The ubiquity of digital devices and the increasing intensity of users’ interactions with them create vast amounts of digital trace data. Companies use these data to optimize their services or products, but these data are also of interest to researchers studying human behavior. As most of these data are owned by private companies and their collection requires adherence to their terms of service, research with digital trace data often entails some form of public-private partnership. Private companies and academic researchers each have their own interests, some of which are shared, while others may conflict. In this article, we explore different types of private-public partnerships for research with digital trace data. Based on general considerations and particular experiences from a research project with linked digital trace data, we propose strategies for identifying and productively negotiating both shared and conflicting interests in these relationships.
Al BaghalTSloanLJessopC, et al. (2019) Linking Twitter and survey data: the impact of survey mode and demographics on consent rates across three UK studies. Social Science Computer Review38(5): 517–532.
2.
AlaimoCKallinikosJ (2017) Computing the everyday: social media as data platforms. The Information Society33(4): 175–191.
3.
AraujoTWonnebergerANeijensP, et al. (2017) How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use. Communication Methods and Measures11(3): 173–190.
BishopELGrayD (2017) Ethical challenges of publishing and sharing social media research data. In: WoodfieldK (ed.) The Ethics of Online Research. Bingley: Emerald Publishing, pp. 159–187.
6.
BorgmanCL (2012) The conundrum of sharing research data. Journal of the American Society for Information Science and Technology63(6): 1059–1078.
7.
boyddCrawfordK (2012) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society15(5): 662–679.
BurkeMKrautRE (2016) The relationship between Facebook use and well-being depends on communication type and tie strength: Facebook and well-being. Journal of Computer-Mediated Communication21(4): 265–281.
10.
CouldryNYuJ (2018) Deconstructing datafication’s brave new world. New Media & Society20(12): 4473–4491.
11.
DehnhardIWeichselgartnerEKrampenG (2013) Researcher’s willingness to submit data for data sharing: a case study on a data archive for psychology. Data Science Journal12: 172–180.
12.
EllisSELeekJT (2018) How to share data for collaboration. The American Statistician72: 53–57.
13.
FafaliosPIosifidisVNtoutsiE, et al. (2018) TweetsKB: a public and large-scale RDF corpus of annotated tweets. In: GangemiANavigliRVidalM-E, et al. (eds) The Semantic Web. Cham: Springer, 2018, pp. 177–190.
14.
FecherBFriesikeSHebingM (2015) What drives academic data sharing? (ed PhillipsRS). PLoS ONE10(2): e0118053.
15.
FlaxmanSGoelSRaoJM (2016) Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly80(S1): 298–320.
16.
FreelonD (2018) Computational research in the post-API age. Political Communication35(4): 665–668.
17.
GuessAM (2015) Measure for measure: an experimental test of online political media exposure. Political Analysis23(1): 59–75.
18.
HaimMNienierzaA (2019) Computational observation: challenges and opportunities of automated observation within algorithmically curated media environments using a browser plug-in. Preprint SocArXiv. DOI: 10.31235/osf.io/xd63n.
19.
HalavaisA (2019) Overcoming terms of service: a proposal for ethical distributed research. Information, Communication & Society22(11): 1567–1581.
20.
HemphillLLeonardSHHedstromM (2018) Developing a social media archive at ICPSR. In: Proceedings of web archiving and digital libraries (WADL’18), Fort Worth, TX, 18 June 2018, article 4. New York: ACM.
21.
HoutkoopBLChambersCMacleodM, et al. (2018) Data sharing in psychology: a survey on barriers and preconditions. Advances in Methods and Practices in Psychological Science1(1): 70–85.
22.
HuttonLHendersonT (2015) “I didn’t sign up for this!” Informed consent in social network research. In: Proceedings of the ninth international AAAI conference on web and social media (ICWSM), Oxford, 26–29 May, pp. 178–187. Palo Alto, CA: The AAAI Press.
Kinder-KurlandaKWellerKZenk-MöltgenW, et al. (2017) Archiving information from geotagged tweets to promote reproducibility and comparability in social media research. Big Data & Society4(2). DOI: 10.1177/2053951717736336.
25.
KingG (1995) Replication, replication. PS: Political Science and Politics28(3): 444.
26.
KingGPersilyN (2018) A new model for industry-academic partnerships. Working paper. Available at: https://gking.harvard.edu/partnerships (accessed 30 October 2018).
27.
KleinOHardwickeTEAustF, et al. (2018) A practical guide for transparency in psychological science. Collabra: Psychology4(1): 20.
28.
LeBelEPMcCarthyRJEarpBD, et al. (2018) A unified framework to quantify the credibility of scientific findings. Advances in Methods and Practices in Psychological Science1(3): 389–402.
