Algorithms, new media, and politics: prioritizing “noise” at both ends
“Politics used to be about ‘who gets what, when, how,’ in Harold Lasswell’s famous formulation. But today it’s more about who said what, when, and how—and why everyone is so upset about it.”
As R. McChesney wrote in The Problem of the Media, the media is “at the center of struggles for power and control in any society, and they are arguably even more vital players in democratic nations.”
The media landscape is in a state of constant evolution, bringing about unexpected developments with profound implications for democratic governance and politics. The advent of new media has fundamentally transformed the functioning of government institutions, the communication strategies of political leaders, the conduct of elections, and the nature of citizen engagement.
In the ethos of Civic Science’s mission to bridge divides, cultivate informed citizenship, and inspire positive societal change through interdisciplinary efforts, I am launching a brief series on this Substack to examine the intersection of new media, data science, psychology, and political science and what that portends for our democracy.
In The New Media’s Role in Politics, Diana Owen outlines an idealistic vision for media, where it serves to inform the public, equip citizens with the knowledge necessary for making decisions about leadership and policy, act as vigilant watchdogs over government actions, shape public discourse on critical issues, provide platforms for political expression, and foster community engagement by connecting individuals with shared causes and solutions to societal challenges. However, the reality often falls short. Many forms of new media—encompassing social platforms, newsletters, Reddit, and other recent innovations in content creation and sharing—fail to live up to these lofty ideals. Instead, they often seem trapped in a ratings-driven cycle, where algorithms prioritize content based on clicks, views, and shares, rather than on its civic value. This trend points to a broader shift in media consumption, one characterized by a lack of rigorous fact-checking, filtering, or consistent editorial standards.
For Gen-Z, a key demographic in the 2024 election, catching up on the news has become a byproduct of their time spent on social media platforms such as Instagram and TikTok. Surveys consistently show TikTok emerging as the primary news source for Gen Z, mirroring its growing influence among Americans at large. A 2023 Pew survey revealed that one-third of adults under 30 now regularly turn to TikTok for news, marking a staggering 255% increase since 2020. This development signals a nuanced shift in the media landscape: many young users aren't actively seeking out news but are instead receiving it through algorithmic feeds, whether they intend to or not.
Unlike the ideal role of the media that Owen wrote about, social media algorithms aren’t programmed to prioritize veracity, rather engagement regardless of the cost. As explained in Cross-Country Trends in Affective Polarization, most social media platforms collect data from your browsing history to personalize your experience using sophisticated algorithms. These recommendation and prediction algorithms suggest content tailored to your preferences, whether derived from the Netflix shows you watch, the Instagram accounts you follow, or your recent Google searches. While such algorithms are designed to enhance and customize the user experience, they also inadvertently create echo chambers, a phenomenon that contributes to significant political divides, as people increasingly lose the ability to engage with nuanced information that challenges their views, and, consequently, become further and further entrenched in their beliefs.
A recent study conducted on TikTok revealed how swiftly a user can become radicalized with minimal input. One of the more striking findings was the impact of engaging with transphobic content. When a user interacted solely with such content, the TikTok algorithm quickly escalated the volume and variety of far-right video recommendations. Researchers coded approximately 450 videos that appeared on the app's "For You" page, a personalized homepage driven by TikTok’s recommendation algorithm. Despite the focus on transphobic content, they discovered that the feed rapidly filled with videos promoting misogyny, racism, white supremacist beliefs, anti-Semitism, conspiracies, hate symbols, and other generally hateful or violent content.
As Owen noted, the complexities of the new media landscape are evident in the diversity of available content. The information disseminated through the vast communications network ranges from fact-based investigative reporting by professional journalists to brash fabrications or “alternative facts.” In this new media era, the boundaries separating these different types of information have become increasingly blurred. Professional media editors, who once regulated the flow of information by adhering to principles and standards associated with the public good, have been supplanted by social media and analytics editors whose primary goal is to attract users to content, irrespective of its news value. Consequently, audience members must work harder to distinguish fact from fiction and to identify what is significant from what is inconsequential.
As John Halpin points out in his, All Politics Is Now Media Criticism, before the surge of digital devices and the ceaseless hum of online activity, mass politics relied on disciplined party and ideological workers organizing and educating small groups about how parties, policies, laws, and regulations could impact their personal interests and values. At present, the relationship between politics and the media is eerily symbiotic – politicians need the media to publicize them and their platform, and the media needs “hot takes” for virality, as research indicates that content triggering intense emotions tends to go viral, irrespective of whether those emotions are positive (such as awe) or negative (including anger or anxiety). This means that politicians are rewarded for blasphemy, outrageous takes, and pretty much anything else that instigates an emotional response. The goal is no longer to inform, but rather to entertain. What results is a relationship between politics and new media that prioritizes “noise” at both ends, and algorithmically-driven information platforms that reward entertainment over thoughtfulness.
Halpin put it aptly, “politics used to be about ‘who gets what, when, how,’ in Harold Lasswell’s famous formulation. But today it’s more about who said what, when, and how—and why everyone is so upset about it.”
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