“Reliable Sources” in Cable News: Analyzing Network Fragmentation in Coverage of Reform Policy

Conway-Silva, Bethany A.; Ervin, Jennifer; Kenski, Kate

doi:10.1080/1461670x.2020.1724184

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Mapping sources cited in cable news reveals that channels of different partisan leans have distinct networks of news sources with little overlap, and an analysis of the think tanks cited on different cable news networks has demonstrated a preference for think tanks associated with the channels' partisan leans. [13], [14] Social science literature has also made attempts to analyze language in news media, although they are oftentimes computationally limited. A large amount of media bias analysis in social science has been qualitative in nature, which allows for maximum interpretation without restricting the analysis to specific methodological techniques [15].…”

Section: B Social Science Literature and Media Biasmentioning

confidence: 99%

Developing a Natural Language Understanding Model to Characterize Cable News Bias

Benson,

Cruickshank

2024

IEEE Access

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Media bias has been extensively studied by both social and computational sciences. However, current work still has a large reliance on human input and subjective assessment to label biases. This is especially true for cable news, which has a continued presence in American media but a lack of textbased bias identification in research. To address these issues, we develop an unsupervised machine learning method to characterize the bias of cable news programs without any human input. This method relies on the analysis of what topics are mentioned through Named Entity Recognition and how those topics are discussed through Stance Analysis in order to cluster programs with similar biases together. Applying our method to 2020 cable news transcripts, we find that cable news programs tend to cluster together consistently over time and roughly correspond to the cable news network of the program. This method reveals the potential for future tools to more objectively assess media bias and characterize unfamiliar media environments, and the empirical results insight into the nature of bias in American cable news programs.

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Section: B Social Science Literature and Media Biasmentioning

confidence: 99%

Developing a Natural Language Understanding Model to Characterize Cable News Bias

Benson,

Cruickshank

2024

IEEE Access

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“…Yamamoto and Nah [2018] suggest that community‐level factors, such as social and political trust, contribute to newspaper credibility. Conway‐Silva, Ervin, and Kenski [2020] reveal that cable news uses different sourcing patterns to produce biased coverage. This literature primarily focuses on political bias in media coverage.…”

Section: Literature Institutional Background and Hypothesesmentioning

confidence: 99%

News Bias in Financial Journalists’ Social Networks

2024

J of Accounting Research

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Connected financial journalists—those with working relationships, common school ties, or social media connections to company management—introduce a marked media slant into their news coverage. Using a comprehensive set of newspaper articles covering mergers and acquisition (M&A) transactions from 1997 to 2016, I find that connected journalists use significantly fewer negative words in their coverage of connected acquirers. These journalists are also more likely to quote connected executives and include less accurate language in their reporting. Moreover, they tend to portray other firms in the same network in a less negative light. Journalists’ favoritism bias has implications for both capital market outcomes and their careers. I find that acquirers whose M&As are covered by connected journalists receive significantly higher stock returns on the news article publication date. However, these acquirers’ stock prices reverse in the long term, suggesting market overreaction to news covered by connected journalists. Around M&A transactions, connected articles are correlated with increased bid competition and deal premiums. In terms of future career development, connected journalists are more likely to leave journalism and join their associated industries in the long run. Taken together, the evidence suggests that financial journalists’ personal networks promote news bias that potentially hinders the efficient dissemination of information.

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Health Journalists’ Social Media Sourcing During the Early Outbreak of the Public Health Emergency

Zhang

Zhu

2022

Journalism Practice

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“Reliable Sources” in Cable News: Analyzing Network Fragmentation in Coverage of Reform Policy

Cited by 5 publications

References 34 publications

Developing a Natural Language Understanding Model to Characterize Cable News Bias

Developing a Natural Language Understanding Model to Characterize Cable News Bias

News Bias in Financial Journalists’ Social Networks

Health Journalists’ Social Media Sourcing During the Early Outbreak of the Public Health Emergency

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