SlowGuess/NAACL_2021_OCR huggingface dataset:
Language Technologies, Volume 1 (Long and Short Papers), pages 1401-1407.
Sabina Hartnett. 2019. Willkommenskultur: A computational and socio-linguistic study of modern german discourse on migrant populations. Transit, 12(1).
Tobias Heidenreich, Fabienne Lind, Jakob-Moritz Eberl, and Hajo G Boomgaarden. 2019. Media Framing Dynamics of the 'European Refugee Crisis': A Comparative Topic Modelling Approach. Journal of Refugee Studies, 32(Special_Issue_1):i172-i182.
Marc Helbling. 2014. Framing Immigration in Western Europe. Journal of Ethnic and Migration Studies, 40(1):21-41.
Nicole Hemmer. 2016. Messengers of the right: Conservative media and the transformation of American politics. University of Pennsylvania Press.
Jackie Hogan and Kristin Haltinner. 2015. Floods, Invaders, and Parasites: Immigration Threat Narratives and Right-Wing Populism in the USA, UK and Australia. Journal of Intercultural Studies, 36(5):520-543.
Jan Fredrik Hovden and Hilmar Mjelde. 2019. Increasingly Controversial, Cultural, and Political: The Immigration Debate in Scandinavian Newspapers 1970-2016. Javnost, 26(2):138-157.
Shanto Iyengar. 1990. Framing responsibility for political issues: The case of poverty. Political Behavior, 12(1):19-40.
Shanto Iyengar. 1991. Is anyone responsible? How television frames political issues. University of Chicago Press.
Shanto Iyengar, Helmut Norpoth, and Kyu S Hahn. 2004. Consumer demand for election news: The horserace sells. The Journal of Politics, 66(1):157-175.
Kristen Johnson, Di Jin, and Dan Goldwasser. 2017. Modeling of political discourse framing on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media, volume 11.
John T Jost, Jack Glaser, Arie W Kruglanski, and Frank J Sulloway. 2003. Political conservatism as motivated social cognition. Psychological bulletin, 129(3):339.
Shima Khanehzar, Andrew Turpin, and Gosia Mikolajczak. 2019. Predicting Political Frames Across Policy Issues and Contexts. In Proceedings of the 17th Workshop of the Australasian Language Technology Association, pages 101-106.
Klaus Krippendorff. 2013. Content Analysis: An Introduction to Its Methodology. Sage Publications.
Haewoon Kwak, Jisun An, and Yong-Yeol Ahn. 2020. A systematic media frame analysis of 1.5 million New York Times articles from 2000 to 2017. In 12th ACM Conference on Web Science, pages 305-314.
Andrea Lawlor. 2015. Local and National Accounts of Immigration Framing in a Cross-national Perspective. Journal of Ethnic and Migration Studies, 41(6):918-941.
Sophie Lecheler, Linda Bos, and Rens Vliegenthart. 2015. The mediating role of emotions: News framing effects on opinions about immigration. Journalism & Mass Communication Quarterly, 92(4):812-838.
Michael T Light and Ty Miller. 2018. Does undocumented immigration increase violent crime? Criminology, 56(2):370-401.
Siyi Liu, Lei Guo, Kate Mays, Margrit Betke, and Derry Tanti Wijaya. 2019a. Detecting frames in news headlines and its application to analyzing news framing trends surrounding US gun violence. In Proceedings of the 23rd Conference on Computational Natural Language Learning, pages 504-514.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019b. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
Sharon Meraz and Zizi Papacharissi. 2013. Networked Gatekeeping and Networked Framing on #Egypt. The International Journal of Press/Politics, 18(2):138-166.
James Milroy. 2001. Language ideologies and the consequences of standardization. Journal of sociolinguistics, 5(4):530-555.
Joshua R Minot, Michael V Arnold, Thayer Alshaabi, Christopher M Danforth, and Peter Sheridan Dodds. 2020. Ratioing the president: An exploration of public engagement with Obama and Trump on Twitter. arXiv preprint arXiv:2006.03526.
Alan Mislove, Sune Lehmann, Yong-Yeol Ahn, Jukka Pekka Onnela, and James Rosenquist. 2011. Understanding the demographics of Twitter users. In Proceedings of the International AAAI Conference on Web and Social Media, volume 5.
Fred Morstatter, Liang Wu, Uraz Yavanoglu, Stephen R. Corman, and Huan Liu. 2018. Identifying Framing Bias in Online News. ACM Transactions on Social Computing, 1(2):1-18.
