Impact of Social Media Algorithms on News Framing and Public Opinion in Emerging Markets

Project Abstract

In this study, we investigate how social media algorithms shape news framing and influence public opinion in emerging markets, addressing a critical gap where algorithmic curation intersects with political communication and consumer behavior. The research adopts a mixed-methods design combining quantitative content analysis of algorithmically recommended news feeds with qualitative interviews of journalists, policymakers, and social media users across three emerging market contexts. We examine how platform-specific algorithms prioritize normative frames—episodic versus thematic, crisis-driven versus issue-focused—and how these frames correlate with shifts in public opinion on salient political issues, trust in media, and perceived legitimacy of institutions. By triangulating data from multiple platforms (e.g., short-form video feeds, timelines, and recommended article flows), we map the differential exposure patterns that emerge from algorithmic ranking, personalization, and echo-chamber effects, while controlling for urban-rural divides, literacy levels, and socio-economic status. The study also assesses the role of user agency and media literacy in moderating algorithm-driven framing effects, including behavioral responses such as sharing, commenting, and alternative information-seeking behavior. A conceptual framework based on agenda-setting, framing theory, and algorithmic accountability guides analysis, complemented by insights from critical political economy to understand platform monetization pressures and their implications for news diversity and civic participation. We employ a multi-stage sampling strategy to gather representative feed samples and conduct scenario-based experiments to measure causal influence on issue salience, policy preferences, and trust calibration. Data collection spans six months, enabling temporal analysis of event-driven fluctuations and platform policy changes. Analytical approaches include computational text analysis to classify frames and sentiment, network analysis to detect information diffusion patterns, and regression models to quantify the relationship between exposure to specific frames and attitude shifts. Ethical considerations address data privacy, consent, and the potential harm of exposure to misinformation, with mitigation strategies such as participant debriefing and platform collaboration for responsible data handling. The expected outcomes illuminate the mechanisms by which algorithmic curation construes news narratives, potentially reinforcing pre-existing asymmetries in political knowledge and participation in emerging markets. The research contributes to theory by integrating algorithmic governance with traditional media effects, and to practice by offering policy recommendations for transparency, user-centered design, and media literacy interventions that promote diverse information ecosystems. Ultimately, findings aim to inform stakeholders—journalists, platform operators, regulators, and civil society—about the conditions under which social media algorithms can support informed public discourse without compromising pluralism or democratic legitimacy in rapidly evolving digital publics.

Project Overview

What This Project Is About

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The project examines how social media algorithms influence the way news is presented (framing) and how people form opinions about it in developing markets. It looks at which kinds of stories are promoted, how headlines are shaped, and how that affects what people think is important or true.

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The Problem It Addresses

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There is limited understanding of how algorithm-driven feeds shape public perception in markets with different media systems and weaker regulatory oversight. This gap can lead to misleading frames, echo chambers, and skewed public opinion that affects civic participation and policy support.

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Objectives of the Project

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1. Identify common news framing patterns created by popular social media feeds.
2. Explore how these frames influence readers’ attitudes toward current events.
3. Compare framing effects across at least two emerging markets.
4. Suggest practical ways to promote diverse and accurate news exposure.

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What You Will Do Step by Step

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1. Review literature on news framing and social media algorithms.
2. Select two or more emerging markets and collect news posts and user comments.
3. Analyze how headlines and summaries are shaped by feeds and what content gets boosted.
4. Survey or interview a small group of readers to gauge opinion changes.
5. Compare findings across markets and identify patterns.
6. Discuss implications for journalists, platforms, and policymakers.

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Expected Outcome

Expected outcomes include a clearer map of how algorithms influence framing and public opinion, a cross-market comparison, and recommendations for more responsible content curation and media literacy efforts.