src.bib

@article{santini_social_2024,
	title = {Social {Media} {Addiction} {Predicts} {Compromised} {Mental} {Health} as well as {Perceived} and {Objective} {Social} {Isolation} in {Denmark}: {A} {Longitudinal} {Analysis} of a {Nationwide} {Survey} {Linked} to {Register} {Data}},
	issn = {1557-1874, 1557-1882},
	shorttitle = {Social {Media} {Addiction} {Predicts} {Compromised} {Mental} {Health} as well as {Perceived} and {Objective} {Social} {Isolation} in {Denmark}},
	url = {https://link.springer.com/10.1007/s11469-024-01283-3},
	doi = {10.1007/s11469-024-01283-3},
	language = {en},
	urldate = {2025-07-02},
	journal = {International Journal of Mental Health and Addiction},
	author = {Santini, Ziggi Ivan and Thygesen, Lau Caspar and Andersen, Susan and Tolstrup, Janne S. and Koyanagi, Ai and Nielsen, Line and Meilstrup, Charlotte and Koushede, Vibeke and Ekholm, Ola},
	month = mar,
	year = {2024},
}

@article{shiraly_mediating_2024,
	title = {The mediating and moderating effects of psychological distress on the relationship between social media use with perceived social isolation and sleep quality of late middle-aged and older adults},
	volume = {24},
	issn = {1471-2318},
	url = {https://bmcgeriatr.biomedcentral.com/articles/10.1186/s12877-024-05252-2},
	doi = {10.1186/s12877-024-05252-2},
	language = {en},
	number = {1},
	urldate = {2025-07-02},
	journal = {BMC Geriatrics},
	author = {Shiraly, Ramin and Yaghooti, Farnaz and Griffiths, Mark D.},
	month = aug,
	year = {2024},
	pages = {655},
}

@article{rey_alienation_2012,
	title = {Alienation, {Exploitation}, and {Social} {Media}},
	volume = {56},
	issn = {0002-7642, 1552-3381},
	url = {https://journals.sagepub.com/doi/10.1177/0002764211429367},
	doi = {10.1177/0002764211429367},
	abstract = {This article is a critical examination of how capitalism has adapted to the explosion of websites devoted to user-generated content (commonly referred to as social media or Web 2.0). The author proceeds by reviewing how Marx applies the concepts of alienation and exploitation to his paradigmatic example (i.e., the factory); the author then attempts to extend the logic of both concepts to determine what they might reveal about the structural conditions of social media. A difference of prime importance between the two case studies is that factory work is wage labor coerced by economic necessity, whereas use of social networking sites is apparently voluntary and done freely. The author concludes by arguing that social media users are subject to levels of exploitation relatively consistent with industrial capitalism, whereas the structural conditions of the digital economy link profitability to a reduction in the intensity of alienation. Finally, he infers that social media is not economically beneficial to most users.},
	language = {en},
	number = {4},
	urldate = {2025-07-02},
	journal = {American Behavioral Scientist},
	author = {Rey, P J},
	month = apr,
	year = {2012},
	pages = {399--420},
}

@article{siddiq_social_2024,
	title = {Social isolation, social media use, and poor mental health among older adults, {California} {Health} {Interview} {Survey} 2019–2020},
	volume = {59},
	issn = {0933-7954, 1433-9285},
	url = {https://link.springer.com/10.1007/s00127-023-02549-2},
	doi = {10.1007/s00127-023-02549-2},
	language = {en},
	number = {6},
	urldate = {2025-07-02},
	journal = {Social Psychiatry and Psychiatric Epidemiology},
	author = {Siddiq, Hafifa and Teklehaimanot, Senait and Guzman, Ariz},
	month = jun,
	year = {2024},
	pages = {969--977},
}

@inbook{dual_process_theories_habits_2014,
	author = {Labrecque, Jennifer and Lin, Pei-Ying and Rünger, Dennis},
	year = {2014},
	month = {01},
	pages = {371-385},
	title = {Habits in dual process models},
	journal = {Dual-process theories of the social mind},
	url = {https://www.researchgate.net/publication/265598810_Habits_in_dual_process_models},
}

