References
- Ananny, M., & Crawford, K. (2018). Seeing without knowing. Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/https://doi.org/10.1177/1461444816676645
- Bakshy, E., Messing, S., & Adamic, L. A. (2015). Political science. Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/https://doi.org/10.1126/science.aaa1160
- Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1–13. https://doi.org/https://doi.org/10.1080/1369118X.2016.1216147
- Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online Behavioral advertising: A literature Review and research Agenda. Journal of Advertising, 46(3), 363–376. https://doi.org/https://doi.org/10.1080/00913367.2017.1339368
- Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15(3), 209–227. https://doi.org/https://doi.org/10.1007/s10676-013-9321-6
- Bucher, T. (2012). Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media & Society, 14(7), 1164–1180. https://doi.org/https://doi.org/10.1177/1461444812440159
- Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30–44. https://doi.org/https://doi.org/10.1080/1369118X.2016.1154086
- Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), https://doi.org/https://doi.org/10.1177/2053951715622512
- Christin, A. (2017). Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society, 4(2), Article 205395171771885. https://doi.org/https://doi.org/10.1177/2053951717718855
- Cotter, K., & Reisdorf, B. C. (2020). Algorithmic knowledge Gaps: A New Dimension of (digital) Inequality. International Journal of Communication (IJoC), 2020(14), 745–765.
- Danaher, J. (2019). The Ethics of algorithmic outsourcing in everyday life (pp. 98–118). In K. Yeung & M. Lodge (Eds.), Algorithmic regulation. Oxford University Press.
- Davison, W. P. (1983). The third-person effect in communication. Public Opinion Quarterly, 47(1), 1. https://doi.org/https://doi.org/10.1086/268763
- DeVito, M. A. (2017). From Editors to algorithms. Digital Journalism, 5(6), 753–773. https://doi.org/https://doi.org/10.1080/21670811.2016.1178592
- Diakopoulos, N., & Koliska, M. (2017). Algorithmic transparency in the news media. Digital Journalism, 5(7), 809–828. https://doi.org/https://doi.org/10.1080/21670811.2016.1208053
- Drunen, M. Z., Helberger, N., & Bastian, M. (2019). Know your algorithm: What media organizations need to explain to their users about news personalization. International Data Privacy Law, 9(4), 220–235. https://doi.org/https://doi.org/10.1093/idpl/ipz011
- Ekstrand, M. D., Harper, F. M., Willemsen, M. C., & Konstan, J. A. (2014). User perception of differences in recommender algorithms. In A. Kobsa, M. Zhou, M. Ester, & Y. Koren (Chairs), the 8th ACM Conference, Foster City, Silicon Valley, California, USA.
- Ekstrand, M. D., Kluver, D., Harper, F. M., & Konstan, J. A. (2015). Letting Users Choose Recommender Algorithms. In H. Werthner, M. Zanker, J. Golbeck, & G. Semeraro (Chairs), the 9th ACM Conference, Vienna, Austria.
- Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., … Sandvig, C. (2015). I always assumed that I wasn't really that close to [her]. In B. Begole, J. Kim, K. Inkpen, & W. Woo (Chairs), the 33rd Annual ACM Conference, Seoul, Republic of Korea.
- Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298–320. https://doi.org/https://doi.org/10.1093/poq/nfw006
- Gal, M. S. (2018). Algorithmic challenges to autonomous Choice. Michigan Technology Law Review, 59(2018), https://doi.org/https://doi.org/10.2139/ssrn.2971456
- Gillespie, T. (2014). Relevance of algorithms. In T. Gillespie, P. J. Boczkowski, & K. A. Foot (Eds.), Inside Technology. Media Technologies: Essays on communication, Materiality, and society (pp. 167–194). The MIT Press.
- Gorwa, R., & Ash, T. G. (2020). Democratic transparency in the platform society (pp. 286-312). In N. Persily & J. Tucker (Eds.), Social media and Democracy: The state of the field. Cambridge University Press.
- Gran, A.-B., Booth, P., & Bucher, T. (2020). To be or not to be algorithm aware: a question of a new digital divide? Information, Communication & Society. https://doi.org/https://doi.org/10.1080/1369118X.2020.1713846.
