Revealing Media Bias in News Articles, Hamborg

Автор: Hamborg
Название: Revealing Media Bias in News Articles
ISBN: 3031176928 ISBN-13(EAN): 9783031176920
Издательство: Springer
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Цена: 37260.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: Mohd Azraai Mohd Razman; Jessnor Arif Mat Jizat; N
Название: Embracing Industry 4.0
ISBN: 9811560242 ISBN-13(EAN): 9789811560248
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Поставка под заказ.
Описание: This book highlights selected articles from the electrical engineering track, with a focus on the latest trends in electrical and electronic engineering toward embracing Industry 4.0, as part of the Malaysian Technical Universities Conference on Engineering and Technology-MUCET 2019.

Автор: Zakaria Muhammad Aizzat, Abdul Majeed Anwar P. P., Hassan Mohd Hasnun Arif
Название: Advances in Mechatronics, Manufacturing, and Mechanical Engineering: Selected Articles from Mucet 2019
ISBN: 9811573115 ISBN-13(EAN): 9789811573118
Издательство: Springer
Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book highlights selected papers from the Mechanical Engineering track, with a focus on mechatronics and manufacturing, presented at the "Malaysian Technical Universities Conference on Engineering and Technology" (MUCET 2019).

Автор: Zakaria Muhammad Aizzat, Abdul Majeed Anwar P. P., Hassan Mohd Hasnun Arif
Название: Advances in Mechatronics, Manufacturing, and Mechanical Engineering: Selected Articles from Mucet 2019
ISBN: 9811573085 ISBN-13(EAN): 9789811573088
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book highlights selected papers from the Mechanical Engineering track, with a focus on mechatronics and manufacturing, presented at the "Malaysian Technical Universities Conference on Engineering and Technology" (MUCET 2019).

Автор: Georg Bol; Gholamreza Nakhaeizadeh; Karl-Heinz Vol
Название: Risk Measurement, Econometrics and Neural Networks
ISBN: 3790811521 ISBN-13(EAN): 9783790811520
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the first part approaches from traditional econometrics and innovative methods from machine learning such as neural nets are applied to financial issues. Neural Networks are successfully applied to different areas such as debtor analysis, forecasting and corporate finance.