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 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 References Allen, J.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2020. Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14): eaay3539. Barnard, S. R. 2018. Citizens at the Gates. Twitter, Networked Publics and The Transformation of American Journalism. Cham, Sveitsi: Palgrave Macmillan. Cheung, Y.-W.; and Lai, K. S. 1995. Lag Order and Critical Values of the Augmented Dickey–Fuller Test. Journal of Business & Economic Statistics, 13(3): 277–280. Chinn, S.; Hart, P. S.; and Soroka, S. 2020. Politicization and Polarization in Climate Change News Content, 1985-2017. Science Communication, 42(1): 112–129. Dash, S.; Mishra, D.; Shekhawat, G.; and Pal, J. 2022. Divided We Rule: Influencer Polarization on Twitter during Political Crises in India. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 16: 135–146. Demszky, D.; Movshovitz-Attias, D.; Ko, J.; Cowen, A.; Nemade, G.; and Ravi, S. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4040–4054. Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 4171–4186. Dutta, S.; Ma, J.; and Choudhury, M. D. 2018. Measuring the Impact of Anxiety on Online Social Interactions. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 12(1): 584–587. Ebeling, R.; Saenz, C. A. C.; Nobre, J. C.; and Becker, K. 2022. Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16: 159–170. Entman, R. M. 2003. Cascading activation: Contesting the White House’s frame after 9/11. Political Communication,, 20(4): 415– 432. Eveland, W. P.; Seo, M.; and Marton, K. 2002. Learning From the News in Campaign 2000: An Experimental Comparison of TV News, Newspapers, and Online News. Media Psychology, 4(4): 353–378. Garimella, K.; Smith, T.; Weiss, R.; and West, R. 2021. Political Polarization in Online News Consumption. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 15: 152–162. Gentzkow, M.; Shapiro, J. M.; and Taddy, M. 2019. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4): 1307–1340. Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2: 329–352. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. Conference on Empirical Methods in Natural Language Processing., 2116–2121. Hoey, M. 2005. Lexical Priming: A new theory of words and language. London: Routledge. Horning, M. 2018. The pundit problem: A look at bias and negativity in cable news coverage as the 2016 election came to a close. The 2016 American Presidential Campaign and the News, 77–99. Jang, S. M.; and Hart, P. S. 2015. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change, 32: 11–17. Kim, E.; Lelkes, Y.; and McCrain, J. 2022. Measuring dynamic media bias. Proceedings of the National Academy of Sciences, 119(32): e2202197119. Kutuzov, A.; Øvrelid, L.; Szymanski, T.; and Velldal, E. 2018. Diachronic word embeddings and semantic shifts: a survey. In Proceedings of the 27th International Conference on Computational Linguistics, 1384–1397. McCombs, M. 1997. Building Consensus: The News Media’s Agenda-Setting Roles. Political Communication, 14(4): 433–443. McLeod, J. M.; Scheufele, D. A.; and Moy, P. 1999. Community, Communication, and Participation: The Role of Mass Media and Interpersonal Discussion in Local Political Participation. Political Communication, 16(3): 315–336. Muise, D.; Hosseinmardi, H.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2022. Quantifying partisan news diets in Web and TV audiences. Science Advances, 8(28): eabn0083. Papacharissi, Z. 2009. Journalism and citizenship. Routledge London. Polignano, M.; Basile, V.; Basile, P.; Gabrieli, G.; Vassallo, M.; and Bosco, C. 2022. A hybrid lexicon-based and neural approach for explainable polarity detection. Information Processing & Management, 59(5): 103058. Recuero, R.; Soares, F. B.; and Gruzd, A. 2020. Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 14: 569–578. Rogstad, I. 2016. Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2): 142–158. Russell Neuman, W.; Guggenheim, L.; Mo Jang, S.; and Bae, S. Y. 2014. The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2): 193–214. Sap, M.; Card, D.; Gabriel, S.; Choi, Y.; and Smith, N. A. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1668–1678. Scheufele, D. A. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3): 297–316. Sundararajan, M.; Taly, A.; and Yan, Q. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, 3319–3328. Tuba, M.; Akashe, S.; and Joshi, A., eds. 2020. Information and Communication Technology for Sustainable Development, volume 933. Advances in Intelligent Systems and Computing. Westfall, J.; Van Boven, L.; Chambers, J. R.; and Judd, C. M. 2015. Perceiving Political Polarization in the United States: Party Identity Strength and Attitude Extremity Exacerbate the Perceived Partisan Divide. Perspectives on Psychological Science, 10(2): 145– 158. Yang, M.; Wen, X.; Lin, Y.-R.; and Deng, L. 2017. Quantifying Content Polarization on Twitter. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), 299– 308. Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2020. BERTScore: Evaluating Text Generation with BERT. arXiv:1904.09675. This paper is available on arxiv under CC 4.0 license. 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 ). Authors: 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 Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) 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 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 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Appendix References Allen, J.