A Narrative-Influence Alignment Framework for Social Media Journalism

Thesis Abstract

This study addresses the growing misalignment between narrative framing in social media journalism and the consequential influence on audience perceptions, trust, and civic participation in the digital information ecosystem. It contends that the rapid, user-generated nature of social platforms amplifies narrative cues that can distort fact-based reporting, reduce source credibility, and skew audience interpretation, thereby undermining journalistic authority and democratic discourse. The aim is to develop and validate a Narrative-Influence Alignment Framework (NIAF) that integrates narrative theory, media effect models, and journalistic ethics to guide content construction, dissemination, and audience engagement on social media. Specific objectives are (1) to identify narrative elements (story arc, source attribution, emotional tonality, and call-to-action cues) that predict perceived credibility and alignment with factual reporting; (2) to examine how platform affordances (algorithmic ranking, interactivity, and share dynamics) moderate these relationships; (3) to test a theoretical model linking narrative alignment to audience outcomes (trust in news, perceived objectivity, and civic engagement); (4) to propose operational guidelines for journalists and newsrooms to optimize narrative-influence alignment without compromising ethical standards; and (5) to assess cross-platform variability among Twitter/X, Instagram, and TikTok audiences in different demographic segments. A mixed-methods approach is employed. The study adopts an explanatory sequential design starting with a quantitative phase, followed by a qualitative phase for deeper insight. The population comprises professional journalists and editors working in mainstream online newsrooms and social media teams across three metropolitan regions. A purposive sample of 12 media organizations will provide 240 social media posts and corresponding engagement data over six months. Data collection instruments include a structured coding scheme for narrative elements, a standardized credibility scale, and platform analytics logs capturing shares, comments, and algorithmic impressions. In the qualitative phase, 30 in-depth interviews with newsroom practitioners and 6 focus groups with 48 diverse audience members will explore contextual factors shaping narrative-influence dynamics. Validity and reliability are addressed through triangulation of content analysis with inter-coder agreement (Cohen’s kappa ? 0.80), pilot testing of instruments, and member-checking of interview transcripts. Analytical procedures comprise multiple regression and structural equation modeling (SEM) to test the NIAF hypotheses, with mediation analyses to assess the role of perceived credibility and narrative alignment. Thematic analysis will be applied to interview and focus group transcripts, using a constant comparative method to identify patterns related to platform affordances, audience literacy, and ethical constraints. Model specification includes latent constructs for Narrative Quality, Alignment with Factual Reporting, Platform Mediation, and Audience Outcomes (Trust, Objectivity Perception, and Civic Intentions). Robustness checks will involve multi-group SEM to assess cross-platform invariance and sensitivity analyses accounting for demographic controls. Key expected findings include (a) distinct narrative elements that significantly influence perceived credibility and alignment with factual reporting, (b) platform-specific moderation effects whereby algorithmic amplification and interactivity alter the strength of narrative-influence relationships, (c) empirical confirmation of a mediation pathway from Narrative Quality through Alignment to Trust and Civic Intentions, and (d) evidence that enhanced ethical framing moderates negative effects of sensationalism on audience perceptions. The study anticipates variations across platforms, with visual and short-form formats (TikTok, Instagram Reels) exhibiting stronger emotional cue effects, while long-form threads on Twitter/X show greater source attribution impact. The contribution to knowledge lies in operationalizing a theoretically grounded framework that unites narrative theory (central narrative, emotional resonance, and source credibility) with media effects and ethical journalism in the context of social media amplification. It provides a validated measurement model and actionable guidelines for newsroom practitioners to achieve narrative-influence alignment while upholding professional standards, enhancing audience trust, and supporting informed civic participation. The study concludes that deliberate alignment of narrative structure with verifiable reporting, tailored to platform affordances, can improve perceived credibility and public engagement without sacrificing accuracy, recommending ongoing newsroom training in narrative ethics, platform-specific content design, and continuous monitoring of audience literacy indicators to sustain responsible social media journalism.