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Visual Persuasion in Mass Media: A C...

Joo, Jungseock.

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Visual Persuasion in Mass Media: A Computational Framework for Understanding Visual Communication.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual Persuasion in Mass Media: A Computational Framework for Understanding Visual Communication./
Author:	Joo, Jungseock.
Description:	115 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
Contained By:	Dissertation Abstracts International77-02B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3725041
ISBN:	9781339089270

Visual Persuasion in Mass Media: A Computational Framework for Understanding Visual Communication.
Joo, Jungseock.

Visual Persuasion in Mass Media: A Computational Framework for Understanding Visual Communication. - 115 p.

Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2015.

This item is not available from ProQuest Dissertations & Theses.

Visuals play a vital role in human communication in the modern media landscape, but there have been little progress on a systematic analysis on massive visual data. In this dissertation, I introduce a novel computational paradigm that brings advanced techniques in computer vision and machine learning to bear on research questions in media studies and social science.

ISBN: 9781339089270Subjects--Topical Terms:

523869
Computer science.

Visual Persuasion in Mass Media: A Computational Framework for Understanding Visual Communication.
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245 1 0 $a Visual Persuasion in Mass Media: A Computational Framework for Understanding Visual Communication.
300 $a 115 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-02(E), Section: B.
500 $a Adviser: Song-Chun Zhu.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2015.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Visuals play a vital role in human communication in the modern media landscape, but there have been little progress on a systematic analysis on massive visual data. In this dissertation, I introduce a novel computational paradigm that brings advanced techniques in computer vision and machine learning to bear on research questions in media studies and social science.
520 $a I first examine how the news media leverage photographs to visually persuade their audiences. I developed a computational model that can predict the viewers' emotional perceptions from politicians' photographs in the mass media---by inferring hidden messages and communicative intents embodied in images. I applied the model to track the visual favorability of the U.S. President in the mass media, revealing strong correlations between the visuals from the mass media and the public opinion. Secondly, I investigate the role of facial appearance in social perception and trait inference and its impacts to real-world social events such as elections. The result shows that the automated trait recognition from the facial appearance of politicians predicts the major election outcomes in the U.S. up to 70% accuracy.
520 $a These approaches extend existing theories and behavioral studies to large-scale analysis, demonstrate the utility of computer vision and machine learning as novel research tools in computational communication science, and suggest a new area of research for computer vision, which goes beyond traditional syntactical scene understanding.
590 $a School code: 0031.
650 4 $a Computer science. $3 523869
650 4 $a Statistics. $3 517247
650 4 $a Mass communication. $3 2144804
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710 2 $a University of California, Los Angeles. $b Computer Science 0201. $3 2049859
773 0 $t Dissertation Abstracts International $g 77-02B(E).
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792 $a 2015
793 $a English
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