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We further modeled what was written about the target medium in Wikipedia

What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context

ACL, pp.3364-3374, (2020)

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Abstract

Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online, has made it impossible to fact-check every single...More

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Introduction
  • The rise of the Web has made it possible for anybody to create a website or a blog and to become a news medium.
  • The issue became a general concern in 2016, a year marked by micro-targeted online disinformation and misinformation at an unprecedented scale, primarily in connection to Brexit and the US Presidential campaign
  • These developments gave rise to the term “fake news”, which can be defined as “false, often sensational, information disseminated under the guise of news reporting.”1 It was declared Word of the Year 2016 by Macquarie Dictionary and of Year 2017 by the Collins English Dictionary
Highlights
  • The rise of the Web has made it possible for anybody to create a website or a blog and to become a news medium
  • We argue that the audience of a news medium can be indicative of the political orientation of that medium
  • We used the Media Bias/Fact Check (MBFC) dataset, which consists of a list of news media along with their labels of both political bias and factuality of reporting
  • We model political bias on a 3-point scale, and the dataset got reduced to 864 news media
  • We have presented experiments in predicting the political ideology, i.e., left/center/right bias, and the factuality of reporting, i.e., high/mixed/low, of news media
  • We further modeled what was written about the target medium in Wikipedia
Methods
  • The authors used the Media Bias/Fact Check (MBFC) dataset, which consists of a list of news media along with their labels of both political bias and factuality of reporting.
  • Factuality is modeled on a 3-point scale: low, mixed, and high.
  • Political bias is modeled on a 7-point scale: extreme-left, left, center-left, center, center-right, right, and extremeright.
  • The authors model political bias on a 3-point scale, and the dataset got reduced to 864 news media.
Results
  • A study has shown that for some very viral claims, more than 50% of the sharing happens within the first ten minutes after posting the micro-post on social media (Zaman et al, 2014), and timing is of utmost importance.
Conclusion
  • Conclusion and Future

    Work

    The authors have presented experiments in predicting the political ideology, i.e., left/center/right bias, and the factuality of reporting, i.e., high/mixed/low, of news media.
  • The authors compared the textual content of what media publish vs who read it on social media, i.e., on Twitter, Facebook, and YouTube.
  • The authors further modeled what was written about the target medium in Wikipedia.
  • The authors have combined a variety of information sources, many of which were not explored for at least one of the target tasks, e.g., YouTube channels, political bias of the Facebook audience, and information from the profiles of the media followers on Twitter.
  • The evaluation results have shown that while what was written matters most, the social media context is important as it is complementary, and putting them all together yields sizable improvements over the state of the art
Summary
  • Introduction:

    The rise of the Web has made it possible for anybody to create a website or a blog and to become a news medium.
  • The issue became a general concern in 2016, a year marked by micro-targeted online disinformation and misinformation at an unprecedented scale, primarily in connection to Brexit and the US Presidential campaign
  • These developments gave rise to the term “fake news”, which can be defined as “false, often sensational, information disseminated under the guise of news reporting.”1 It was declared Word of the Year 2016 by Macquarie Dictionary and of Year 2017 by the Collins English Dictionary
  • Methods:

    The authors used the Media Bias/Fact Check (MBFC) dataset, which consists of a list of news media along with their labels of both political bias and factuality of reporting.
  • Factuality is modeled on a 3-point scale: low, mixed, and high.
  • Political bias is modeled on a 7-point scale: extreme-left, left, center-left, center, center-right, right, and extremeright.
  • The authors model political bias on a 3-point scale, and the dataset got reduced to 864 news media.
  • Results:

    A study has shown that for some very viral claims, more than 50% of the sharing happens within the first ten minutes after posting the micro-post on social media (Zaman et al, 2014), and timing is of utmost importance.
  • Conclusion:

    Conclusion and Future

    Work

    The authors have presented experiments in predicting the political ideology, i.e., left/center/right bias, and the factuality of reporting, i.e., high/mixed/low, of news media.
  • The authors compared the textual content of what media publish vs who read it on social media, i.e., on Twitter, Facebook, and YouTube.
  • The authors further modeled what was written about the target medium in Wikipedia.
  • The authors have combined a variety of information sources, many of which were not explored for at least one of the target tasks, e.g., YouTube channels, political bias of the Facebook audience, and information from the profiles of the media followers on Twitter.
  • The evaluation results have shown that while what was written matters most, the social media context is important as it is complementary, and putting them all together yields sizable improvements over the state of the art
Tables
  • Table1: Label counts in the dataset
  • Table2: Political bias prediction: ablation study of the proposed features. Dim refers to the number of features, whereas (c) and (en) indicate whether the features are concatenated or an ensemble was used, respectively
  • Table3: Factuality of reporting: ablation study of the proposed features. Dim refers to the number of features, whereas (c) and (en) indicate whether the features are concatenated or an ensemble was used, respectively
Download tables as Excel
Related work
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