Nona Naderi and Graeme Hirst. 2017. Classifying frames at the sentence level in news articles. International Conference Recent Advances in Natural Language Processing, RANLP, 2017-Septe:536-542.
Innocent Ndubuisi-Obi, Sayan Ghosh, and David Jurrgens. 2019. Wetin dew with these comments? Modeling sociolinguistic factors affecting codeswitching behavior in Nigerian online discussions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6204-6214.
Zizi Papacharissi and Maria De Fatima Oliveira. 2008. News frames terrorism: A comparative analysis of frames employed in terrorism coverage in U.S. and U.K. newspapers. International Journal of Press/Politics, 13(1):52-74.
Shamik Roy and Dan Goldwasser. 2020. Weakly supervised learning of nuanced frames for analyzing polarization in news media. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 7698-7716.
W. Russell Neuman, Lauren Guggenheim, S. Mo Jang, and Soo Young Bae. 2014. The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2):193-214.
Dietram A. Scheufele. 1999. Framing as a theory of media effects. Journal of Communication, 49(1):103-122.
Holli A Semetko and Patti M Valkenburg. 2000. Framing European politics: A content analysis of press and television news. Journal of communication, 50(2):93-109.
Philippa Shoemark, Debnil Sur, Luke Shrimpton, Iain Murray, and Sharon Goldwater. 2017. Aye or naw, whit dae ye hint? scottish independence and linguistic identity on social media. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 1239-1248.
Eugenia Siapera, Moses Boudourides, Sergios Lenis, and Jane Suiter. 2018. Refugees and Network Publics on Twitter: Networked Framing, Affect, and Capture. Social Media and Society, 4(1).
Robert Courtney Smith. 2017. Dont let the illegals vote!: The myths of illegal latino voters and voter fraud in contested local immigrant integration. The Russell Sage Foundation Journal of the Social Sciences, 3(4):148-175.
Francesco Somaini. 2019. News stories framed episodically offer more diversified portrayals of immigrants. Newspaper Research Journal, 40(2):190-210.
Burton Speakman and Marcus Funk. 2020. News, nationalism, and hegemony: The formation of consistent issue framing throughout the U.S. political right. *Mass Communication and Society*, 23(5):656-681.
Ian Stewart, Yuval Pinter, and Jacob Eisenstein. 2018. Si o no, que penses? Catalanian independence and linguistic identity on social media. In Proceedings
of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 136-141.
Kjersti Thorbjørnsrud. 2015. Framing irregular immigration in western media. American Behavioral Scientist, 59(7):771-782.
Adriano Udani and David C Kimball. 2018. Immigrant resentment and voter fraud beliefs in the US electorate. *American Politics Research*, 46(3):402-433.
Baldwin van Gorp. 2005. Where is the frame? : Victims and intruders in the Belgian press coverage of the asylum issue. European Journal of Communication, 20(4):484-507.
A Frame distribution in annotated data
Figure 5 shows the distribution of frames as a fraction of total tweets in the annotated data.
Figure 5: Distribution of frames in annotated data.
B Inter-annotator agreement plots
Figures 6 and 7 show inter-annotator agreement (Krippendorff's  $\alpha$ ) across frame types.
Figure 6: Inter-annotator agreement between first author and other coders before consensus-coding.
Figure 7: Agreement between each coder and consensus annotations before consensus-coding.
C Frame detection performance
Tables 5-8 and Figures 8-9 provide details about the fine-tuned RoBERTa models' performance.
Frame Type	Precision	Recall	F1-score	LRAP
Issue-Generic Policy	0.722	0.727	0.716	0.745
Issue-Specific	0.667	0.493	0.550	0.785
Narrative	0.780	0.884	0.825	0.896
Table 5: Performance by frame type on dev set.
Issue-Generic	Issue-Specific	Narrative
Human-Machine	0.443	0.488	0.421
Human-Human	0.417	0.491	0.458
Table 6: Average Krippendorff  $\alpha$  agreement between human annotators and machine-predicted labels (top row) and between human annotator pairs (bottom row). Overall, our classifiers had similar agreement with human annotators as humans did with one another.
Figure 8: F1 score of logistic regression (1,2-gram features) and fine-tuned RoBERTa for each frame and frame support in evaluation sets. RoBERTa consistently outperforms logistic regression, especially for low-frequency frames.
Figure 9: Average F1 scores on combined dev/test set separated by region. Models achieve comparable performance for the United States, United Kingdom, and European Union, except for slightly lower performance for issue-specific frames on EU tweets.
