A Framework for Analyzing Multimodal Communication in Digital Discourse

Thesis Abstract

Digital discourse has seen a rapid proliferation of multimodal communication strategies, encompassing visual, textual, auditory, and gestural elements that collectively shape meaning in online interactions. Yet, existing analytical models predominantly focus on linguistic features, often neglecting the integrated role of multimodal resources that influence interpretation and engagement in digital environments. This study aims to develop a comprehensive analytical framework for examining multimodal communication in digital discourse, thereby filling a vital gap in current communication and linguistics literature. The primary objectives include identifying key multimodal features across digital platforms, conceptualizing their interactions within a cohesive analytical model, and evaluating the framework's applicability through empirical case studies of social media interactions, online debates, and digital advertising. The research adopts a mixed-methods design, combining qualitative and quantitative approaches to ensure a robust exploration of multimodal features and their interactions. The study population comprises 300 digital communication instances sourced from a diverse range of social media platforms, online forums, and digital advertising campaigns. The sample is selected via stratified purposive sampling to ensure representation across different digital contexts, with 100 instances from each category. Data collection instruments include a detailed multimodal coding scheme developed through an initial qualitative pilot study, alongside semi-structured interviews with digital communication practitioners to validate coding categories and interpretative frameworks. Data analysis involves multi-layered techniques, including thematic analysis of qualitative data to identify recurrent multimodal patterns, coupled with complex network analysis to examine the interactions among multimedia elements. Additionally, regression analysis will be employed to test the predictive relationships between multimodal features and user engagement metrics, such as likes, shares, and comments. The analytical framework is anchored in Kress and van Leeuwen’s Visual Grammar theory and Halliday’s Systemic Functional Linguistics, facilitating an integrated understanding of visual and linguistic semiotics within digital contexts. The framework aims to delineate how multimodal resources synergistically function to construct meaning and influence audience responses in digital discourse. Expected findings include the identification of core multimodal strategies that enhance message clarity and emotional impact, alongside the recognition of contexts where specific modalities are prioritized or de-emphasized. The study anticipates revealing significant correlations between certain multimodal configurations and levels of user engagement, thereby providing empirical validation of the proposed analytic framework. Furthermore, the research aims to demonstrate that the integrated framework effectively captures the complexities of digital communication beyond traditional linguistic analysis. This study contributes novel insights into the multi-semiotic nature of digital discourse, offering a systematic, empirically validated analytical tool that can be employed by linguists, communication scholars, and digital media practitioners. It advances understanding of how multimodal elements coalesce to create meaning in online environments and offers practical guidelines for designing and evaluating effective digital communication strategies. The main conclusion emphasizes the necessity of adopting an integrated, multimodal perspective for accurate analysis of digital discourse, advocating for further refinement and application of the framework across diverse digital platforms. Recommendations include extending the model to cross-cultural digital communication studies, developing training modules for digital content creators, and exploring technological integration through machine learning algorithms to automate multimodal analysis. Overall, this research aims to position itself as a foundational contribution to multimodal communication theory and practice within digital discourse analysis.