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Dual-Screen Television: Computational Analysis of Audience Attunement
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Dual-Screen Television: Computational Analysis of Audience Attunement
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Dual-Screen Television: Computational Analysis of Audience Attunement
Dual-Screen Television: Computational Analysis of Audience Attunement
Dissertation

Dual-Screen Television: Computational Analysis of Audience Attunement

2023
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Overview
This research uses computational analysis of tweets to interpret audience response to televisual narrative. Audiences today turn to Twitter to share reactions during broadcast television; viewers form a community of fans, and also shape an online image through these mediated judgements. In particular, I focus on HBO’s Euphoria, which earned the status of most-tweeted about television show by garnering thirty-four million tweets during its second season alone. These responses morph the work into an assemblage of viewer response and viral content. Building on existing methods in the digital humanities, I formulate a novel way to computationally analyze what television, or cultural experiences generally, do in real-time: what feelings they set in motion in audiences, how audiences attune to these works, and how they self-cultivate via narrative.In pairing response analysis with a close reading of Euphoria, this thesis examines in Chapter One how audiences identify with character, and in Chapter Two how they emotionally respond to character and scene. Influenced by reader response theory and Rita Felski’s concept of attunement, it interrogates viewers’ character judgements. Utilizing affect theory and Silvan Tomkin’s model of emotional contagion, it studies how emotions can move between the work and its viewers. Finally, it discusses the jouissanceof tweeting while watching uncomfortable television, and what Twitter makes possible or forecloses in audience response. Television is the dominant entertainment medium of late capitalism, and this thesis aims to use computational tools to take a pulse on how spectorial pleasure manifests on the Internet