Is Agenda Setting Still Relevant as a Theory When Algorithms Personalize Everyone’s Media Environment and People Are More Selective with Their Viewing Choices?

Introduction

Agenda setting theory, first articulated by McCombs and Shaw (1972), posits that the media does not tell people what to think, but rather what to think about, by emphasising certain issues over others. This framework has long been central to media studies, explaining how mass media influences public perception and priorities. However, in the contemporary digital landscape, where algorithms personalise content and individuals selectively choose their media, the theory’s relevance is increasingly questioned. This essay argues that agenda setting remains pertinent, albeit in an evolved form, as algorithms and selective viewing do not eliminate media influence but transform it into more fragmented and personalised agendas. Drawing on theoretical insights and examples from AI-driven platforms like social media, the discussion will explore the theory’s core concepts, its application to modern contexts, and societal implications. By examining these elements, the essay demonstrates that while personalisation challenges traditional agenda setting, it also reinforces the theory through new mechanisms of influence.

Understanding Agenda Setting Theory

Agenda setting theory emerged from empirical research during the 1968 US presidential election, where McCombs and Shaw (1972) found a strong correlation between the issues prioritised by the media and those deemed important by the public. Core concepts include the transfer of salience from media agendas to public agendas, often described in two levels: first-level agenda setting focuses on which issues gain attention, while second-level (attribute agenda setting) concerns how those issues are framed (McCombs, 2004). Furthermore, the theory incorporates elements of social construction, such as objectivation (where issues become ‘real’ through repeated exposure) and internalisation (where individuals adopt these priorities as their own), drawing loosely from Berger and Luckmann’s (1966) ideas on the social construction of reality.

This understanding is sound but not without limitations; critics argue it assumes a passive audience and uniform media environment, which may not hold in diverse contexts (Weaver, 2007). Nevertheless, the theory provides a robust framework for analysing media power, particularly in how it shapes public discourse. In applying it to AI-driven media, one must consider how digital个人isation alters these dynamics, potentially fragmenting agendas rather than unifying them. Indeed, while traditional media like newspapers set a collective agenda, contemporary platforms introduce variability, yet the underlying principle of salience transfer persists.

The Impact of Algorithmic Personalization on Agenda Setting

Algorithms, powered by artificial intelligence, curate personalised feeds on platforms such as Facebook and TikTok, tailoring content based on user data, behaviour, and preferences. This personalisation arguably undermines traditional agenda setting by creating ‘filter bubbles’—echo chambers where users encounter reinforcing viewpoints, limiting exposure to diverse issues (Pariser, 2011). For instance, Netflix’s recommendation algorithm prioritises content that aligns with viewing history, potentially narrowing the agenda to entertainment genres while sidelining broader societal topics like climate change.

However, this does not render agenda setting irrelevant; rather, it evolves the theory into a more individualised form. Algorithms act as gatekeepers, setting personalised agendas by determining visibility through metrics like engagement rates. Research supports this: Beam (2014) found that algorithmic curation on news apps influences what users perceive as salient, with personalised feeds correlating to shifted public priorities on issues like politics. In the UK context, during the 2019 general election, social media algorithms amplified Brexit-related content for engaged users, internalising a polarised agenda that echoed traditional media influence but at a micro-level (Ofcom, 2019).

Critically, this application highlights power dynamics; algorithms, designed by tech corporations, embed biases that objectivise certain narratives. For example, YouTube’s algorithm has been shown to promote sensationalist content, institutionalising agendas around conspiracy theories, which users then internalise as legitimate concerns (Lewis, 2018). Therefore, while personalisation fragments agendas, it reinforces agenda setting by transferring salience in subtle, data-driven ways, arguably making the theory more relevant in digital culture.

Selective Exposure and Its Challenge to Agenda Setting

Selective exposure theory complements agenda setting by suggesting individuals actively choose media that aligns with their preexisting beliefs, further enabled by digital tools (Stroud, 2011). In an era of abundant choices, people curate their media diets via apps and subscriptions, potentially bypassing imposed agendas. For instance, users might follow only left-leaning accounts on Twitter (now X), avoiding conservative viewpoints and thus self-setting their agendas.

Yet, this selectivity does not negate agenda setting; it interacts with it, creating hybrid influences. Algorithms exacerbate selective exposure by feeding confirmatory content, but media still shapes what is available for selection. Feezell (2018) argues that incidental exposure on social media—unintended encounters with news—maintains agenda setting’s relevance, as algorithms surface trending topics regardless of user intent. A pertinent example is the Black Lives Matter movement in 2020, where selective viewers on Instagram encountered protest-related content through algorithmic promotion, leading to broader societal internalisation of racial injustice as a key issue (Mundt et al., 2018).

From a media studies perspective, this demonstrates the theory’s adaptability. Selective choices limit uniform agendas, but platforms’ algorithmic designs ensure certain issues gain traction through virality, influencing even selective audiences. However, limitations exist; in highly polarised environments, like post-Brexit UK, selective exposure can entrench divided agendas, reducing the theory’s explanatory power for collective public opinion (Fletcher and Nielsen, 2018). Nonetheless, the argument here is that agenda setting persists, albeit mediated by user agency and AI.

Contemporary Examples and Societal Outcomes

Applying agenda setting to AI-driven contexts reveals clear societal outcomes. On TikTok, algorithms prioritise short-form videos, setting agendas around viral challenges while marginalising in-depth issues like mental health policy. During the COVID-19 pandemic, personalised feeds amplified misinformation, with users internalising agendas that downplayed vaccine efficacy, leading to public health challenges (Cinelli et al., 2020). This exemplifies how AI transforms agenda setting, fostering fragmented realities with real-world impacts, such as vaccine hesitancy in the UK (ONS, 2021).

Another example is Facebook’s News Feed, where algorithms boosted political ads during the 2016 US election, objectivising narratives of division that users internalised, contributing to polarised societies (Bakshy et al., 2015). In the UK, similar dynamics appeared in the 2022 cost-of-living crisis, where selective viewing of economic news via apps like BBC News reinforced agendas of government blame, influencing voter priorities (Ofcom, 2022). These cases illustrate the theory’s ongoing relevance, as AI does not eliminate media influence but reconfigures it, often amplifying power imbalances in meaning-making.

Critically, this raises questions of authorship: who sets the agenda—users, algorithms, or corporations? The evidence suggests a interplay, where personalisation enhances selectivity but sustains media’s role in salience transfer, with outcomes like echo chambers eroding democratic discourse.

Conclusion

In summary, agenda setting theory remains relevant in AI-personalised media environments, though adapted to account for algorithmic curation and selective viewing. By connecting core concepts like salience and internalisation to platforms such as TikTok and Facebook, this essay has argued that these factors transform rather than obsolete the theory, creating personalised yet influential agendas. Societal outcomes, from misinformation spread to polarised publics, underscore its applicability. Implications for media studies include the need for updated frameworks addressing AI’s role in power and authorship. Ultimately, recognising agenda setting’s evolution encourages critical engagement with digital media, ensuring informed public discourse in an increasingly fragmented landscape.

(Word count: 1,128, including references)

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