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2020. Evaluating the fake news problem at the scale of the information ecosystem. Science Advances, 6(14): eaay3539. Barnard, S. R. 2018. Citizens at the Gates. Twitter, Networked Publics and The Transformation of American Journalism. Cham, Sveitsi: Palgrave Macmillan. Cheung, Y.-W.; and Lai, K. S. 1995. Lag Order and Critical Values of the Augmented Dickey–Fuller Test. Journal of Business & Economic Statistics, 13(3): 277–280. Chinn, S.; Hart, P. S.; and Soroka, S. 2020. Politicization and Polarization in Climate Change News Content, 1985-2017. Science Communication, 42(1): 112–129. Dash, S.; Mishra, D.; Shekhawat, G.; and Pal, J. 2022. Divided We Rule: Influencer Polarization on Twitter during Political Crises in India. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 16: 135–146. Demszky, D.; Movshovitz-Attias, D.; Ko, J.; Cowen, A.; Nemade, G.; and Ravi, S. 2020. GoEmotions: A dataset of fine-grained emotions. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4040–4054. Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 4171–4186. Dutta, S.; Ma, J.; and Choudhury, M. D. 2018. Measuring the Impact of Anxiety on Online Social Interactions. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 12(1): 584–587. Ebeling, R.; Saenz, C. A. C.; Nobre, J. C.; and Becker, K. 2022. Analysis of the Influence of Political Polarization in the Vaccination Stance: The Brazilian COVID-19 Scenario. Proceedings of the International AAAI Conference on Web and Social Media, 16: 159–170. Entman, R. M. 2003. Cascading activation: Contesting the White House’s frame after 9/11. Political Communication,, 20(4): 415– 432. Eveland, W. P.; Seo, M.; and Marton, K. 2002. Learning From the News in Campaign 2000: An Experimental Comparison of TV News, Newspapers, and Online News. Media Psychology, 4(4): 353–378. Garimella, K.; Smith, T.; Weiss, R.; and West, R. 2021. Political Polarization in Online News Consumption. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 15: 152–162. Gentzkow, M.; Shapiro, J. M.; and Taddy, M. 2019. Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4): 1307–1340. Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2: 329–352. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. Conference on Empirical Methods in Natural Language Processing., 2116–2121. Hoey, M. 2005. Lexical Priming: A new theory of words and language. London: Routledge. Horning, M. 2018. The pundit problem: A look at bias and negativity in cable news coverage as the 2016 election came to a close. The 2016 American Presidential Campaign and the News, 77–99. Jang, S. M.; and Hart, P. S. 2015. Polarized frames on “climate change” and “global warming” across countries and states: Evidence from Twitter big data. Global Environmental Change, 32: 11–17. Kim, E.; Lelkes, Y.; and McCrain, J. 2022. Measuring dynamic media bias. Proceedings of the National Academy of Sciences, 119(32): e2202197119. Kutuzov, A.; Øvrelid, L.; Szymanski, T.; and Velldal, E. 2018. Diachronic word embeddings and semantic shifts: a survey. In Proceedings of the 27th International Conference on Computational Linguistics, 1384–1397. McCombs, M. 1997. Building Consensus: The News Media’s Agenda-Setting Roles. Political Communication, 14(4): 433–443. McLeod, J. M.; Scheufele, D. A.; and Moy, P. 1999. Community, Communication, and Participation: The Role of Mass Media and Interpersonal Discussion in Local Political Participation. Political Communication, 16(3): 315–336. Muise, D.; Hosseinmardi, H.; Howland, B.; Mobius, M.; Rothschild, D.; and Watts, D. J. 2022. Quantifying partisan news diets in Web and TV audiences. Science Advances, 8(28): eabn0083. Papacharissi, Z. 2009. Journalism and citizenship. Routledge London. Polignano, M.; Basile, V.; Basile, P.; Gabrieli, G.; Vassallo, M.; and Bosco, C. 2022. A hybrid lexicon-based and neural approach for explainable polarity detection. Information Processing & Management, 59(5): 103058. Recuero, R.; Soares, F. B.; and Gruzd, A. 2020. Hyperpartisanship, Disinformation and Political Conversations on Twitter: The Brazilian Presidential Election of 2018. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), 14: 569–578. Rogstad, I. 2016. Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2): 142–158. Russell Neuman, W.; Guggenheim, L.; Mo Jang, S.; and Bae, S. Y. 2014. The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2): 193–214. Sap, M.; Card, D.; Gabriel, S.; Choi, Y.; and Smith, N. A. 2019. The Risk of Racial Bias in Hate Speech Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 1668–1678. Scheufele, D. A. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3): 297–316. Sundararajan, M.; Taly, A.; and Yan, Q. 2017. Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, 3319–3328. Tuba, M.; Akashe, S.; and Joshi, A., eds. 2020. Information and Communication Technology for Sustainable Development, volume 933. Advances in Intelligent Systems and Computing. Westfall, J.; Van Boven, L.; Chambers, J. R.; and Judd, C. M. 2015. Perceiving Political Polarization in the United States: Party Identity Strength and Attitude Extremity Exacerbate the Perceived Partisan Divide. Perspectives on Psychological Science, 10(2): 145– 158. Yang, M.; Wen, X.; Lin, Y.-R.; and Deng, L. 2017. Quantifying Content Polarization on Twitter. In 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC), 299– 308. Zhang, T.; Kishore, V.; Wu, F.; Weinberger, K. Q.; and Artzi, Y. 2020. BERTScore: Evaluating Text Generation with BERT. arXiv:1904.09675. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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