Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0201385" target="_blank" >RIV/00216305:26230/26:0201385 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-031-71736-9_7" target="_blank" >http://dx.doi.org/10.1007/978-3-031-71736-9_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-71736-9_7" target="_blank" >10.1007/978-3-031-71736-9_7</a>

Result language

angličtina

Original language name

Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

Original language description

Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain bias indicators are discernible from content analysis, descriptors like political bias and fake news pose greater challenges. In this paper, we propose an extension to a recently presented news media reliability estimation method that focuses on modeling outlets and their longitudinal web interactions. Concretely, we assess the classification performance of four reinforcement learning strategies on a large news media hyperlink graph. Our experiments, targeting two challenging bias descriptors, factual reporting and political bias, showed a significant performance improvement at the source media level. Additionally, we validate our methods on the CLEF 2023 Check-That! Lab challenge, outperforming the reported results in both, F1-score and the official MAE metric. Furthermore, we contribute by releasing the largest annotated dataset of news source media, categorized with factual reporting and political bias labels. Our findings suggest that profiling news media sources based on their hyperlink interactions over time is feasible, offering a bird's-eye view of evolving media landscapes.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-031-71735-2

ISSN

—

e-ISSN

1611-3349

Number of pages

12

Pages from-to

127-138

Publisher name

Springer Nature

Place of publication

CHAM

Event location

Grenoble, France

Event date

Sep 9, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001336410600007