<div>Media polarization is a serious issue that can affect someone's views, ranging from a scientific fact to the perceived results of a presidential election. The media outlets in the United States are aligned along political spectrum representing different stances on various issues. Without providing any false information (but usually by omitting some facts), media outlets can report events by deliberately using the words and styles that favor particular political positions. <br></div>This research investigated the U.S. media polarization with authorship attribution approaches, analyzing stylistic differences between the left-leaning and right-leaning media and discovering specific linguistic patterns that made the news articles display biased political attitudes. Several models of authorship attribution were tested while controlling for topic, stance, and style, and were applied to media companies and their identity within a political spectrum. Style features that were compared included semantic and/or sentiment-related information, such as stance taking, with features that seemingly do not capture it, such as part of speech tags. The results demonstrate that a successful classification of articles as left-leaning or right-learning is possible regardless of their stance. Finally, we provide an analysis of the patterns that we found.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13350812 |
| Date | 14 December 2020 |
| Creators | Yifei Hu (9193709) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/A_Study_of_Media_Polarization_with_Authorship_Attribution_Methods/13350812 |
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