Why do engagement-driven algorithms create echo chambers for citizen journalism?

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Engagement-Driven Algorithms Foster Echo Chambers by Prioritising User Interaction

Engagement-driven algorithms contribute to the formation of echo chambers by prioritising content that elicits strong user reactions and prolonged platform usage. This algorithmic design inherently favours reinforcing existing beliefs over exposing individuals to diverse perspectives. Within these digital silos, audiences tend to implicitly trust citizen-shot videos, even when context is removed, because the content aligns with their pre-existing biases.
Algorithms Amplify Content Eliciting Strong Reactions

The core function of engagement-driven algorithms is to maximise user interaction on social media platforms. This is achieved by identifying and promoting content that generates significant engagement, such as likes, shares, comments, and watch time. Content that provokes strong emotional responses, whether positive or negative, is particularly effective at capturing user attention and encouraging interaction. This prioritisation means that sensational or emotionally charged citizen journalism, which can often be unverified or presented out of context, is more likely to be amplified.
Reinforcement of Existing Biases is Favoured

By continuously presenting users with content that aligns with their demonstrated interests and previous interactions, these algorithms create a feedback loop. This loop effectively traps individuals within "digital silos" or echo chambers. Within these self-reinforcing environments, users are primarily exposed to information and viewpoints that confirm their existing beliefs and biases. The algorithms are designed to keep users on the platform longer, and showing them content they are already predisposed to agree with is an effective strategy for achieving this goal.
Citizen Journalism's "Aesthetic of Amateurism" Enhances Trust Within Echo Chambers

The nature of citizen journalism, often characterised by its "aesthetic of amateurism"—unpolished footage, background noise, and imperfect lighting—can paradoxically enhance its perceived authenticity within these echo chambers. Audiences may interpret these imperfections as indicators of unfiltered honesty, bypassing critical verification processes. When such content, which confirms existing biases, is encountered within an algorithmic echo chamber, users are more likely to accept it at face value, even if it has been manipulated or stripped of its original context, as demonstrated by the misattributed video of a store burning during the George Floyd protests.

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Why do engagement-driven algorithms create echo chambers for citizen journalism?

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