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Exploring Journalism and Computer Science Student Collaboration
A Norwegian case study

Introduction The digitalization of journalism and convergence of media platforms has resulted in an increased overlap between technology and journalism. From a professional perspective, this means that technology developers have begun working as technology developer-journalists and computer science engineers have shown a growing interest in journalism (Lewis & Usher, 2014). Leading voices in the field of journalism have also pushed for journalists to embrace computer science (Lewis & Usher, 2016) and pointed out the profound implications of digitalization for

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Encounters with Self-Monitoring Data on ICT Use

(6): 985-1002. Best, K. (2010). Living in the control society: Surveillance, users and digital screen technologies. International Journal of Cultural Studies , 13(1): 5-24. Cadwalladr, C. & Graham-Harrison, E. (2018, March 17). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. The Guardian [online]. Retrieved from https://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-election [accessed 2018, March 28]. Coté, M. (2014). Data motility. The materiality of big social data

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Tracing Communicative Patterns
A comparative ethnography across platforms, media and contexts

). Network sampling: From snowball and multiplicity to respondent-driven sampling. Annual Review of Sociology, 43(1): 101-119. doi: https://doi.org/10.1146/annurevsoc-060116-053556 Helles, R. (2011). Hverdagslivets nye medier. Dansk Sociologi, 21(3): 49-63. Hutchby, I. (2001). Conversation and technology, from the telephone to the Internet. Cambridge & Malden. Polity Press. Jensen, K. B. (2008). Intermediality. In W. Donsbach (ed.), International encyclopedia of communication. Malden, MA: Blackwell. Lash, S. & Urry, J. (1987). The end of

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Structural Ageism in Big Data Approaches

References AGE Platform Europe. (2016). AGE Platform Europe position on structural ageism . Brussels, Belgium. Alvarez-Lozano, J., Osmani, V., Mayora, O. et al. (2014). Tell me your apps and I will tell you your mood. In Conference on pervasive technologies related to assistive environments (PETRA’14) (pp. 1-7). Island of Rhodes: ACM Press. Ayalon, L. & Tesch-Römer, C. (eds.) (2018). Contemporary perspectives on ageism . Cham: Springer Open. Bayot, R. K. & Gon, T. (2017). Age and gender classification of tweets using convolutional

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The Meaning of Links
On the interpretation of hyperlinks in the study of polarization in blogging about climate change

Technology , 57(1): 1-147. Turner, J. C. et al. (1987). Rediscovering the social group: A self-categorization theory . New York: Basil Blackwell. Yardi, S. & Boyd, D. (2010). Dynamic debates: An analysis of group polarization over time on Twitter. Bulletin of Science, Technology & Society , 30(5): 316-327.

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One Day in the Life of a National Twittersphere

-and-why-do-some-politicians-want-it-changed-64660 [Accessed 2018, December 7]. Münch, F. (2019). Measuring networked media – Exploring network science methods for large scale online media studies . PhD Dissertation. Brisbane: Queensland University of Technology. Murthy, D. (2018). Twitter (2nd ed.). Cambridge: Polity. Palen, L., Starbird, K., Vieweg, S. & Hughes, A. (2010). Twitter-based information distribution during the 2009 Red River Valley flood threat. Bulletin of the American Society for Information Science and Technology , 36(5): 13-17. Papacharissi, Z. & Blasiola, S. (2016

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A Room of One’s Own?
Using period trackers to escape menstrual stigma

). Introduktion til det kvalitative forskningsinterview [Introduction to the qualitative research interview]. Hans Reitzels Forlag. Lindqvist, J. (2018). New challenges to personal data processing agreements: Is the GDPR fit to deal with contract, accountability and liability in a world of the Internet of Things? International Journal of Law and Information Technology . Lupton, D. (2014). Quantified sex: A critical analysis of sexual and reproductive self-tracking using apps. Culture, Health & Sexuality: An International Journal for Research, Intervention and

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Exploring the Meaning Problem of Big and Small Data Through Digital Method Triangulation

. & Bennato, D. (2012). The open laboratory: Limits and possibilities of using Facebook, Twitter and YouTube as a research data source. Journal of Technology in Human Services , 30(3-4): 145-159. Greenberg, B. S., Eastin, M. S., Skalski, P., Cooper, L., Levy, M. & Lachlan, K. (2005). Comparing survey and diary measures of internet and traditional media use. Communication Reports , 18(1): 1-8. Halkier, B. (2010). Focus groups as social enactments: Integrating interaction and content in the analysis of focus group data. Qualitative Research , 10(71): 71

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Making Sense of Small and Big Data as Onlife Traces

Review , 40 (Special Issue 1): 159-176. Møller, K & Robards, B. (2019). Walking through, going along and scrolling back: Ephemeral mobilities in digital ethnography. Nordicom Review , 40 (Special Issue1): 95-109. Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life . Palo Alto, CA: Stanford University Press. Ofcom. (2018). Communications market report 2018 . London: Ofcom. Retrieved from https://www.ofcom.org.uk/__data/assets/pdf_file/0022/117256/CMR-2018-narrative-report.pdf Peirce, C. S. (1934

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Four Styles of Quali-Quantitative Analysis
Making sense of the new Nordic food movement on the web

/of the world system: The emergence of multi-sited ethnography. Annual Review of Anthropology , 24(1): 95-117. Marres, N. (2015). Why map issues? On controversy analysis as a digital method. Science, Technology, & Human Values , 40(5): 655-686. Marres, N. (2017). Digital sociology: The reinvention of social research . Cambridge: Polity Press. Marres, N. & Gerlitz, C. (2016). Interface methods: Renegotiating relations between digital social research, STS and sociology. Sociological Review , 64(1): 21-46. Munk, A. (2013). Techno

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