@article{doi:10.1073/pnas.1320040111,
	author = {Adam D. I. Kramer  and Jamie E. Guillory  and Jeffrey T. Hancock },
	title = {Experimental evidence of massive-scale emotional contagion through social networks},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {111},
	number = {24},
	pages = {8788-8790},
	year = {2014},
	doi = {10.1073/pnas.1320040111},
	URL = {https://www.pnas.org/doi/abs/10.1073/pnas.1320040111},
	eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.1320040111},
	abstract = {We show, via a massive (N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. We provide experimental evidence that emotional contagion occurs without direct interaction between people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues. Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people.}
}

@article{de_social_nodate,
	title = {Social {Media} {Algorithms} and {Teen} {Addiction}: {Neurophysiological} {Impact} and {Ethical} {Considerations}},
	volume = {17},
	issn = {2168-8184},
	shorttitle = {Social {Media} {Algorithms} and {Teen} {Addiction}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11804976/},
	doi = {10.7759/cureus.77145},
	abstract = {Does it matter how many hours we spend scrolling through Instagram? This article examines the neurobiological impact of prolonged social media use, focusing on how it affects the brain's reward, attention, and emotional regulation systems. Frequent engagement with social media platforms alters dopamine pathways, a critical component in reward processing, fostering dependency analogous to substance addiction. Furthermore, changes in brain activity within the prefrontal cortex and amygdala suggest increased emotional sensitivity and compromised decision-making abilities. The role of artificial intelligence (AI) in this process is significant. AI-driven social media algorithms are designed solely to capture our attention for profit without prioritizing ethical concerns, personalizing content, and enhancing user engagement by continuously tailoring feeds to individual preferences. These adaptive algorithms are designed to maximize screen time, thereby deepening the activation of the brain's reward centers. This cycle of optimized content and heightened engagement accelerates the development of addictive behaviors. The interplay between altered brain physiology and AI-driven content optimization creates a feedback loop that promotes social media addiction among teenagers. This raises significant ethical concerns regarding privacy and the promotion of personalized content. This review article offers a comprehensive and in-depth analysis of the neurophysiological impact of social media on adolescents and the moral concerns governing them. It also provides solutions for ethical social media use and preventing addiction among teenagers.},
	number = {1},
	urldate = {2025-07-05},
	journal = {Cureus},
	author = {De, Debasmita and El Jamal, Mazen and Aydemir, Eda and Khera, Anika},
	pmid = {39925596},
	pmcid = {PMC11804976},
	pages = {e77145},
}

@misc{milli_engagement_2024,
	title = {Engagement, {User} {Satisfaction}, and the {Amplification} of {Divisive} {Content} on {Social} {Media}},
	url = {http://arxiv.org/abs/2305.16941},
	doi = {10.48550/arXiv.2305.16941},
	abstract = {In a pre-registered algorithmic audit, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse about their political out-group. Furthermore, we find that users do {\textbackslash}emph\{not\} prefer the political tweets selected by the algorithm, suggesting that the engagement-based algorithm underperforms in satisfying users' stated preferences. Finally, we explore the implications of an alternative approach that ranks content based on users' stated preferences and find a reduction in angry, partisan, and out-group hostile content, but also a potential reinforcement of pro-attitudinal content. The evidence underscores the necessity for a more nuanced approach to content ranking that balances engagement and users' stated preferences.},
	urldate = {2025-07-05},
	publisher = {arXiv},
	author = {Milli, Smitha and Carroll, Micah and Wang, Yike and Pandey, Sashrika and Zhao, Sebastian and Dragan, Anca D.},
	month = dec,
	year = {2024},
	note = {arXiv:2305.16941 [cs]},
	keywords = {Computer Science - Computers and Society, Computer Science - Social and Information Networks},
}
@inproceedings{agarwal_system-2_2024,
	address = {Rio de Janeiro Brazil},
	title = {System-2 {Recommenders}: {Disentangling} {Utility} and {Engagement} in {Recommendation} {Systems} via {Temporal} {Point}-{Processes}},
	copyright = {https://creativecommons.org/licenses/by-nc/4.0/},
	shorttitle = {System-2 {Recommenders}},
	url = {https://dl.acm.org/doi/10.1145/3630106.3659004},
	doi = {10.1145/3630106.3659004},
	abstract = {Recommender systems are an important part of the modern human experience whose influence ranges from the food we eat to the news we read. Yet, there is still debate as to what extent online recommendation platforms are aligned with the goals of their users. A core issue fueling this debate is the challenge of inferring a user’s utility based on their engagement signals such as likes, shares, watch time etc., which are often the primary metric used by platforms to optimize content. This is because users’ utility-driven decision-processes (which we refer to as System-2), e.g., reading news that are accurate and relevant for them, are often confounded by their impulsive or unconscious decision-processes (which we refer to as System-1), e.g., spend time on click-bait news articles. As a result, it is difficult to infer whether an observed engagement is utility-driven or impulse-driven. In this paper we explore a new approach to recommender systems where we infer user’s utility based on their return probability to the platform rather than engagement signals. This approach is based on the intuition that users tend to return to a platform in the long run if it creates utility for them, while pure engagement-driven interactions, i.e., interactions that do not add meaningful utility, may affect user return in the short term but will not have a lasting effect. For this purpose, we propose a generative model in which past content interactions impact the arrival rates of users based on a self-exciting Hawkes process. These arrival rates to the platform are a combination of both System-1 and System-2 decision processes. The System-2 arrival intensity depends on the utility drawn from past content interactions and has a long lasting effect on return probability. In contrast, System-1 arrival intensity depends on the instantaneous gratification or moreishness and tends to vanish rapidly in time. We show analytically that given samples from this model it is provably possible to disentangle the System-1 and System-2 decision-processes and thus infer user’s utility, thereby allowing us to optimize content based on it. We conduct experiments on synthetic data to demonstrate the effectiveness of our approach over engagement optimization.},
	language = {en},
	urldate = {2025-07-09},
	booktitle = {The 2024 {ACM} {Conference} on {Fairness}, {Accountability}, and {Transparency}},
	publisher = {ACM},
	author = {Agarwal, Arpit and Usunier, Nicolas and Lazaric, Alessandro and Nickel, Maximilian},
	month = jun,
	year = {2024},
	pages = {1763--1773},
}

