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Abstract

All mainstream social media platforms now use algorithms to display recommended content, and some (e.g. Instagram, LinkedIn) have started showing what we call algorithmic transparency cues about why certain posts are recommended. However, little is known about what cues users see on their own feeds and how they experience them. Thus, using an online survey (N = 515) of adult U.S. social media users, we gathered data about two research questions: (1) What types of algorithmic cues users find in their own feeds, and (2) their experiences with algorithms and their transparency. Content analysis of user-submitted screenshots and cue descriptions shows that most transparency cues refer to users’ behaviors, behaviors of others in their network, and sponsored posts. Furthermore, open-ended responses indicate that users have critical opinions about algorithms, calling for greater algorithmic transparency on social media, and offering suggestions for researchers and platform designers moving forward.

Keywords

Algorithmic cues algorithmic literacy algorithmic transparency content analysis social media

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References

Adadi

Berrada

(2018) Peeking inside the Black-Box: a survey on explainable artificial intelligence (XAI). IEEE Access 6: 52138–52160.

Adisa

(2023) Everything you need to know about social media algorithms. Sprout Social, 30 October. Available at: https://sproutsocial.com/insights/social-media-algorithms/

Alavi

Iyer

Bright

(2024) Advertisement avoidance and algorithmic media: the role of social media fatigue, algorithmic literacy and privacy concerns. Journal of Digital & Social Media Marketing 12(3): 276–292.

Alvarado

Heuer

Vanden Abeele

, et al (2020) Middle-aged video consumers’ beliefs about algorithmic recommendations on YouTube. Proceedings of the ACM on Human-Computer Interaction 4(CSCW2): 1–24.

Braun

Clarke

(2006) Using thematic analysis in psychology. Qualitative Research in Psychology 3(2): 77–101.

Bucher

(2017) The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information Communication and Society 20(1): 30–44.

Büchi

Fosch-Villaronga

Lutz

, et al (2023) Making sense of algorithmic profiling: user perceptions on Facebook. Information, Communication & Society 26(4): 809–825.

Dedezade

(2025) Meta Boycott and TikTok ban could signal social media’s transformation. Forbes, 21 January. Available at: https://www.forbes.com/sites/esatdedezade/2025/01/21/meta-boycott-and-tiktok-ban-could-signal-social-medias-transformation/

DeVito

(2021) Adaptive folk theorization as a path to algorithmic literacy on changing platforms. Proceedings of the ACM Conference on Human-Computer Interaction 5(CSCW2): 339.

10.

DeVito

Gergle

Birnholtz

(2017) “Algorithms ruin everything”: #RIPTwitter, folk theories, and resistance to algorithmic change in social media. In: Proceedings of the conference on human factors in computing systems, Denver, CO, 6–11 May, pp. 3163–3174. New York: Association for Computing Machinery.

11.

Di Placido

(2024) The X (Twitter) Exodus to Bluesky, Explained. Forbes, 20 November. Available at: https://www.forbes.com/sites/danidiplacido/2024/11/19/the-x-twitter-exodus-to-bluesky-explained/

12.

Diakopoulos

(2016) Accountability in algorithmic decision making. Communications of the ACM 59(2): 56–62.

13.

Dietvorst

Simmons

Massey

(2015) Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General 144(1): 114–126.

14.

Dietvorst

Simmons

Massey

(2018) Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them. Management Science 64(3): 1155–1170.

15.

Draper

Pieter Hoffmann

Lutz

, et al (2024) Privacy resignation, apathy, and cynicism: introduction to a special theme. Big Data & Society 11(3): 20539517241270663.

16.

(2023) Personalization, echo chambers, news literacy, and algorithmic literacy: a qualitative study of AI-powered news app users. Journal of Broadcasting & Electronic Media 67(3): 246–273.

17.

Eslami

Karahalios

Sandvig

, et al (2016) First I “like” it, then I hide it: folk theories of social feeds. In: Proceedings of the conference on human factors in computing systems, San Jose, CA, 7–12 May, pp. 2371–2382. New York: Association for Computing Machinery.

