A computational reward learning account of social media engagement
Open accessAbstract
Social media has become a modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (likes), portraying the online world as a Skinner Box for the modern human. Yet despite such portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. Here, we apply a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyze over one million posts from over 4000 individuals on multiple social media platforms, using computational models based on reinforcement learning theory. Our results consistently show that human behavior on social media conforms qualitatively and quantitatively to the principles of reward learning. Specifically, social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. Results further reveal meaningful individual difference profiles in social reward learning on social media. Finally, an online experiment (n = 176), mimicking key aspects of social media, verifies that social rewards causally influence behavior as posited by our computational account. Together, these findings support a reward learning account of social media engagement and offer new insights into this emergent mode of modern human behavior.
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The interval at which an algorithm rewards behavior influences how quickly it is repeated.
“Furthermore, the interval at which an algorithm rewards behavior influences how quickly it is repeated (Lindström et al., 2021).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Lindström et al., 2021
The Introduction and Results (Study 3; page not available) link higher social reward rates to shorter intervals between posts, and the experiment found longer posting latencies when participants received fewer likes. However, the experiment changed the number of likes per post, not the interval at which an algorithm delivered rewards; that specific mechanism is not established. (LLM, conf=0.78)
Experimental — accuracy not yet measuredHumans are susceptible to social feedback on digital media.
“Studies show that humans are susceptible to social feedback on digital media: Likes influence how quickly people post again (Lindström et al., 2021), whether or not they consider a post successful (Carr et al., 2018), and how happy, self-assured, and popular they feel after posting status updates (Rosenthal-von der Pütten et al., 2019; Zell & Moeller, 2018).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Lindström et al., 2021
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Algorithm-Mediated Social Learning in Online Social NetworksPreprint
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Self-citationOld Strategies, New Environments: Reinforcement Learning on Social Media
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The Economics of AttentionPreprint
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“Given that prior engagement, especially a rewarding one, is a strong predictor of future engagement (Brady et al., 2020; Lindström et al., 2021), emotions seem to be contributing to the strengthening of social interactions, which then leads to an even further stronger activation in future…”
BackgroundDoes Social Bot Help Socialize? Evidence from a Microblogging Platform
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“Second, social media algorithms often expose users to other people who share similar interests or pursue similar actions (Kaiser & Rauchfleisch, 2020; Lindström et al., 2021).”
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“Hello jar: The Hello Jar (Hello box) in which HCPs can leave messages for their colleagues provides a platform for positive reinforcement through recognition and appreciation, boosting morale and fostering a supportive work environment [28].”
BackgroundDigital Media, Cognition, and Brain Development in Adolescence
Marciano, L., Dubicka, B., Magis‐Weinberg, L., Morese, R., Viswanath, K., & Weber, R. (2024) · Springer eBooks · doi:10.1007/978-3-031-69362-5_4 · Cited by 18 · see citing works
Hidden Reward: Affect and Its Prediction Errors as Windows Into Subjective Value
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“…of departure, using affect and its prediction errors to complement our understanding of valuation in contexts where value is less tangible than monetary reward (Lindström et al., 2021; E. R. Pool et al., 2022; Tamir & Mitchell, 2012), encompassing policy decisions, social interactions, and more.”
BackgroundConnected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness
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Social Media and Well-being: A Methodological PerspectivePreprint
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“Although its use within SMU research is still in a nascent stage, formal modeling has already proven useful to better understand how reward learning mechanisms contribute to SMU and may underlie some forms of compulsive SMU [45].”
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“For example, Lindström et al. (2021) were able to demonstrate reinforcement effects by focusing on inter-post time (which they labeled "latency") as a key social media engagement metric.”
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Considering information-sharing motives to reduce misinformation
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“These personal accounts of mental health problems are often rewarded with praise and attention in the form of ‘likes’, comments and followers, which are powerful indicators of social acceptance [63, 85].”
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“At the same time, the attempt to experience and define one’s own self through the “gaze of the Other” may be captivating or socially rewarding [93].”
