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.
AlaviSIyerPBrightLF (2024) Advertisement avoidance and algorithmic media: the role of social media fatigue, algorithmic literacy and privacy concerns. Journal of Digital & Social Media Marketing12(3): 276–292.
4.
AlvaradoOHeuerHVanden AbeeleV, et al (2020) Middle-aged video consumers’ beliefs about algorithmic recommendations on YouTube. Proceedings of the ACM on Human-Computer Interaction4(CSCW2): 1–24.
5.
BraunVClarkeV (2006) Using thematic analysis in psychology. Qualitative Research in Psychology3(2): 77–101.
6.
BucherT (2017) The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information Communication and Society20(1): 30–44.
7.
BüchiMFosch-VillarongaELutzC, et al (2023) Making sense of algorithmic profiling: user perceptions on Facebook. Information, Communication & Society26(4): 809–825.
DeVitoMA (2021) Adaptive folk theorization as a path to algorithmic literacy on changing platforms. Proceedings of the ACM Conference on Human-Computer Interaction5(CSCW2): 339.
10.
DeVitoMAGergleDBirnholtzJ (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, 6–11May, pp. 3163–3174. New York: Association for Computing Machinery.
DiakopoulosN (2016) Accountability in algorithmic decision making. Communications of the ACM59(2): 56–62.
13.
DietvorstBJSimmonsJPMasseyC (2015) Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General144(1): 114–126.
14.
DietvorstBJSimmonsJPMasseyC (2018) Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them. Management Science64(3): 1155–1170.
15.
DraperNAPieter HoffmannCLutzC, et al (2024) Privacy resignation, apathy, and cynicism: introduction to a special theme. Big Data & Society11(3): 20539517241270663.
16.
DuYR (2023) Personalization, echo chambers, news literacy, and algorithmic literacy: a qualitative study of AI-powered news app users. Journal of Broadcasting & Electronic Media67(3): 246–273.
17.
EslamiMKarahaliosKSandvigC, 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, 7–12May, pp. 2371–2382. New York: Association for Computing Machinery.
18.
FelzmannHFosch-VillarongaELutzC, et al (2020) Towards transparency by design for artificial intelligence. Science and Engineering Ethics26(6): 3333–3361.
HamiltonKKarahaliosKSandvigC, 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.
HargittaiE (2009) An update on survey measures of web-oriented digital literacy. Social Science Computer Review27(1): 130–137.
22.
HargittaiEMarwickA (2016) “What can I really do?” Explaining the privacy paradox with online apathy. International Journal of Communication10: 3737–3757.
23.
HartleyJMSchwartzSA (2020) Trust, disconnection, minimizing risk and apathy: a compass of coping tactics in datafied everyday lives. MedieKultur: Journal of Media and Communication Research36(69): 11–28.
24.
HayesAFKrippendorffK (2007) Answering the call for a standard reliability measure for coding data. Communication Methods and Measures1: 77–89.
25.
HoffmannCPLutzCRanziniG (2016) Privacy cynicism: a new approach to the privacy paradox. Cyberpsychology: Journal of Psychosocial Research on Cyberspace10(4): 7.
26.
HouYT-YJungMF (2021) Who is the expert? Reconciling algorithm aversion and algorithm appreciation in AI-supported decision making. Proceedings of the ACM on Human-Computer Interaction5(CSCW2): 1–25.
27.
KappelerKFesticNLatzerM (2023) Dataveillance imaginaries and their role in chilling effects online. International Journal of Human-Computer Studies179: 103120.
28.
KarizatNDelmonacoDEslamiM, 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.
KitchinR (2017) Thinking critically about and researching algorithms. Information Communication and Society20(1): 14–29.
31.
KlugDQinYEvansM, 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, 21–25June, pp. 84–92. New York: Association for Computing Machinery.
32.
KolkmanD (2022) The (in)credibility of algorithmic models to non-experts. Information, Communication & Society25(1): 93–109.
33.
LakkarajuHKamarECaruanaR, 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, 27–28January, pp. 131–138. New York: Association for Computing Machinery.
34.
LarssonSHeintzF (2020) Transparency in artificial intelligence. Internet Policy Review9(2): 1–16.
35.
LoggJMMinsonJAMooreDA (2019) Algorithm appreciation: people prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes151: 90–103.
MorrowGSwire-ThompsonBPolnyJM, et al (2022) The emerging science of content labeling: contextualizing social media content moderation. Journal of the Association for Information Science and Technology73(10): 1365–1386.
38.
Oeldorf-HirschANeubaumG (2023) What do we know about algorithmic literacy? The status quo and a research agenda for a growing field. New Media & Society27: 681–701.
39.
OtisA (2024) The effects of transparency cues on news source credibility online: an investigation of “opinion labels.”Journalism25(1): 198–217.
PotterWJ (2010) The state of media literacy. Journal of Broadcasting & Electronic Media54(4): 675–696.
42.
RaderEGrayR (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, 18–23April, pp. 173–182. New York: Association for Computing Machinery.
43.
RaderECotterKChoJ (2018) Explanations as mechanisms for supporting algorithmic transparency. In: Proceedings of the 2018 CHI conference on human factors in computing systems, Montreal, QC, Canada, 21–26April, pp. 1–13. New York: Association for Computing Machinery.
44.
SchellewaldA (2022) Theorizing “stories about algorithms” as a mechanism in the formation and maintenance of algorithmic imaginaries. Social Media + Society8(1): 20563051221077025.
SilesIMeléndez-MoranA (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, 27–31May.
47.
SilesISegura-CastilloASolísR, et al (2020) Folk theories of algorithmic recommendations on Spotify: enacting data assemblages in the global South. Big Data & Society7(1): 2053951720923377.
48.
SimpsonEHamannASemaanB (2022) How to tame “your” algorithm: LGBTQ + users’ domestication of TikTok. Proceedings of the ACM on Human-Computer Interaction6(GROUP): 1–27.
StrycharzJSegijnCM (2024a) Chilling effects as a result of corporate surveillance in digital communication: a comparison between American and Dutch media users. International Journal of Communication18: 320–343.
51.
StrycharzJSegijnCM (2024b) Ethical side-effect of dataveillance in advertising: impact of data collection, trust, privacy concerns and regulatory differences on chilling effects. Journal of Business Research173: 114490.
52.
TaddickenM (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 Communication19(2): 248–273.
Ytre-ArneBMoeH (2021) Folk theories of algorithms: understanding digital irritation. Media, Culture and Society43(5): 807–824.
59.
ZerilliJKnottAMaclaurinJ, et al (2019) Transparency in algorithmic and human decision-making: is there a double standard?Philosophy & Technology32(4): 661–683.
60.
ZhangDBoermanSCHendriksH, et al (2024) “They know everything”: folk theories, thoughts, and feelings about dataveillance in media technologies. International Journal of Communication18: 2710–2730.
