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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.
This book constitutes the proceedings of the 14th International Conference on Information in Contemporary Society, iConference 2019, held in Washington, DC, USA, in March/April 2019. The 44 full papers and 33 short papers presented in this volume were carefully reviewed and selected from 133 submitted full papers and 88 submitted short papers. The papers are organized in the following topical sections: Scientific work and data practices; methodological concerns in (big) data research; concerns about “smart” interactions and privacy; identity questions in online communities; measuring and tracking scientific literature; limits and affordances of automation; collecting data about vulnerable populations; supporting communities through public libraries and infrastructure; information behaviors in academic environments; data-driven storytelling and modeling; online activism; digital libraries, curation and preservation; social-media text mining and sentiment analysis; data and information in the public sphere; engaging with multi-media content; understanding online behaviors and experiences; algorithms at work; innovation and professionalization in technology communities; information behaviors on Twitter; data mining and NLP; informing technology design through offline experiences; digital tools for health management; environmental and visual literacy; and addressing social problems in iSchool research.
In "The Impact of Biases on the Reportorial and Editorial Processes in American Newsrooms," Swift explores the idea of media bias and its implications on eight editors and reporters from newsrooms across the United States. Based on extensive interviews, Swift investigates six forms of media bias and their impact by using Table 1, "Automated identification of media bias in news articles: an interdisciplinary literature review," as a framework for assessment. She argues for an established set of guidelines for reporters and editors to address the following types of bias -- event selection, source selection, commission and omission, labeling and word choice, story placement and size allocation, and overall spin. As the 2022-23 editor-in-chief of The Daily Collegian, she provides guidelines to identify the ways in which Penn State's longstanding independent newspaper, as well as other newsrooms, can integrate possible solutions into their journalistic procedures moving forward to mitigate and/or eliminate media bias in their reportorial and editorial processes.
Much of framing scholarship focuses either exclusively on the analysis of words or of visuals. This book aims to address this gap by proposing a six-step approach to the analysis of verbal frames, visual frames and the interplay between them—an integrative framing analysis. This approach is then demonstrated through a study investigating the way words and visuals are used to frame people living with HIV/AIDS in various communication contexts: the news, public service announcements and special interest publications. This application of integrative framing analysis reveals differences between verbal frames and visual frames in the same messages, underscoring the importance of looking at these frames together.
This analysis of how the ability to participate in society online affects political and economic opportunity finds that technology use matters in wages and income and civic participation and voting. Just as education has promoted democracy and economic growth, the Internet has the potential to benefit society as a whole. Digital citizenship, or the ability to participate in society online, promotes social inclusion. But statistics show that significant segments of the population are still excluded from digital citizenship. The authors of this book define digital citizens as those who are online daily. By focusing on frequent use, they reconceptualize debates about the digital divide to include both the means and the skills to participate online. They offer new evidence (drawn from recent national opinion surveys and Current Population Surveys) that technology use matters for wages and income, and for civic engagement and voting. Digital Citizenship examines three aspects of participation in society online: economic opportunity, democratic participation, and inclusion in prevailing forms of communication. The authors find that Internet use at work increases wages, with less-educated and minority workers receiving the greatest benefit, and that Internet use is significantly related to political participation, especially among the young. The authors examine in detail the gaps in technological access among minorities and the poor and predict that this digital inequality is not likely to disappear in the near future. Public policy, they argue, must address educational and technological disparities if we are to achieve full participation and citizenship in the twenty-first century.
This two-volume set LNCS 12645-12646 constitutes the refereed proceedings of the 16th International Conference on Diversity, Divergence, Dialogue, iConference 2021, held in Beijing, China, in March 2021. The 32 full papers and the 59 short papers presented in this two-volume set were carefully reviewed and selected from 225 submissions. They cover topics such as: AI and machine learning; data science; human-computer interaction; social media; digital humanities; education and information literacy; information behavior; information governance and ethics; archives and records; research methods; and institutional management.
Nine in ten Americans believe the media are biased. Trust in journalists ranks beneath that in lawyers, and even the media themselves regularly portray their own industry as slanted toward Democrats and liberals. These perceptions, however, do not coincide with reality, as David Niven reveals in his bold new take on an often-debated subject. Tilt? The Search for Media Bias presents the first comprehensive review of the charges, the evidence, and the effects, beginning with a simple but altogether overlooked premise: to measure media bias or fairness, one has to have a fair baseline with which to compare coverage. Using situations in which presidents, governors, mayors, and members of Congress from different political parties have produced the same results in office, Tilt? compares media coverage of Democrats and Republicans in situations in which they clearly deserved equal treatment. The lack of evidence for partisan media bias is only part of the story. The media cover allegations of bias as if their industry has already been tried and convicted, while the American people readily accepted the premise that their main sources of information are selfishly slanted toward reporters' personal political agendas. Niven's findings, unmistakable and consistent, reveal that when the output of politicians is the same, media coverage follows—a conclusion that is as provocative as it is timely and necessary.