Version 1
: Received: 4 June 2019 / Approved: 6 June 2019 / Online: 6 June 2019 (13:15:28 CEST)
How to cite:
Pottinger, A. S. Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media. Preprints2019, 2019060051. https://doi.org/10.20944/preprints201906.0051.v1
Pottinger, A. S. Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media. Preprints 2019, 2019060051. https://doi.org/10.20944/preprints201906.0051.v1
Pottinger, A. S. Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media. Preprints2019, 2019060051. https://doi.org/10.20944/preprints201906.0051.v1
APA Style
Pottinger, A. S. (2019). Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media. Preprints. https://doi.org/10.20944/preprints201906.0051.v1
Chicago/Turabian Style
Pottinger, A. S. 2019 "Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media" Preprints. https://doi.org/10.20944/preprints201906.0051.v1
Abstract
An article's tone and framing not only influence an audience's perception of a story but may also reveal attributes of author identity and bias. Building upon prior media, psychological, and machine learning research, this neural network-based system detects those writing characteristics in ten news agencies' reporting, discovering patterns that, intentional or not, may reveal an agency's topical perspectives or common contextualization patterns. Specifically, learning linguistic markers of different organizations through a newly released open database, this probabilistic classifier predicts an article's publishing agency with 74% hidden test set accuracy given only a short snippet of text. The resulting model demonstrates how unintentional 'filter bubbles' can emerge in machine learning systems and, by comparing agencies' patterns and highlighting outlets' prototypical articles through an open source exemplar search engine, this paper offers new insight into news media bias.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.