A study of Media Polarization with Stylometry Methods

This research investigated the U.S. media polarization with stylometry approaches, creating classification models to identify the political leanings of news articles based on their writing style. We tested the models of authorship attribution, while controlling for topic, stance, and style, and appl...

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Main Authors: Yifei Hu, Julia Rayz
Format: Article
Language:English
Published: LibraryPress@UF 2021-04-01
Series:Proceedings of the International Florida Artificial Intelligence Research Society Conference
Subjects:
Online Access:https://journals.flvc.org/FLAIRS/article/view/128477
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author Yifei Hu
Julia Rayz
author_facet Yifei Hu
Julia Rayz
author_sort Yifei Hu
collection DOAJ
description This research investigated the U.S. media polarization with stylometry approaches, creating classification models to identify the political leanings of news articles based on their writing style. We tested the models of authorship attribution, while controlling for topic, stance, and style, and applied them to media companies and their identity within a political spectrum. We tested style features that could include semantic and/or sentiment-related information, such as stance taking, with features that seemingly do not capture it. We were able to successfully classify articles as left-leaning or right-learning regardless of stance. Finally, we provide an analysis of some of the patterns that we found.
format Article
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institution OA Journals
issn 2334-0754
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publishDate 2021-04-01
publisher LibraryPress@UF
record_format Article
series Proceedings of the International Florida Artificial Intelligence Research Society Conference
spelling doaj-art-2d7e8fae0c3b4cd4b76f20b7a01e13072025-08-20T01:52:19ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622021-04-013410.32473/flairs.v34i1.12847762871A study of Media Polarization with Stylometry MethodsYifei Hu0Julia Rayz1Purdue UniversityPurdue UniversityThis research investigated the U.S. media polarization with stylometry approaches, creating classification models to identify the political leanings of news articles based on their writing style. We tested the models of authorship attribution, while controlling for topic, stance, and style, and applied them to media companies and their identity within a political spectrum. We tested style features that could include semantic and/or sentiment-related information, such as stance taking, with features that seemingly do not capture it. We were able to successfully classify articles as left-leaning or right-learning regardless of stance. Finally, we provide an analysis of some of the patterns that we found.https://journals.flvc.org/FLAIRS/article/view/128477natural language processingmedia polarizationauthorship attributionstylometry
spellingShingle Yifei Hu
Julia Rayz
A study of Media Polarization with Stylometry Methods
Proceedings of the International Florida Artificial Intelligence Research Society Conference
natural language processing
media polarization
authorship attribution
stylometry
title A study of Media Polarization with Stylometry Methods
title_full A study of Media Polarization with Stylometry Methods
title_fullStr A study of Media Polarization with Stylometry Methods
title_full_unstemmed A study of Media Polarization with Stylometry Methods
title_short A study of Media Polarization with Stylometry Methods
title_sort study of media polarization with stylometry methods
topic natural language processing
media polarization
authorship attribution
stylometry
url https://journals.flvc.org/FLAIRS/article/view/128477
work_keys_str_mv AT yifeihu astudyofmediapolarizationwithstylometrymethods
AT juliarayz astudyofmediapolarizationwithstylometrymethods
AT yifeihu studyofmediapolarizationwithstylometrymethods
AT juliarayz studyofmediapolarizationwithstylometrymethods