18.

Felzmann

Fosch-Villaronga

Lutz

, et al (2020) Towards transparency by design for artificial intelligence. Science and Engineering Ethics 26(6): 3333–3361.

19.

Graber

(2023) Algorithmic choice. Bluesky Blog, 30 March. Available at: https://bsky.social/about/blog/3-30-2023-algorithmic-choice

20.

Hamilton

Karahalios

Sandvig

, et al (2014) A path to understanding the effects of algorithm awareness. In: Extended abstracts on human factors in computing systems, Toronto, ON, Canada, PP. 631–642. New York: Association for Computing Machinery.

21.

Hargittai

(2009) An update on survey measures of web-oriented digital literacy. Social Science Computer Review 27(1): 130–137.

22.

Hargittai

Marwick

(2016) “What can I really do?” Explaining the privacy paradox with online apathy. International Journal of Communication 10: 3737–3757.

23.

Hartley

Schwartz

(2020) Trust, disconnection, minimizing risk and apathy: a compass of coping tactics in datafied everyday lives. MedieKultur: Journal of Media and Communication Research 36(69): 11–28.

24.

Hayes

Krippendorff

(2007) Answering the call for a standard reliability measure for coding data. Communication Methods and Measures 1: 77–89.

25.

Hoffmann

Lutz

Ranzini

(2016) Privacy cynicism: a new approach to the privacy paradox. Cyberpsychology: Journal of Psychosocial Research on Cyberspace 10(4): 7.

26.

Hou

YT-Y

Jung

(2021) Who is the expert? Reconciling algorithm aversion and algorithm appreciation in AI-supported decision making. Proceedings of the ACM on Human-Computer Interaction 5(CSCW2): 1–25.

27.

Kappeler

Festic

Latzer

(2023) Dataveillance imaginaries and their role in chilling effects online. International Journal of Human-Computer Studies 179: 103120.

28.

Karizat

Delmonaco

Eslami

, et al (2021) Algorithmic folk theories and identity: how TikTok users co-produce knowledge of identity and engage in algorithmic resistance. Proceedings of the ACM on Human-Computer Interaction 5(CSCW2): 1–44.

29.

Kennedy

Tyson

Saks

(2023) Public awareness of artificial intelligence in everyday activities. Pew Research Center Science & Society. Available at: https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/

30.

Kitchin

(2017) Thinking critically about and researching algorithms. Information Communication and Society 20(1): 14–29.

31.

Klug

Qin

Evans

, et al (2021) Trick and please. A mixed-method study on user assumptions about the TikTok algorithm. In: ACM international conference proceeding series, Virtual Event, 21–25 June, pp. 84–92. New York: Association for Computing Machinery.

32.

Kolkman

(2022) The (in)credibility of algorithmic models to non-experts. Information, Communication & Society 25(1): 93–109.

33.

Lakkaraju

Kamar

Caruana

, et al (2019) Faithful and customizable explanations of black box models. In: Proceedings of the 2019 AAAI/ACM conference on AI, ethics, and society, Honolulu, HI, 27–28 January, pp. 131–138. New York: Association for Computing Machinery.

34.

Larsson

Heintz

(2020) Transparency in artificial intelligence. Internet Policy Review 9(2): 1–16.

35.

Logg

Minson

Moore

(2019) Algorithm appreciation: people prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151: 90–103.

36.

Meta (2023) Our approach to Facebook feed ranking. Transparency Center. Available at: https://transparency.fb.com/features/ranking-and-content/

37.

Morrow

Swire-Thompson

Polny

, et al (2022) The emerging science of content labeling: contextualizing social media content moderation. Journal of the Association for Information Science and Technology 73(10): 1365–1386.

38.

Oeldorf-Hirsch

Neubaum

(2023) What do we know about algorithmic literacy? The status quo and a research agenda for a growing field. New Media & Society 27: 681–701.

39.

Otis

(2024) The effects of transparency cues on news source credibility online: an investigation of “opinion labels.” Journalism 25(1): 198–217.

40.

