Preprint Article Version 1 This version is not peer-reviewed

Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media

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. Preprints 2019, 2019060051 (doi: 10.20944/preprints201906.0051.v1). Pottinger, A.S. Machine Learning Techniques for Detecting Identifying Linguistic Patterns in News Media. Preprints 2019, 2019060051 (doi: 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.

Supplementary and Associated Material

https://doi.org/10.24433/CO.5660509.v2: Containerized code for reproducibility
http://dx.doi.org/10.17504/protocols.io.3j6gkre: Supplemental protocol description
https://whowrotethis.com/data: Open dataset download and information page
https://whowrotethis.com: Open source exemplar search engine

Subject Areas

NLP, news media, bias, neural networking, LSTM, information retrieval, filter bubble

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