29.
LomborgS (2013) Personal internet archives and ethics. Research Ethics9(1): 20–31.
30.
LomborgSBechmannA (2014) Using APIs for data collection on social media. The Information Society30(4): 256–265.
31.
LundbergINarayananALevyK, et al. (2018) Privacy, ethics, and data access: a case study of the fragile families challenge. arXiv preprint. Available at: https://arxiv.org/abs/1809.00103 (accessed 30 October 2018).
32.
McKeeHAPorterJE (2009) The Ethics of Internet Research: A Rhetorical, Case-based Process. New York: Peter Lang.
33.
MannheimerSHullEA (2017) Sharing selves: developing an ethical framework for curating social media data. International Journal of Digital Curation12(9): 1–15.
34.
MarkhamA (2012) Fabrication as ethical practice. Information, Communication & Society15(3): 334–353.
35.
MarkhamABuchananE (2012) Ethical decision-making and internet research: recommendations from the AoIR ethics working committee (Version 2.0). Available at: https://aoir.org/reports/ethics2.pdf (accessed 30 October 2018).
36.
MatzSCKosinskiMNaveG, et al. (2017) Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences of the United States of America114(48): 12714–12719.
PasekJMcClainCANewportF, et al. (2019) Who’s tweeting about the president? What big survey data can tell us about digital traces?Social Science Computer Review38(5): 633–650.
39.
PerkmannMWalshK (2007) University–industry relationships and open innovation: towards a research agenda. International Journal of Management Reviews9(4): 259–280.
40.
Poyago-TheotokyJBeathJSiegelDS (2002) Universities and fundamental research: reflections on the growth of university–industry partnerships. Oxford Review of Economic Policy18(1): 10–21.
41.
RuckensteinMPantzarM (2017) Beyond the quantified self: thematic exploration of a dataistic paradigm. New Media & Society19(3): 401–418.
42.
RuthsDPfefferJ (2014) Social media for large studies of behavior. Science346(6213): 1063–1064.
43.
ScharkowM (2016) The accuracy of self—reported internet use —a validation study using client log data. Communication Methods and Measures10(1): 13–27.
44.
SharonTZandbergenD (2017) From data fetishism to quantifying selves: self-tracking practices and the other values of data. New Media & Society19(11): 1695–1709.
VaccariCValerianiABarberáP, et al. (2016) Of echo chambers and contrarian clubs: exposure to political disagreement among German and Italian users of Twitter. Social Media + Society2(3): 1–24.
49.
Van AtteveldtWPengTQ (2018) When communication meets computation: opportunities, challenges, and pitfalls in computational communication science. Communication Methods and Measures12(2–3): 81–92.
50.
Van den EyndenVCortiLWoollardM, et al. (2019) Managing and Sharing Research Data: A Guide to Good Practice. London: SAGE.
51.
Van DijckJ (2014) Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society12(2): 197–208.
52.
VanpaemelWVermorgenMDeriemaeckerL, et al. (2015) Are we wasting a good crisis? The availability of psychological research data after the storm. Collabra: Psychology1(1): Article 3.
53.
WellerKKinder-KurlandaK (2015) Uncovering the challenges in collection, sharing and documentation: the hidden data of social media research? In: Standards and practices in large-scale social media research: papers from the ICWSM workshop 2015. Available at: http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10657/10552 (accessed 30 October 2018).
54.
WellerKKinder-KurlandaK (2017) To share or not to share? Ethical challenges in sharing social media-based research data. In: ZimmerMKinder-KurlandaK (eds) Internet Research Ethics for the Social Age: New Challenges, Cases, and Contexts. New York: Peter Lang, pp. 115–129.
55.
WellerKKinder-KurlandaKE (2016) A manifesto for data sharing in social media research. In: Proceedings of the 8th ACM conference on web science —WebSci’16, Hannover, 22–25 May, pp. 166–172. New York: ACM Press.
56.
WilliamsMLBurnapPSloanL (2017) Towards an ethical framework for publishing Twitter data in social research: taking into account users’ views, online context and algorithmic estimation. Sociology51(6): 1149–1168.
57.
YarkoniTWestfallJ (2017) Choosing prediction over explanation in psychology: lessons from machine learning. Perspectives on Psychological Science12(6): 1100–1122.
58.
Zenk-MöltgenWAkdenizEKatsanidouA, et al. (2018) Factors influencing the data sharing behavior of researchers in sociology and political science. Journal of Documentation74(5): 1053–1073.
59.
ZuboffS (2015) Big other: surveillance capitalism and the prospects of an information civilization. Journal of Information Technology30(1): 75–89.