Pew Research Center (2024) Social media fact sheet. Available at: https://www.pewresearch.org/internet/fact-sheet/social-media/

41.

Potter

(2010) The state of media literacy. Journal of Broadcasting & Electronic Media 54(4): 675–696.

42.

Rader

Gray

(2015) Understanding user beliefs about algorithmic curation in the Facebook news feed. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, Seoul Republic of Korea, 18–23 April, pp. 173–182. New York: Association for Computing Machinery.

43.

Rader

Cotter

Cho

(2018) Explanations as mechanisms for supporting algorithmic transparency. In: Proceedings of the 2018 CHI conference on human factors in computing systems, Montreal, QC, Canada, 21–26 April, pp. 1–13. New York: Association for Computing Machinery.

44.

Schellewald

(2022) Theorizing “stories about algorithms” as a mechanism in the formation and maintenance of algorithmic imaginaries. Social Media + Society 8(1): 20563051221077025.

45.

Sethuraman

(2019) Why am I seeing this? We have an answer for you. Meta, 31 March. Available at: https://about.fb.com/news/2019/03/why-am-i-seeing-this/

46.

Siles

Meléndez-Moran

(2021) “The most aggressive of algorithms”: user awareness of and attachment to TikTok’s content personalization. In: Paper presented at the 71st annual conference of the international communication association (ICA), Denver, CO, 27–31 May.

47.

Siles

Segura-Castillo

Solís

, et al (2020) Folk theories of algorithmic recommendations on Spotify: enacting data assemblages in the global South. Big Data & Society 7(1): 2053951720923377.

48.

Simpson

Hamann

Semaan

(2022) How to tame “your” algorithm: LGBTQ + users’ domestication of TikTok. Proceedings of the ACM on Human-Computer Interaction 6(GROUP): 1–27.

49.

Smith

(2021) How TikTok reads your mind. The New York Times, 6 December. Available at: https://www.nytimes.com/2021/12/05/business/media/tiktok-algorithm.html

50.

Strycharz

Segijn

(2024a) Chilling effects as a result of corporate surveillance in digital communication: a comparison between American and Dutch media users. International Journal of Communication 18: 320–343.

51.

Strycharz

Segijn

(2024b) Ethical side-effect of dataveillance in advertising: impact of data collection, trust, privacy concerns and regulatory differences on chilling effects. Journal of Business Research 173: 114490.

52.

Taddicken

(2014) The “privacy paradox” in the social web: the impact of privacy concerns, individual characteristics, and the perceived social relevance on different forms of self-disclosure. Journal of Computer-Mediated Communication 19(2): 248–273.

53.

TikTok (2020) How TikTok recommends videos #Foryou. Newsroom| Tiktok, 18 June. Available at: https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you

54.

U.S. Census Bureau (2021a) Bachelor’s degree attainment in the United States: 2005 to 2019, 9 February. Available at: https://www.census.gov/library/publications/2021/acs/acsbr-009.html

55.

U.S. Census Bureau (2021b) Race and ethnicity in the United States: 2010 census and 2020 census, 12 August. Available at: https://www.census.gov/library/visualizations/interactive/race-and-ethnicity-in-the-united-state-2010-and-2020-census.html

56.

Willson

(2017) Algorithms (and the) everyday. Information, Communication & Society 20(1): 137–150.

57.

X (2024) For you home timeline recommendations. Help Center. Available at: https://help.x.com/en/resources/recommender-systems/for-you-home-timeline-recommendations

58.

Ytre-Arne

Moe

(2021) Folk theories of algorithms: understanding digital irritation. Media, Culture and Society 43(5): 807–824.

59.

Zerilli

Knott

Maclaurin

, et al (2019) Transparency in algorithmic and human decision-making: is there a double standard? Philosophy & Technology 32(4): 661–683.

60.

Zhang

Boerman

Hendriks

, et al (2024) “They know everything”: folk theories, thoughts, and feelings about dataveillance in media technologies. International Journal of Communication 18: 2710–2730.

Exploring social media users’ experiences with algorithmic transparency cues

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