Frame Type	Frame	Precision	Recall	F1-score	Support	LRAP
Issue-General	Capacity and Resources	0.530	0.706	0.601	17.0	0.745
Crime and Punishment	0.787	0.798	0.791	88.0	0.745
Cultural Identity	0.716	0.815	0.762	91.0	0.745
Economic	0.821	0.968	0.888	87.0	0.745
External Regulation and Reputation	0.658	0.493	0.562	45.0	0.745
Fairness and Equality	0.678	0.712	0.692	68.0	0.745
Health and Safety	0.841	0.866	0.852	73.0	0.745
Legality, Constitutionality, Jurisdiction	0.764	0.822	0.790	37.0	0.745
Morality and Ethics	0.792	0.549	0.643	55.0	0.745
Policy Prescription and Evaluation	0.689	0.807	0.740	108.0	0.745
Political Factors and Implications	0.890	0.948	0.918	154.0	0.745
Public Sentiment	0.619	0.388	0.473	33.0	0.745
Quality of Life	0.502	0.484	0.490	31.0	0.745
Security and Defense	0.825	0.818	0.821	80.0	0.745
Hero: Cultural Diversity	0.699	0.387	0.490	15.0	0.785
Hero: Integration	0.455	0.354	0.391	13.0	0.785
Hero: Worker	0.501	0.369	0.421	13.0	0.785
Threat: Fiscal	0.613	0.504	0.541	25.0	0.785
Threat: Jobs	1.000	0.525	0.683	8.0	0.785
Issue-Specific	Threat: National Cohesion	0.565	0.512	0.526	16.0	0.785
Threat: Public Order	0.773	0.746	0.758	74.0	0.785
Victim: Discrimination	0.615	0.684	0.646	45.0	0.785
Victim: Global Economy	0.933	0.600	0.720	5.0	0.785
Victim: Humanitarian	0.647	0.488	0.551	64.0	0.785
Victim: War	0.533	0.250	0.328	4.0	0.785
Narrative	Episodic	0.706	0.902	0.791	200.0	0.896
Thematic	0.855	0.865	0.859	260.0	0.896
Table 7: Performance per frame on development set
Frame Type	Frame	Precision	Recall	F1-score	Support	LRAP
Issue-General	Capacity and Resources	0.451	0.611	0.517	18.0	0.750
Crime and Punishment	0.817	0.695	0.749	76.0	0.750
Cultural Identity	0.687	0.852	0.760	93.0	0.750
Economic	0.824	0.950	0.882	112.0	0.750
External Regulation and Reputation	0.708	0.581	0.629	32.0	0.750
Fairness and Equality	0.721	0.635	0.673	79.0	0.750
Health and Safety	0.784	0.878	0.828	54.0	0.750
Legality, Constitutionality, Jurisdiction	0.817	0.875	0.844	32.0	0.750
Morality and Ethics	0.698	0.570	0.623	47.0	0.750
Policy Prescription and Evaluation	0.660	0.855	0.743	87.0	0.750
Political Factors and Implications	0.912	0.911	0.911	149.0	0.750
Public Sentiment	0.713	0.338	0.455	26.0	0.750
Quality of Life	0.657	0.520	0.574	30.0	0.750
Security and Defense	0.725	0.816	0.768	51.0	0.750
Hero: Cultural Diversity	0.591	0.567	0.569	12.0	0.806
Hero: Integration	0.503	0.500	0.498	14.0	0.806
Hero: Worker	0.710	0.575	0.634	24.0	0.806
Threat: Fiscal	0.694	0.689	0.683	27.0	0.806
Threat: Jobs	0.743	0.620	0.671	10.0	0.806
Issue-Specific	Threat: National Cohesion	0.344	0.455	0.383	11.0	0.806
Threat: Public Order	0.737	0.681	0.707	52.0	0.806
Victim: Discrimination	0.785	0.570	0.656	60.0	0.806
Victim: Global Economy	0.571	0.450	0.489	8.0	0.806
Victim: Humanitarian	0.715	0.658	0.681	45.0	0.806
Victim: War	0.133	0.080	0.100	5.0	0.806
Narrative	Episodic	0.630	0.922	0.748	181.0	0.894
Thematic	0.885	0.852	0.868	263.0	0.894
Table 8: Performance per frame on test set
D Frame-building (region) regressions
Tables 9-35 show independent variable coefficients for logit regressions predicting frames from region.
