A Networked Framing Model for Climate Change News Coverage

Thesis Abstract

The study investigates how networked framing processes shape media coverage of climate change across national and local news ecosystems, addressing the persistent gap between public discourse and policy action due to fragmented and temporal framing practices. The aim is to develop a Networked Framing Model that explains how cross-platform, cross-organization, and cross-audience frames interact to produce coherent or contested representations of climate change in news coverage. Specific objectives are (1) to identify dominant frames and their evolution across a 12-month news cycle in five major newspapers and five broadcasting outlets in a metropolitan region; (2) to map the intermedia linkages and information flows using social network analysis to delineate frame propagation pathways and influential actors; (3) to test the applicability of two established theories—agenda-setting theory and framing theory—in a networked context with emphasis on issue ownership and frame resonance; (4) to develop a parsimonious networked framing model that integrates event-driven, issue-driven, and actor-driven frames; and (5) to derive actionable recommendations for journalists and editors to improve framing consistency and public understanding of climate risks. A mixed-methods design combines quantitative content analysis, social network analysis (SNA), and qualitative thematic analysis. The population comprises climate-related news items published or broadcast over 12 months by five national newspapers and five metropolitan TV/radio channels, supplemented by 200 social media posts from top climate-related accounts associated with these outlets. A stratified random sample of 600 news items (120 per outlet) will be collected, alongside 1,000 related social media posts and 30 semi-structured interviews with senior editors, climate reporters, and media scholars. Content analysis will code frames into a predefined taxonomy (e.g., risk, responsibility, solutions, economic impact, morality) and identify emerging subframes. SNA will quantify inter outlet collaborations, citation networks, and frame diffusion using degree, betweenness, and eigenvector centrality measures, with temporal network reconstruction to observe frame lifecycles. Thematic analysis will interrogate interview transcripts to reveal decision-rationale, editorial policies, and perceived audience reception. Validity will be enhanced through intercoder reliability testing (Cohen’s kappa ? 0.80) and triangulation across data sources. Reliability will be ensured by piloting coding schemes and maintaining a dual-coder approach with adjudication by a senior researcher. Data analysis will integrate quantitative and qualitative findings. Descriptive statistics will profile frame frequencies and co-occurrence patterns; logistic regression will test predictors of frame selection (e.g., outlet type, topic salience, or audience metrics). Network models will identify core frame disseminators and clusters, while temporal analyses will detect frame persistence and transitions aligned with climate events. Thematic insights will contextualize statistical results, enabling refinement of the theoretical model. The expected findings include (a) identifiable cross-outlet frames with central actors who significantly influence framing trajectories; (b) evidence that networked framing produces higher frame resonance in policy-relevant discussions when aligned with authoritative frames such as risk and solutions; and (c) differential framing effectiveness across outlet types and audiences. The study contributes to knowledge by operationalizing a Networked Framing Model that synthesizes agenda-setting and framing theories within interconnected news ecosystems, offering a formal representation of how frames circulate, mutate, and converge across media networks. It advances methodological practice by combining content analysis, SNA, and qualitative methods to capture structure and meaning in framing processes. Practically, the model provides actionable guidance for journalists to coordinate framing strategies across platforms, for editors to monitor frame drift, and for policymakers and communicators to anticipate audience interpretations of climate risk. The main conclusion is that climate change coverage is increasingly shaped by intermedia networks where certain frames, championed by influential actors, propagate rapidly and engender coherent public perception; therefore, a deliberate network-aware framing approach is essential to enhance public understanding and foster informed climate action. Recommended directions include developing newsroom guidelines for cross-platform framing coherence, training programs on network-aware storytelling, and follow-up studies assessing audience reception and behavioural responses to networked frames.