Introduction
Agenda setting theory, first articulated by McCombs and Shaw in their seminal 1972 study, posits that mass media influence public perception by determining which issues are deemed important, rather than dictating opinions on those issues. This framework has long been central to media studies, emphasising how media ‘set the agenda’ for public discourse. However, the rise of AI-driven algorithms that personalize content on platforms like social media and streaming services, combined with users’ increasingly selective viewing habits, raises questions about the theory’s continued applicability. This essay argues that agenda setting remains relevant, albeit in an evolved form, as algorithms and user selectivity do not negate media influence but rather redistribute it in fragmented, personalized ways. Drawing on key theoretical concepts, contemporary examples from digital platforms, and scholarly evidence, the discussion will explore the theory’s foundations, the challenges posed by personalization, and its enduring significance in AI-mediated environments. By applying agenda setting to modern contexts, such as algorithmic curation on platforms like Facebook and TikTok, the essay demonstrates how the theory adapts to digital culture while highlighting limitations in power dynamics and meaning-making.
Understanding Agenda Setting Theory
Agenda setting theory emerged from empirical research during the 1968 US presidential election, where McCombs and Shaw (1972) observed a strong correlation between media emphasis on issues and public perceptions of their salience. Core concepts include the transfer of salience from media agendas to public agendas, often described through levels: first-level agenda setting focuses on ‘what’ issues are prioritised, while second-level involves ‘how’ they are framed through attributes (McCombs, 2005). This process is not about controlling opinions but shaping the cognitive map of what matters, influencing everything from political priorities to social concerns.
In traditional media contexts, such as newspapers and broadcast television, agenda setting operated through gatekeeping by editors and journalists, who selected and emphasised stories. For instance, during major events like the Watergate scandal, media coverage elevated political corruption to the forefront of public attention, demonstrating the theory’s real-world impact. However, critics have noted limitations, such as its initial focus on mass audiences assuming uniform exposure, which overlooks diverse interpretations (Weaver, 2007). Despite this, the theory’s strength lies in its emphasis on media as a powerful intermediary in meaning-making, where institutional power—held by media organisations—objectifies certain narratives as central.
Applying this to contemporary AI-driven media, agenda setting provides a lens to examine how algorithms, rather than human gatekeepers, now institutionalize salience. Yet, as personalization fragments media environments, the theory must be interrogated for relevance. Indeed, while traditional models assumed broad audiences, today’s digital landscape challenges this uniformity, prompting a re-evaluation of how agendas are set in selective, algorithmically tailored spaces.
The Impact of Algorithmic Personalization on Media Environments
AI algorithms on platforms like Facebook, Instagram, and YouTube personalize content feeds based on user data, such as past interactions, location, and preferences, creating bespoke media experiences. This personalization, powered by machine learning, arguably disrupts traditional agenda setting by decentralizing control from mass media to individual bubbles. For example, TikTok’s For You Page uses algorithms to curate short-form videos, prioritizing content that maximizes engagement time, which can amplify niche topics over broadly salient ones (Gillespie, 2018). Consequently, users might encounter hyper-personalized agendas, such as climate change denial content for sceptical viewers, rather than a shared public agenda.
This shift raises questions about agenda setting’s relevance: if algorithms tailor content to reinforce existing views, does media still ‘set’ agendas, or merely echo them? Pariser (2011) describes this as the ‘filter bubble’ effect, where personalization isolates users from diverse perspectives, potentially weakening the theory’s assumption of media as a unifying force. In terms of societal outcomes, this has led to polarized discourses, evident in events like the 2016 US election, where algorithmic amplification of misinformation on social media platforms influenced voter priorities without a centralized agenda (Allcott and Gentzkow, 2017). Here, agenda setting evolves; algorithms do not eliminate salience transfer but institutionalize it at a micro-level, objectifying personalized issues as important.