Table 9: Capacity.and.Resourceso
countryEU	-0.091*** (0.015)
countryGB	0.403*** (0.007)
has_hastag1	-0.301*** (0.011)
has Mention1	0.057*** (0.009)
has_url1	0.001 (0.011)
is-quote_status1	0.324*** (0.009)
is_reply1	0.359*** (0.009)
is_verified1	-0.239*** (0.020)
log_chars	0.730*** (0.008)
log_followers	-0.019*** (0.003)
log_following	0.006** (0.003)
log statuses	-0.005** (0.002)
Constant	-6.801*** (0.082)
Observations	2,661,435
Log Likelihood	-611,982.800
Akaike Inf. Crit.	1,223,998.000
Bayesian Inf. Crit.	1,224,202.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 10: Crime.and.Punishment
countryEU	-0.635*** (0.008)
countryGB	-1.162*** (0.006)
has_hastag1	-0.123*** (0.005)
has Mention1	-0.032*** (0.004)
has_url1	0.175*** (0.005)
isQuote_status1	0.084*** (0.005)
is_reply1	0.072*** (0.005)
is_verified1	-0.311*** (0.009)
log_chars	0.261*** (0.004)
log_followers	-0.013*** (0.001)
log_following	0.031*** (0.001)
log statuses	0.023*** (0.001)
Constant	-2.550*** (0.038)
Observations	2,661,435
Log Likelihood	-1,598,017.000
Akaike Inf. Crit.	3,196,066.000
Bayesian Inf. Crit.	3,196,271.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 11: Cultural.Identity
countryEU	0.641*** (0.008)
countryGB	0.643*** (0.005)
has_hastag1	-0.102*** (0.006)
has Mention1	-0.357*** (0.006)
has_url1	-0.322*** (0.007)
is quote_status1	0.008 (0.006)
isreply1	0.228*** (0.006)
is_verified1	-0.065*** (0.011)
log chars	0.758*** (0.005)
log_followers	0.029*** (0.002)
log_following	-0.043*** (0.002)
log statuses	-0.022*** (0.001)
Constant	-4.890*** (0.043)
Observations	2,661,435
Log Likelihood	-1,162,059.000
Akaike Inf. Crit.	2,324,150.000
Bayesian Inf. Crit.	2,324,354.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 12: Economic
countryEU	-0.197*** (0.009)
countryGB	0.154*** (0.005)
has_hashtag1	-0.129*** (0.006)
has Mention1	-0.010** (0.005)
has_url1	0.017*** (0.006)
isQuote_status1	0.208*** (0.005)
is_reply1	0.199*** (0.005)
is_verified1	-0.089*** (0.010)
log_chars	0.916*** (0.005)
log_followers	-0.053*** (0.001)
log_following	0.027*** (0.002)
log statuses	0.031*** (0.001)
Constant	-6.173*** (0.052)
Observations	2,661,435
Log Likelihood	-1,409,032.000
Akaike Inf. Crit.	2,818,096.000
Bayesian Inf. Crit.	2,818,301.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 13: External.Regulation.and.Reputation
countryEU	0.934*** (0.012)
countryGB	0.898*** (0.008)
has_hashtag1	0.112*** (0.011)
has Mention1	0.016 (0.010)
has_url1	0.673*** (0.014)
isQuote_status1	-0.044*** (0.011)
is_reply1	-0.058*** (0.010)
is_verified1	0.020 (0.020)
log_chars	0.551*** (0.009)
log_followers	-0.014*** (0.003)
log_following	-0.011*** (0.003)
log statuses	0.047*** (0.002)
Constant	-7.119*** (0.087)
Observations	2,661,435
Log Likelihood	-448,512.800
Akaike Inf. Crit.	897,057.500
Bayesian Inf. Crit.	897,262.200
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 14: Fairness.and.Equality
countryEU	-0.068*** (0.010)
countryGB	0.280*** (0.006)
has_hastag1	0.029*** (0.007)
has Mention1	-0.243*** (0.006)
has_url1	-0.117*** (0.008)
isQuote_status1	0.511*** (0.006)
is_reply1	0.566*** (0.007)
is_verified1	-0.031** (0.013)
log_chars	0.933*** (0.006)
log_followers	-0.034*** (0.002)
log_following	0.017*** (0.002)
log statuses	0.002 (0.001)
Constant	-6.774*** (0.042)
Observations	2,661,435
Log Likelihood	-1,027,084.000
Akaike Inf. Crit.	2,054,199.000
