Background reading

Why Researchers Want Broader Access to Social Media Data

What could journalists and social scientists shed light on if they had a better view of the digital world?

Teresa Carr, Undark, 2022 - Read on Undark

This article is a little dated but provides a brief background on why we need new approaches and tools for studying digital platforms with some examples from the US.

Further reading

A selection of recent publications on data donation research methods and infrastructure

Please see the Australian Internet Observatory public Zotero library for a more extensive list of publications and links to resources.

Abdo, A et al. 2022, A Safe Harbor for Platform Research, Knight First Amendment Institute, Columbia University, Ney York, USA, viewed 16 March 2023, <http://knightcolumbia.org/content/a-safe-harbor-for-platform-research>.

ACOLA 2022, Australia’s Data-Enabled Research Future: Humanities, Australian Council of Learned Academies, <https://humanities.org.au/wp-content/uploads/2022/06/Australias-Data-Enabled-Research-Future-Humanities.pdf>.

ADM+S 2024, The Australian Ad Observatory Technical and Data Report, <https://www.admscentre.org.au/files/ADM+S%20Working%20Paper%20Series%2009_Ad%20Observatory%20Technical%20Paper.pdf>.

Alba, D 2022, ‘Meta Pulls Support for Tool Used to Keep Misinformation in Check’, Bloomberg, viewed 27 April 2023, <https://www.bloomberg.com/news/articles/2022-06-23/meta-pulls-support-for-tool-used-to-keep-misinformation-in-check>.

Angus, D et al. 2024, The Australian Ad Observatory Technical and Data Report, ARC Centre of Excellence for Automated Decision-Making and Society, Carlton, Vic, viewed 2 April 2024, <https://www.admscentre.org.au/files/ADM+S%20Working%20Paper%20Series%2009_Ad%20Observatory%20Technical%20Paper.pdf>.

Aneesh, P et al. 2021, Technical methods for regulatory inspection of algorithmic systems, Ada Lovelace Institute, London, UK, viewed 18 January 2023, <https://www.adalovelaceinstitute.org/report/technical-methods-regulatory-inspection/>.

Anzt, H et al. 2021, ‘An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action’, F1000Research, viewed 28 April 2023, <https://doi.org/10.12688/f1000research.23224.2>.

Araujo, T et al. 2022, ‘OSD2F: An Open-Source Data Donation Framework’, Computational Communication Research, vol. 4, no. 2, pp. 372–387, viewed 27 April 2023, <https://www.aup-online.com/content/journals/10.5117/CCR2022.2.001.ARAU>.

Atske, S 2021, ‘The Future of Digital Spaces and Their Role in Democracy’, Pew Research Center: Internet, Science & Tech, viewed 25 May 2023, <https://www.pewresearch.org/internet/2021/11/22/the-future-of-digital-spaces-and-their-role-in-democracy/>.

Atteveldt, W van et al. 2019, ‘A Roadmap for Computational Communication Research’, Computational Communication Research, vol. 1, no. 1, pp. 1–11, viewed 9 January 2023, <https://computationalcommunication.org/ccr/article/view/37>.

Ball, MP et al. 2014, ‘Harvard Personal Genome Project: lessons from participatory public research’, Genome Medicine, vol. 6, no. 2, p. 10, viewed 12 April 2023, <https://doi.org/10.1186/gm527>.

Ben-David, A 2020, ‘Counter-archiving Facebook’, European Journal of Communication, vol. 35, no. 3, pp. 249–264, viewed 16 March 2023, <https://doi.org/10.1177/0267323120922069>.

Bietz, M, Patrick, K, & Bloss, C 2019, ‘Data Donation as a Model for Citizen Science Health Research’, Citizen Science: Theory and Practice, vol. 4, no. 1, p. 6, viewed 8 July 2022, <http://theoryandpractice.citizenscienceassociation.org/articles/10.5334/cstp.178/>.

Boeschoten, L et al. 2023, ‘Port: A software tool for digital data donation’, Journal of Open Source Software, vol. 8, no. 90, p. 5596, viewed 4 January 2024, <https://joss.theoj.org/papers/10.21105/joss.05596>.

Botelho, A 2023, ‘Lab notebook: improving topic modeling for digital political ads’, Cybersecurity for Democracy, viewed 16 March 2023, <https://medium.com/cybersecurity-for-democracy/lab-notebook-improving-topic-modeling-for-digital-political-ads-15e5adf5d6a>.

Bradshaw, S & Barrett, B 2022, Civil Society Organizations’ Data, Access, and Tooling Needs for Social Media Research, Information Environment, <https://informationenvironment.org/wp-content/uploads/2022/09/RP5-Civil-Society-Organizations-Data-Access-and-Tooling-Needs-for-Social-Media-Research.pdf>.

Bruns, A 2019, ‘After the “APIcalypse”: social media platforms and their fight against critical scholarly research’, Information, Communication & Society, vol. 22, no. 11, pp. 1544–1566, viewed 8 July 2022, <https://doi.org/10.1080/1369118X.2019.1637447>.

