This open access book presents an interdisciplinary approach to reveal biases in English news
articles reporting on a given political event. The approach named person-oriented framing
analysis identifies the coverage's different perspectives on the event by assessing how
articles portray the persons involved in the event. In contrast to prior automated approaches
the identified frames are more meaningful and substantially present in person-oriented news
coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe
problems caused by slanted news coverage and identifies the research gap that motivated the
research described in this thesis. Chapter 2 discusses manual analysis concepts and exemplary
studies from the social sciences and automated approaches mostly from computer science and
computational linguistics to analyze and reveal media bias. This way it identifies the
strengths and weaknesses of current approaches for identifying and revealing media bias.
Chapter 3 discusses the solution design space to address the identified research gap and
introduces person-oriented framing analysis (PFA) a new approach to identify substantial
frames and to reveal slanted news coverage. Chapters 4 and 5 detail target concept analysis and
frame identification the first and second component of PFA. Chapter 5 also introduces the
first large-scale dataset and a novel model for target-dependent sentiment classification (TSC)
in the news domain. Eventually Chapter 6 introduces Newsalyze a prototype system to reveal
biases to non-expert news consumers by using the PFA approach. In the end Chapter 7 summarizes
the thesis and discusses the strengths and weaknesses of the thesis to derive ideas for future
research on media bias.This book mainly targets researchers and graduate students from computer
science computational linguistics political science and further social sciences who want to
get an overview of the relevant state of the art in the other related disciplines and
understand and tackle the issue of bias from a more effective interdisciplinary viewpoint.