Bayesian Inf. Crit.	2,054,404.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 15: Health.and.Safety
countryEU	-0.070*** (0.009)
countryGB	-0.375*** (0.006)
has_hashtag1	-0.061*** (0.006)
has Mention1	-0.134*** (0.005)
has_url1	0.512*** (0.007)
isQuote_status1	-0.148*** (0.006)
is_reply1	-0.140*** (0.005)
is_verified1	-0.072*** (0.011)
log_chars	0.432*** (0.005)
log_followers	-0.026*** (0.002)
log_following	0.036*** (0.002)
log statuses	0.028*** (0.001)
Constant	-4.279*** (0.078)
Observations	2,661,435
Log Likelihood	-1,256,905.000
Akaike Inf. Crit.	2,513,841.000
Bayesian Inf. Crit.	2,514,046.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 18: Policy.Prescription.and.Evaluation
countryEU	-0.152*** (0.008)
countryGB	0.356*** (0.004)
has_hashtag1	0.220*** (0.005)
has Mention1	0.107*** (0.004)
has_url1	0.213*** (0.005)
is quote_status1	0.059*** (0.005)
is_reply1	-0.083*** (0.005)
is_verified1	0.337*** (0.009)
log_chars	0.670*** (0.004)
log_followers	-0.046*** (0.001)
log_following	0.021*** (0.001)
log statuses	0.042*** (0.001)
Constant	-4.886*** (0.031)
Observations	2,661,435
Log Likelihood	-1,574,272.000
Akaike Inf. Crit.	3,148,576.000
Bayesian Inf. Crit.	3,148,780.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 16: Legality..Constitutionality..Jurisdiction
countryEU	-0.494*** (0.012)
countryGB	-0.688*** (0.008)
has_hashtag1	-0.041*** (0.007)
has Mention1	-0.012** (0.006)
has_url1	0.263*** (0.008)
isQuote_status1	-0.206*** (0.007)
is_reply1	-0.200*** (0.006)
is_verified1	0.363*** (0.012)
log_chars	0.734*** (0.006)
log_followers	-0.050*** (0.002)
log_following	0.024*** (0.002)
log statuses	0.058*** (0.001)
Constant	-6.193*** (0.041)
Observations	2,661,435
Log Likelihood	-968,625.200
Akaike Inf. Crit.	1,937,282.000
Bayesian Inf. Crit.	1,937,487.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 19: Political.Factors.and.Implications
countryEU	-0.531*** (0.007)
countryGB	-0.326*** (0.004)
has_hashtag1	0.192*** (0.005)
has Mention1	0.180*** (0.004)
has_url1	0.214*** (0.005)
isQuote_status1	0.156*** (0.005)
is_reply1	-0.301*** (0.004)
is_verified1	-0.060*** (0.008)
log_chars	0.984*** (0.004)
log_followers	-0.044*** (0.001)
log_following	0.052*** (0.001)
log.statuses	0.075*** (0.001)
Constant	-6.163*** (0.028)
Observations	2,661,435
Log Likelihood	-1,735,632.000
Akaike Inf. Crit.	3,471,297.000
Bayesian Inf. Crit.	3,471,501.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 17: Morality.and.Ethics
countryEU	-0.195*** (0.013)
countryGB	-0.495*** (0.009)
has_hashtag1	0.169*** (0.007)
has Mention1	-0.231*** (0.007)
has_url1	0.064*** (0.009)
isQuote_status1	0.126*** (0.007)
is_reply1	-0.125*** (0.007)
is_verified1	0.164*** (0.014)
log_chars	0.874*** (0.007)
log_followers	-0.044*** (0.002)
log_following	0.033*** (0.002)
log statuses	-0.006*** (0.002)
Constant	-6.794*** (0.077)
Observations	2,661,435
Log Likelihood	-768,383.100
Akaike Inf. Crit.	1,536,798.000
Bayesian Inf. Crit.	1,537,003.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 20: Public.Sentiment
countryEU	0.047*** (0.015)
countryGB	0.286*** (0.008)
has_hashtag1	0.278*** (0.009)
has Mention1	-0.017* (0.009)
has_url1	0.462*** (0.013)
isQuote_status1	-0.135*** (0.010)
is_reply1	-0.279*** (0.009)
is_verified1	0.049*** (0.017)
log_chars	0.538*** (0.008)
log_followers	0.011*** (0.003)
log_following	0.009*** (0.003)
log statuses	0.0004 (0.002)
