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Methodological Considerations in Semantic Polarization Research by@editorialist
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Methodological Considerations in Semantic Polarization Research

by THE Tech EditorialistJune 20th, 2024
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The appendix of the study provides detailed insights into the methodological nuances and data analysis approaches employed in researching semantic polarization in media discourse, offering valuable information for academic researchers in the field
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Authors:

(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: [email protected]);

(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: [email protected]);

(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: [email protected] ).

Abstract and Introduction

Related Work

Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020)

Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020

Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse

Discussion and Ethics Statement

Appendix and References

Appendix

Table 7: ADF test results showing whether the Twitter and TV news time series data are stationary (stat) or nonstationary (non-stat).


Table 8: Top 10 tokens most predictive of how CNN and Fox News TV stations and Twitter users replying to @CNN and@FoxNews use keywords topically related to racism.


Table 9: Top 10 tokens most predictive of how CNN and Fox News TV stations and Twitter users replying to @CNN and @FoxNews use keywords topically related to immigration.


Table 10: Top 10 tokens most predictive of how CNN and Fox News TV stations and Twitter users replying to @CNN and@FoxNews use keywords topically related to climate change.


Table 11: Top 10 tokens most predictive of how CNN and Fox News TV stations and Twitter users replying to @CNN and@FoxNews use keywords topically related to Black Lives Matter


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This paper is under CC 4.0 license.


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