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Modeling the Social Reinforcement of Misinformation Dissemination on Social Media
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Graphical Evolutionary Game Theoretic Modeling of Strategy Evolution Over Heterogeneous Networks
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“, 2021) is being used to design schedules of reinforcements that increase the chances of repeated use, as in social platforms or online gaming (Lindström et al., 2021).”
MethodProspective Predictors of Adolescent Screen Time and Problematic Screen Use
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Political reinforcement learners
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“This feedback can function like RL signals, akin to a Skinner box, as evidenced by observational [6,7] and experimental studies [116] on social media platforms.”
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“For example, in a series of quantitative analyses, Lindström et al. (2021) showed that the latency of posting behavior (a dimension of behavior strength) was a function of the number of likes received in preceding posts.”
InfluentialKenntemich, C., Büttner, C., & Rudert, S. (2024) · Personality and Social Psychology Bulletin · doi:10.1177/01461672241297824 · Cited by 12 · see citing works
“This observation again aligns with the Reconnection with Many Hypothesis and with social media user’s reward-learning approaches after popular posts (Lindström et al., 2021).”
InfluentialAcute isolation is associated with increased reward seeking and reward learning in human adolescents
Tomova, L., Towner, E., Thomas, K., Zhang, L., Palminteri, S., & Blakemore, S. (2025) · Communications Psychology · doi:10.1038/s44271-025-00306-6 · Cited by 7 · see citing works
Consider the time dimension: theorizing and formalizing sequential media selection
Gong, X., & Huskey, R. (2023) · Human Communication Research · doi:10.1093/hcr/hqad051 · Cited by 7 · see citing works
The brain digitalization: it's all happening so fast!
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How social media users adopt the toxic behaviors of ingroup and outgroup accounts
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“Social media are online platforms that encourage reward-seeking behaviour, comparable to a Skinner box, with teenagers tending to be more vulnerable to these stimuli [16, 17].”
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“Besides, users prefer to engage with an IS that has the capability to provide intrinsic or extrinsic rewards to themselves [40].”
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Cognitive modeling of real-world behavior for understanding mental health
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When smartphones take over: a mixed methods study of phubbing in child and adolescent psychiatry
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“Conversely, a decrease in the number of likes received on social media posts can lead to increased post latency, ultimately influencing social media behavior [42].”
Signatures of Reinforcement Learning in Natural Behavior
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“Another study tracking individuals on Instagram showed that increased social reward, operationalized as “likes” on one’s posts, predicted greater posting frequency (Lindström et al., 2021).”
Experience and advice consequences shape information sharing strategies
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“More recently, the RL framework has been applied to explain the dynamics of financial markets, as well as social media engagement (Lindström et al. 2021; Lussange et al. 2024; Turner et al. 2024).”
Old strategies, new environments: Reinforcement Learning on social mediaPreprint
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Persons and Personalization on Digital Platforms
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Big tech, the state, or you? Who is responsible for generative AI addiction?
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- Authors
- Lindström, B., Bellander, M., Schultner, D. T., Chang, A., Tobler, P. N., & Amodio, D. M.
- Affiliations
- University of Amsterdam (NL)
- Karolinska Institutet (SE)
- Boston University (US)
- University of Zurich (CH)
- New York University (US)
- Corresponding author
- Lindström, Björn
- ORCID
- Venue
- Nature Communications
- Published
- 2021-02-26
- Volume
- 12
- Issue
- 1
- Pages
- 1311-1311
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7910435
- OpenAlex
- W3130507978
- Cited by
- 185
- References
- 61
- Citations by year
- 2026: 49 · 2025: 43 · 2024: 33 · 2023: 33 · 2022: 18 · 2021: 8 · 2020: 1
- FWCI
- 31.88
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-03-16)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion, other-oa)
- UvA-DARE (University of Amsterdam) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (100019, 176016)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016.Vici.185.058)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Computer Science › Computer Science Applications › Digital Media and Philosophy
- Keywords
- social media engagement, reward learning, reinforcement learning, social reward, likes, posting behavior, effort cost, opportunity cost, individual differences, computational modeling, online experiments
- MeSH terms
- Female, Humans, Learning, Male, Reinforcement, Psychology, Reward, Social Behavior, Social Media
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