However, this does not render the theory obsolete. Instead, it highlights an adaptation where AI acts as a new gatekeeper, applying core agenda-setting principles in digital contexts. For instance, during the COVID-19 pandemic, algorithms on Twitter (now X) promoted official health updates to some users while burying contrarian views for others, effectively setting health agendas in segmented ways. This demonstrates that, despite personalization, media platforms retain power in meaning-making, albeit through opaque, data-driven processes rather than editorial decisions.
User Selectivity and Its Implications for Agenda Setting
Compounding algorithmic personalization is users’ selective viewing choices, enabled by the abundance of digital content. Individuals increasingly curate their media diets, subscribing to podcasts, newsletters, or channels that align with their interests, often avoiding dissonant information—a phenomenon known as selective exposure (Stroud, 2010). This selectivity challenges agenda setting by suggesting that publics actively resist media-imposed agendas, choosing instead to internalize self-selected narratives.
In practice, platforms like Netflix exemplify this: its recommendation algorithms, combined with user choices, create echo chambers where viewers binge-watch genres reinforcing their worldviews, such as political documentaries that align with partisan biases. Societally, this has contributed to fragmented public spheres, as seen in the UK’s Brexit referendum, where selective consumption of pro- or anti-EU content via social media deepened divisions, with users prioritizing echo-affirming sources over balanced agendas (Del Vicario et al., 2016). Arguably, this selectivity limits the theory’s applicability, as it shifts agency from media to users, undermining the internalization of externally set agendas.
Yet, agenda setting retains relevance because selectivity often operates within algorithmically constrained environments. Users may believe they choose freely, but algorithms pre-filter options, subtly setting the agenda by determining available content. For example, Google’s search algorithms prioritize results based on perceived relevance, influencing what information users encounter and deem important, even as they selectively engage. This interplay suggests that while user agency is heightened, it does not fully escape media influence; rather, agenda setting persists through a hybrid model where personalization and selectivity co-construct salience.
Relevance of Agenda Setting in Contemporary AI-Driven Media
Despite these challenges, agenda setting theory remains pertinent, offering critical insights into power dynamics in AI-mediated cultures. By applying concepts like objectivation—where media legitimize issues as significant—the theory illuminates how algorithms perpetuate inequalities in authorship and meaning-making. For instance, on platforms dominated by tech giants, algorithmic biases can amplify marginalized voices or, conversely, suppress them, as in the case of racial bias in content recommendation systems (Noble, 2018). This extends agenda setting beyond traditional media, revealing how AI institutionalizes agendas that shape societal outcomes, such as public health awareness or political mobilization.
Critically, the theory’s evolution is evident in concepts like ‘network agenda setting,’ which accounts for interconnected digital influences (Guo and McCombs, 2016). In this view, personalized environments do not dismantle agendas but create networked ones, where user selectivity reinforces algorithmic priorities. However, limitations persist: the theory may overemphasize media power, neglecting user resistance or alternative agenda setters like influencers. Nonetheless, evidence from contemporary contexts supports its ongoing utility, urging media scholars to adapt it for analyzing AI’s role in fragmented publics.
Conclusion
In summary, agenda setting theory endures as a relevant framework in an era of algorithmic personalization and user selectivity, evolving to address decentralized media landscapes. While traditional models are challenged by filter bubbles and echo chambers, as seen in platforms like TikTok and Netflix, the theory’s core principles—salience transfer and meaning-making—persist in adapted forms. This adaptation highlights implications for media studies, including the need to scrutinize algorithmic power and its societal effects on polarization and discourse. Ultimately, recognizing agenda setting’s continued applicability encourages critical engagement with AI-driven media, fostering more inclusive digital environments. By bridging theory with contemporary examples, this essay underscores the theory’s resilience, even as digital transformations demand nuanced applications.
(Word count: 1,248 including references)
References
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- Noble, S.U. (2018) Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.
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- Weaver, D.H. (2007) Thoughts on Agenda Setting, Framing, and Priming. Journal of Communication, 57(1), pp.142-147.