@article{jia_embedding_2024,
	title = {Embedding {Democratic} {Values} into {Social} {Media} {AIs} via {Societal} {Objective} {Functions}},
	volume = {8},
	issn = {2573-0142},
	url = {http://arxiv.org/abs/2307.13912},
	doi = {10.1145/3641002},
	abstract = {Can we design artificial intelligence (AI) systems that rank our social media feeds to consider democratic values such as mitigating partisan animosity as part of their objective functions? We introduce a method for translating established, vetted social scientific constructs into AI objective functions, which we term societal objective functions, and demonstrate the method with application to the political science construct of anti-democratic attitudes. Traditionally, we have lacked observable outcomes to use to train such models, however, the social sciences have developed survey instruments and qualitative codebooks for these constructs, and their precision facilitates translation into detailed prompts for large language models. We apply this method to create a democratic attitude model that estimates the extent to which a social media post promotes anti-democratic attitudes, and test this democratic attitude model across three studies. In Study 1, we first test the attitudinal and behavioral effectiveness of the intervention among US partisans (N=1,380) by manually annotating (alpha=.895) social media posts with anti-democratic attitude scores and testing several feed ranking conditions based on these scores. Removal (d=.20) and downranking feeds (d=.25) reduced participants' partisan animosity without compromising their experience and engagement. In Study 2, we scale up the manual labels by creating the democratic attitude model, finding strong agreement with manual labels (rho=.75). Finally, in Study 3, we replicate Study 1 using the democratic attitude model instead of manual labels to test its attitudinal and behavioral impact (N=558), and again find that the feed downranking using the societal objective function reduced partisan animosity (d=.25). This method presents a novel strategy to draw on social science theory and methods to mitigate societal harms in social media AIs.},
	language = {en},
	number = {CSCW1},
	urldate = {2025-07-09},
	journal = {Proceedings of the ACM on Human-Computer Interaction},
	author = {Jia, Chenyan and Lam, Michelle S. and Mai, Minh Chau and Hancock, Jeff and Bernstein, Michael S.},
	month = apr,
	year = {2024},
	note = {arXiv:2307.13912 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction},
	pages = {1--36},
	annote = {Comment: This paper has been accepted to CSCW 2024 and will be published in Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 163 (April 2024)},
}

@article{doi:10.1177/001872675400700202,
	author = {Leon Festinger},
	title ={A Theory of Social Comparison Processes},
	journal = {Human Relations},
	volume = {7},
	number = {2},
	pages = {117-140},
	year = {1954},
	doi = {10.1177/001872675400700202},
	URL = {https://doi.org/10.1177/001872675400700202},
	eprint = {https://doi.org/10.1177/001872675400700202}
}