Unveiling the Hidden Agenda: Biases in News Reporting and Consumption
- URL: http://arxiv.org/abs/2301.05961v1
- Date: Sat, 14 Jan 2023 18:58:42 GMT
- Title: Unveiling the Hidden Agenda: Biases in News Reporting and Consumption
- Authors: Alessandro Galeazzi, Antonio Peruzzi, Emanuele Brugnoli, Marco
Delmastro, Fabiana Zollo
- Abstract summary: We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases.
We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions.
Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
- Score: 59.55900146668931
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: One of the most pressing challenges in the digital media landscape is
understanding the impact of biases on the news sources that people rely on for
information. Biased news can have significant and far-reaching consequences,
influencing our perspectives and shaping the decisions we make, potentially
endangering the public and individual well-being. With the advent of the
Internet and social media, discussions have moved online, making it easier to
disseminate both accurate and inaccurate information. To combat mis- and
dis-information, many have begun to evaluate the reliability of news sources,
but these assessments often only examine the validity of the news (narrative
bias) and neglect other types of biases, such as the deliberate selection of
events to favor certain perspectives (selection bias). This paper aims to
investigate these biases in various news sources and their correlation with
third-party evaluations of reliability, engagement, and online audiences. Using
machine learning to classify content, we build a six-year dataset on the
Italian vaccine debate and adopt a Bayesian latent space model to identify
narrative and selection biases. Our results show that the source classification
provided by third-party organizations closely follows the narrative bias
dimension, while it is much less accurate in identifying the selection bias.
Moreover, we found a nonlinear relationship between biases and engagement, with
higher engagement for extreme positions. Lastly, analysis of news consumption
on Twitter reveals common audiences among news outlets with similar ideological
positions.
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