Bruns, A 2022, Australian Search Experience Project: Background Paper, ARC Centre of Excellence for Automated Decision-Making and Society, Melbourne, viewed 4 May 2022, <https://apo.org.au/node/316976>.

Burgess, J et al. 2021, Critical simulation as hybrid digital method for exploring the data operations and vernacular cultures of visual social media platforms, SocArXiv, viewed 30 March 2022, <https://osf.io/2cwsu>.

Burgess, J, Andrejevic, M, et al. 2022, Australian Ad Observatory: background paper, ARC Centre of Excellence for Automated Decision-Making and Society, viewed 11 October 2022, <https://apo.org.au/node/318616>.

Burgess, J, Matamoros-Fernandes, A, & Bartolo, L 2022, Recommender systems and algorithms – position statement, eSafety Commissioner, <https://www.esafety.gov.au/industry/tech-trends-and-challenges/recommender-systems-and-algorithms>.

Carnegie 2022, Institute for Research on the Information Environment (IRIE): brochure, Carnegie Endowment for International Peace, <https://carnegieendowment.org/irie>.

Carrière, TC et al. 2023, Best practices in data donation: A workflow for studies using digital data donation, Open Science Framework, viewed 4 January 2024, <https://osf.io/3vhbj>.

Data Donation Lab 2023, Data Donation Symposium 2023, Data Donation Lab, <https://datadonation.uzh.ch/en/symposium-2023/>.

Delgado, F et al. 2023,‘The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice’, in, Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, EAAMO ’23, Association for Computing Machinery, New York, NY, USA, pp.1–23, viewed 4 December 2023, <https://dl.acm.org/doi/10.1145/3617694.3623261>.

DESE 2022, 2021 National Research Infrastructure Roadmap, Department of Education, Skills and Employment, Australian Government, <https://www.dese.gov.au/national-research-infrastructure/resources/2021-national-research-infrastructure-roadmap>.

EC 2020, Towards a European strategy on business-to-government data sharing for the public interest: Final Report, High Level Expert Group on Business to Government Data Sharing, European Commission, LU, viewed 26 April 2023, <https://data.europa.eu/doi/10.2759/406717>.

EC 2022, The Digital Services Act package | Shaping Europe’s digital future, European Commission, Brussels, viewed 30 November 2022, <https://digital-strategy.ec.europa.eu/en/policies/digital-services-act-package>.

Edelson, L & McCoy, D 2021, ‘We Research Misinformation on Facebook. It Just Disabled Our Accounts.’, The New York Times, viewed 16 March 2023, <https://www.nytimes.com/2021/08/10/opinion/facebook-misinformation.html>.

Flew, T & Su, C 2022, Mapping International Enquiries into the Power of Digital Platforms, Zenodo, viewed 27 August 2022, <https://zenodo.org/record/5913069>.

Fukuyama, F et al. 2020, Report of the Working Group on Platform Scale, Stanford Cyber Policy Center, Stanford, California, viewed 18 January 2023, <https://cyber.fsi.stanford.edu/publication/report-working-group-platform-scale>.

Garibay, O et al. 2023, ‘Six Human-Centered Artificial Intelligence Grand Challenges’, International Journal of Human–Computer Interaction, vol. 39, no. 3, pp. 391–437, viewed 5 April 2023, <https://doi.org/10.1080/10447318.2022.2153320>.

Gomez Ortega, A et al. 2023, ‘Beyond data transactions: a framework for meaningfully informed data donation’, AI & SOCIETY, viewed 17 November 2023, <https://doi.org/10.1007/s00146-023-01755-5>.

Goodman, EP & Trehu, J 2022, AI Audit-Washing and Accountability | Strengthening Transatlantic Cooperation, German Marshall Fund, viewed 27 April 2023, <https://www.gmfus.org/news/ai-audit-washing-and-accountability>.

Haim, M, Graefe, A, & Brosius, H-B 2018, ‘Burst of the Filter Bubble?: Effects of personalization on the diversity of Google News’, Digital Journalism, vol. 6, no. 3, pp. 330–343, viewed 26 April 2023, <https://www.tandfonline.com/doi/full/10.1080/21670811.2017.1338145>.

Hart, AG et al. 2022, ‘Understanding Engagement, Marketing, and Motivation to Benefit Recruitment and Retention in Citizen Science’, Citizen Science: Theory and Practice, vol. 7, no. 1, p. 5, viewed 12 April 2023, <http://theoryandpractice.citizenscienceassociation.org/articles/10.5334/cstp.436/>.

Kloppenborg, K, Price Ball, M, & Greshake Tzovaras, B 2022, ‘A Peer Production Model for Citizen Science: Comparative Analysis of Three Online Platforms’, viewed 12 April 2023, <https://papers.ssrn.com/abstract=4314974>.

