Methodological Considerations in Semantic Polarization Research

20 Jun 2024

Authors:

(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);

(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);

(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ).

Table of Links

Abstract and Introduction

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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