why we Twitter: Understanding microblogging usage and communities. In Proceedings of the Joint 9th WebKDD and 1st SNA-KDD Workshop 2007, 56–65. New York, NY: ACM Press.
  30. 10.1080/1369118X.2012.665468 · DOI
  31. 10.1145/2207676.2207787 · DOI
  32. Leskovec, J., and E. Horwitz. 2007. Planetary-scale views on an instant-messaging network. Microsoft Research technical report (Microsoft Research technical report MSR-TR-2006-186). . http://research.microsoft.com/pubs/70389/tr-2006-186.pdf (accessed April 3, 2014).
  33. Lomborg, S. 2011. Social media. A genre perspective. doctoral dissertation, Aarhus University, Aarhus, Denmark.
  34. 10.1080/15228835.2012.744719 · DOI
  35. 10.1177/1747016112459450 · DOI
  36. Lotan, G., E. Graeff, M. Ananny, D. Gaffney, I. Pearce, and d. boyd. 2011. The revolutions were tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions. International Journal of Communications 5: 1375–405.
  37. 10.1080/08838151.2012.761700 · DOI
  38. Markham, A. 2012. Moving into the flow: Using a network perspective to explore complexity in Internet contexts. In Network analysis: Methodological challenges, ed. S. Lomborg, 45–58 (. CFI Monograph Series, vol. 14). Aarhus, Denmark: Center for Internet Studies, Aarhus University.
  39. Markham, A.N., and E.A. Buchanan. 2012. Ethical decision-making and Internet research: Recommendations from the AoIR Ethics Working Committee (Version 2.0). . http://aoir.org/reports/ethics2.pdf (accessed April 3, 2014).
  40. McChesney, R. 2007. Communication revolution: Critical junctures and the future of media. New York, NY: New Press.
  41. Morstatter F. J. Pfeffer H. Liu and K.M. Carley. 2013. Is the sample good enough? Comparing data from Twitter's Streaming API with Twitter's Firehose. Paper presented at the 7th International Conference on Weblogs and Social Media (ICWSM 2013) Boston MA July. · DOI · Paper page
  42. Naaman, M., J. Boase, and C.-H. Lai. 2010. Is it really about me? Message content in social awareness streams. In Proceedings of the 2010 ACM conference on computer supported cooperative work (CSCW-2010), 189–92. New York, NY: ACM · DOI
  43. 10.1080/1369118X.2011.616519 · DOI
  44. Ohm, P. 2010. Broken promises of privacy: Responding to the surprising failure of anonymization, UCLA Law Review 57: 1701–77.
  45. 10.3758/s13428-011-0116-6 · DOI
  46. Rogers, R. 2012. Digital methods. Cambridge, MA: MIT Press.
  47. Serrano, A. 2011. The social media explosion: By the numbers. Fiscal Times, . September 12. http://www.thefiscaltimes.com/Articles/2011/09/12/The-Social-Media-Explosion-By-the-Numbers.aspx#page1 (accessed July 6, 2012).
  48. Smith, M.A., B. Schneiderman, N. Milic-Frayling, E.M. Rodrigues, V. Barash, C. Dunne, T. Capone, A. Perer, and E. Gleave. 2009. Analyzing (social media) networks with NodeXL. C&T ‘09: Proceedings of the fourth international conference on communities and technologies, 255–64. New York, NY: ACM
  49. Stutzman, F., R. Gross, and A. Acquisti. 2012. Silent listeners: The evolution of privacy and disclosure on Facebook. Journal of Privacy and Confidentiality 4(2): 7–41.
  50. Vicente, C.R., I. Assent, and C.S. Jensen. 2011. effective privacy-preserving online route planning. In 12th IEEE International Conference on Mobile Data Management (MDM), 119–28. Washington, DC: IEEE Computer Society Press.
  51. Weller, K., and C. Puschmann. 2011. Twitter for scientific communication: How can citations/references be identified and measured? In Proceedings of the ACM Web Science Conference (WebSci ‘11), 1–4. . http://journal.webscience.org/500 (accessed April 3, 2014).
  52. 10.1145/1963405.1963504 · DOI
  53. 10.1177/0163443708098245 · DOI

Cited by

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Metadata

Authors
Lomborg, S., & Bechmann, A.
Affiliations
  • University of Copenhagen (DK)
  • Aarhus University (DK)
Corresponding author
Lomborg, Stine
ORCID
Venue
The Information Society
Publisher
Informa UK Limited
Published
2014-07-08
Volume
30
Issue
4
Pages
256-265
ISSN
  • 0197-2243 (ISSN-L)
  • 1087-6537
DOI
10.1080/01972243.2014.915276
OpenAlex
W2102861279
Cited by
258
References
2
Citations by year
2026: 14 · 2025: 24 · 2024: 23 · 2023: 28 · 2022: 20 · 2021: 27 · 2020: 47 · 2019: 25 · 2018: 18 · 2017: 11 · 2016: 13 · 2015: 7 · 2014: 1
FWCI
66.76
Type
journal article
Language
en
Open access
Locations
  • The Information Society (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
Topics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Social Sciences › Sociology and Political Science › Focus Groups and Qualitative Methods
  • Social Sciences › Sociology and Political Science › Data Analysis and Archiving
Keywords
social media APIs, social media research, data collection, third-party developers, research ethics, legal consequences, qualitative and quantitative methods

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CoChat. (2026). Citation check: Using APIs for Data Collection on Social Media. https://app.cochat.ai/papers/10.1080/01972243.2014.915276
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Lomborg, S., & Bechmann, A. (2014). Using APIs for Data Collection on Social Media. The Information Society. https://doi.org/10.1080/01972243.2014.915276