Krafft, TD, Gamer, M, & Zweig, KA 2019, ‘What did you see? A study to measure personalization in Google’s search engine’, EPJ Data Science, vol. 8, no. 1, p. 38, viewed 1 May 2023, <https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-019-0217-5>.

Lazer, D et al. 2021, ‘Meaningful measures of human society in the twenty-first century’, Nature, vol. 595, no. 7866, pp. 189–196, viewed 24 January 2023, <https://www.nature.com/articles/s41586-021-03660-7>.

Metaxa, D et al. 2021, ‘Auditing Algorithms: Understanding Algorithmic Systems from the Outside In’, Foundations and Trends® in Human–Computer Interaction, vol. 14, no. 4, pp. 272–344, viewed 29 April 2023, <http://www.nowpublishers.com/article/Details/HCI-083>.

NAIRR 2023, Strengthening and Democratizing the U.S. Artificial Intelligence  Innovation Ecosystem: An Implementation Plan for a  National Artificial Intelligence Research Resource, National Artificial Intelligence Research Resource Task Force, National Science Foundation, Washington, D.C., viewed 15 March 2023, <https://www.ai.gov/wp-content/uploads/2023/01/NAIRR-TF-Final-Report-2023.pdf>.

Nechushtai, E & Lewis, SC 2019, ‘What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations’, Computers in Human Behavior, vol. 90, pp. 298–307, viewed 26 April 2023, <https://linkinghub.elsevier.com/retrieve/pii/S0747563218303650>.

Ohme, J et al. 2021, ‘Mobile data donations: Assessing self-report accuracy and sample biases with the iOS Screen Time function’, Mobile Media & Communication, vol. 9, no. 2, pp. 293–313, viewed 14 October 2022, <https://doi.org/10.1177/2050157920959106>.

Ohme, J & Araujo, T 2022, ‘Digital data donations: A quest for best practices’, Patterns, vol. 3, no. 4, p. 100467, viewed 8 July 2022, <https://www.sciencedirect.com/science/article/pii/S2666389922000472>.

Papakyriakopoulos, O et al. 2022,‘How Algorithms Shape the Distribution of Political Advertising: Case Studies of Facebook, Google, and TikTok’, in, Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’22: AAAI/ACM Conference on AI, Ethics, and Society, ACM, Oxford United Kingdom, pp.532–546, viewed 26 April 2023, <https://dl.acm.org/doi/10.1145/3514094.3534166>.

Pasquale, F 2015, The Black Box Society, Harvard University Press, Cambridge, MA, viewed 24 November 2021, <https://www.hup.harvard.edu/catalog.php?isbn=9780674970847>.

Pisharodył, N & Reynolds, JR 2022, Current Academic Research on the Information Environment, Institute for Research on the Information Envrionment (IRIE), <https://informationenvironment.org/wp-content/uploads/2022/10/RP1-Current-Academic-Research-on-the-Information-Environment-1.pdf>.

Prainsack, B 2019,‘Data Donation: How to Resist the iLeviathan’, in J Krutzinna & L Floridi (eds.), The Ethics of Medical Data Donation, Philosophical Studies Series, Springer International Publishing, Cham, pp.9–22, viewed 23 June 2022, <https://doi.org/10.1007/978-3-030-04363-6_2>.

Rieder, B & Hofmann, J 2020, ‘Towards platform observability’, Internet Policy Review, vol. 9, no. 4, viewed 2 November 2022, <https://policyreview.info/articles/analysis/towards-platform-observability>.

Sanderson, Z 2024, ‘Beyond Competition: Designing Data Portability to Support Research on the Digital Information Environment’, viewed 26 May 2024, <https://papers.ssrn.com/abstract=4739362>.

Shneiderman, B 2020, ‘Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy’, International Journal of Human–Computer Interaction, vol. 36, no. 6, pp. 495–504, viewed 5 April 2023, <https://doi.org/10.1080/10447318.2020.1741118>.

Shneiderman, B 2022, Human-centered AI, Oxford University Press, New York.

Stark, B & Stegmann, D 2020, Are Algorithms a Threat to Democracy? The Rise of Intermediaries:  A Challenge for Public Discourse, Algorithm Watch, Germany.

Tzovaras, BG et al. 2019, ‘Open Humans: A platform for participant-centered research and personal data exploration’, GigaScience, vol. 8, no. 6, p. giz076, viewed 12 April 2023, <https://doi.org/10.1093/gigascience/giz076>.

Vasse’i, RM & McCrosky, J 2023, AI Transparency in Practice, Mozilla Foundation, viewed 7 April 2023, <https://foundation.mozilla.org/en/research/library/ai-transparency-in-practice/ai-transparency-in-practice/>.

Wanless, A & Shapiro, JN 2022, A CERN Model for Studying the Information Environment, Carnegie Endowment for International Peace, Washington, DC, viewed 28 November 2022, <https://carnegieendowment.org/2022/11/17/cern-model-for-studying-information-environment-pub-88408>.