Abstract
The explosion of data available from online systems such as social media is creating a wealth of trace data, that is, data that record evidence of human activity. The volume of data available offers great potential to advance social and behavioural science research. However, the data are of a very different kind than more conventional social and behavioural science data, posing challenges to use. This paper adopts a data framework from Earth observation science and applies it to trace data to identify possible issues in analysing trace data. Application of the framework also reveals issues for sharing and reusing data.
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References
Agarwal, R., Gupta, A. K., & Kraut, R. (2008). Editorial overview: The interplay between digital and social networks. Information Systems Research, 19(3), 243–252. https://doi.org/10.1287/isre.1080.0200.
Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878.
Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67–80. https://doi.org/10.1016/j.dss.2013.08.008.
Crowston, K., Wei, K., Li, Q., Howison, J. (2006). Core and periphery in free/libre and open source software team communications. In Proceedings of Hawai’i International Conference on System System (HICSS-39). Kaua’i.
Crowston, K., Wiggins, A., Howison, J. (2010). Analyzing leadership dynamics in distributed group communication. In Proceedings of Hawaii International Conference on System Sciences (HICSS-43). Lihue. doi:https://doi.org/10.1109/HICSS.2010.62.
Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., Seaton, D. T., & Chuang, I. (2014). Privacy, anonymity, and big data in the social sciences. Communications of the ACM, 57(9), 56–63. https://doi.org/10.1145/2643132.
Edwards, A., Housley, W., Williams, M., Sloan, L., & Williams, M. (2013). Digital social research, social media and the sociological imagination: Surrogacy, augmentation and re-orientation. International Journal of Social Research Methodology, 16(3), 245–260. https://doi.org/ 10.1080/13645579.2013.774185.
Freelon, D. (2014). On the interpretation of digital trace data in communication and social computing research. Journal of Broadcasting & Electronic Media, 58(1), 59–75. https://doi.org/10.1080/08838151.2013.875018.
Hemphill, L., & Roback, A. J. (2014). Tweet acts: How constituents lobby congress via Twitter. In Proceedings of ACM conference on computer supported cooperative work & social computing (pp. 1200–1210). Baltimore.
Howison, J., Wiggins, A., & Crowston, K. (2011). Validity issues in the use of social network analysis for the study of online communities. Journal of the Association for Information Systems, 12(12), 323–346.
Krippendorff, K. (2004). Content analysis: An introduction to its methodology. Newbury Park: Sage.
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Life in the network: The coming age of computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742.
Liang, H., & Fu, K.-W. (2015). Testing propositions derived from twitter studies: Generalization and replication in computational social science. PloS One, 10(8), e0134270. https://doi.org/10.1371/ journal.pone.0134270.
Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the digital humanities (pp. 460–475). Minneapolis: University of Minnesota Press.
McClelland, C. A. (1967). Event interaction analysis in the setting of quantitative international relations research. Los Angeles: Department of Political Science, University of Southern California.
McClelland, C. A. (1983). Let the user beware. International Studies Quarterly, 27(2), 169–177. https://doi.org/10.2307/2600544.
Panciera, K., Priedhorsky, R., Erickson, T., Terveen, L. (2010). Lurking? Cyclopaths? A quantitative lifecyle analysis of user behavior in a geowiki. In Proceedings of ACM conference on Computer-Human Interaction (CHI). Atlanta.
Parkinson, C. L., Ward, A., & King, M. D. (Eds.). (2006). Earth science reference handbook: A guide to NASA’s earth science program and earth observing satellite missions. Washington, DC: National Aeronautics and Space Administration. Available from: http://eospso.gsfc.nasa.gov/sites/default/files/publications/2006ReferenceHandbook.pdf.
Veen, T. (2008). Event data: A method for analysing political behaviour in the EU. In Proceedings of prepared for the fourth Pan-European conference on EU Politics, Riga, Latvia. Available from: http://www.jhubc.it/ecpr-riga/virtualpaperroom/002.pdf.
Watts, D. J. (2007). A twenty-first century science. Nature, 445(7127), 489–489. https://doi.org/ 10.1038/445489a.
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Crowston, K. (2017). Levels of Trace Data for Social and Behavioural Science Research. In: Matei, S., Jullien, N., Goggins, S. (eds) Big Data Factories. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-59186-5_4
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DOI: https://doi.org/10.1007/978-3-319-59186-5_4
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