- Grzymek, V., & Puntschuh, M. (2019). What Europe knows and thinks about algorithms. Results of a representative survey. Bertelsmann Foundation. Available online: https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/what-europe-knows-and-thinks-about-algorithms/
- Haim, M., Graefe, A., & Brosius, H.-B. (2018). Burst of the filter Bubble? Digital Journalism, 6(3), 330–343. https://doi.org/https://doi.org/10.1080/21670811.2017.1338145
- Hargittai, E., Gruber, J., Djukaric, T., Fuchs, J., & Brombach, L. (2020). Black box measures? How to study people’s algorithm skills. Information, Communication & Society, Online First. https://doi.org/https://doi.org/10.1080/1369118X.2020.1713846
- Helberger, N., Karppinen, K., & D’Acunto, L. (2018). Exposure diversity as a design principle for recommender systems. Information, Communication & Society, 21(2), 191–207. https://doi.org/https://doi.org/10.1080/1369118X.2016.1271900
- Just, N., & Latzer, M. (2017). Governance by algorithms: Reality construction by algorithmic selection on the internet. Media, Culture & Society, 39(2), 238–258. https://doi.org/https://doi.org/10.1177/0163443716643157
- Katzenbach, C., & Ulbricht, L. (2019). Algorithmic governance. Internet Policy Review, 8(4), https://doi.org/https://doi.org/10.14763/2019.4.1424
- Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. https://doi.org/https://doi.org/10.1080/1369118X.2016.1154087
- Latzer, M., Hollnbuchner, K., Just, N., & Saurwein, F. (2016). The Economics of algorithmic selection on the internet. In J. M. Bauer, & M. Latzer (Eds.), Handbook on the economics of the internet (pp. 395–424). Edward Elgar Publishing.
- Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), https://doi.org/https://doi.org/10.1177/2053951718756684
- Leerssen, P. (2020). The Soap Box as a Black Box: Regulating Transparency in Social Media Recommender Systems. Draft Paper. https://www.academia.edu/42260593/The_Soap_Box_as_a_Black_Box_Regulating_Transparency_in_Social_Media_Recommender_Systems
- Marwick, A. E., & boyd, d. (2014). Networked privacy: How teenagers negotiate context in social media. New Media & Society, 16(7), 1051–1067. https://doi.org/https://doi.org/10.1177/1461444814543995
- Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), Article 205395171667967. https://doi.org/https://doi.org/10.1177/2053951716679679
- Moeller, J., Trilling, D., Helberger, N., Irion, K., & de Vreese, C. (2016). Shrinking core? Exploring the differential agenda setting power of traditional and personalized news media. info, 18(6), 26–41. https://doi.org/https://doi.org/10.1108/info-05-2016-0020
- Nechushtai, E., & Lewis, S. C. (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, 90, 298–307. https://doi.org/https://doi.org/10.1016/j.chb.2018.07.043
- Neyland, D., & Möllers, N. (2017). Algorithmic IF … THEN rules and the conditions and consequences of power. Information, Communication & Society, 20(1), 45–62. https://doi.org/https://doi.org/10.1080/1369118X.2016.1156141
- Pasquale, F. (2015). The black Box society: The Secret algorithms that control Money and information. Harvard University Press. https://doi.org/https://doi.org/10.4159/harvard.9780674736061
- Prey, R. (2017). Nothing personal: Algorithmic individuation on music streaming platforms. Media, Culture & Society, 10(1). https://doi.org/https://doi.org/10.1177/0163443717745147
- Rader, E., & Gray, R. (2015). Understanding user Beliefs about algorithmic curation in the Facebook news Feed. Proceedings of the 33rd Annual ACM Conference, 173–182. https://doi.org/https://doi.org/10.1145/2702123.2702174
- Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: Research methods for Detecting discrimination on internet platforms. Paper presented at the Annual Meeting of the International communication Association, Seattle, WA, May 22, 2014.
- Shorey, S., & Howard, P. (2016). Automation, Big data, and Politics: A research Review. International Journal of Communication, 10, 5032–5055.
- Willson, M. (2017). Algorithms (and the) everyday. Information, Communication & Society, 20(1), 137–150. https://doi.org/https://doi.org/10.1080/1369118X.2016.1200645
- Ziewitz, M. (2016). Governing algorithms. Science, Technology, & Human Values, 41(1), 3–16. https://doi.org/https://doi.org/10.1177/0162243915608948