Constant	-6.210*** (0.085)
Observations	2,661,435
Log Likelihood	-523,820.000
Akaike Inf. Crit.	1,047,672.000
Bayesian Inf. Crit.	1,047,877.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 21: Quality.of.Life
countryEU	0.059*** (0.016)
countryGB	0.168*** (0.009)
has_hashtag1	-0.178*** (0.011)
has Mention1	-0.565*** (0.010)
has_url1	0.139*** (0.014)
isQuote_status1	0.131*** (0.010)
is_reply1	0.349*** (0.010)
is_verified1	0.123*** (0.018)
log_chars	1.050*** (0.009)
log_followers	0.033*** (0.003)
log_following	-0.031*** (0.003)
log statuses	-0.063*** (0.002)
Constant	-7.958*** (0.058)
Observations	2,661,435
Log Likelihood	-491,143.800
Akaike Inf. Crit.	982,319.500
Bayesian Inf. Crit.	982,524.300
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 24: Hero..Integration
countryEU	-0.096*** (0.023)
countryGB	-0.073*** (0.013)
has_hashtag1	-0.103*** (0.015)
has Mention1	-0.399*** (0.014)
has_url1	-0.169*** (0.016)
isQuote_status1	0.102*** (0.014)
is_reply1	0.199*** (0.014)
is_verified1	0.404*** (0.023)
log_chars	0.649*** (0.012)
log_followers	0.031*** (0.004)
log_following	-0.033*** (0.004)
log statuses	-0.088*** (0.003)
Constant	-6.012*** (0.075)
Observations	2,661,435
Log Likelihood	-287,803.800
Akaike Inf. Crit.	575,639.600
Bayesian Inf. Crit.	575,844.400
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 22: Security.and Defense
countryEU	-0.260*** (0.009)
countryGB	-0.469*** (0.006)
has_hashtag1	0.203*** (0.005)
has Mention1	0.012** (0.005)
has_url1	0.260*** (0.006)
isQuote_status1	0.006 (0.005)
is_reply1	-0.104*** (0.005)
is_verified1	-0.147*** (0.010)
log_chars	0.602*** (0.005)
log_followers	-0.016*** (0.002)
log_following	0.014*** (0.002)
log statuses	0.026*** (0.001)
Constant	-4.981*** (0.071)
Observations	2,661,435
Log Likelihood	-1,332,280.000
Akaike Inf. Crit.	2,664,591.000
Bayesian Inf. Crit.	2,664,796.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 25: Hero..Worker
countryEU	0.287*** (0.027)
countryGB	1.287*** (0.011)
has_hashtag1	-0.134*** (0.020)
has Mention1	-0.123*** (0.018)
has_url1	0.119*** (0.020)
isQuote_status1	0.111*** (0.019)
is_reply1	0.501*** (0.018)
is_verified1	0.342*** (0.033)
log_chars	0.462*** (0.015)
log_followers	-0.049*** (0.005)
log_following	0.009* (0.005)
log statuses	-0.016*** (0.004)
Constant	-6.720*** (0.099)
Observations	2,661,435
Log Likelihood	-210,181.700
Akaike Inf. Crit.	420,395.400
Bayesian Inf. Crit.	420,600.100
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 23: Hero..Cultural.Diversity
countryEU	0.041 (0.031)
countryGB	0.051*** (0.018)
has_hashtag1	0.114*** (0.020)
has Mention1	-0.208*** (0.020)
has_url1	-0.480*** (0.021)
isQuote_status1	0.270*** (0.020)
is_reply1	0.200*** (0.020)
is_verified1	0.405*** (0.035)
log_chars	0.449*** (0.017)
log_followers	-0.003 (0.006)
log_following	0.004 (0.006)
log statuses	-0.095*** (0.004)
Constant	-5.560*** (0.104)
Observations	2,661,435
Log Likelihood	-164,157.100
Akaike Inf. Crit.	328,346.100
Bayesian Inf. Crit.	328,550.800
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 26: Threat..Fiscal
countryEU	-0.633*** (0.017)
countryGB	-0.435*** (0.009)
has_hashtag1	-0.244*** (0.010)
has Mention1	0.227*** (0.008)
has_url1	-0.238*** (0.009)
isQuote_status1	0.716*** (0.008)
is_reply1	0.557*** (0.009)
is_verified1	-1.728*** (0.034)
log_chars	0.480*** (0.007)
log_followers	0.019*** (0.002)
log_following	0.023*** (0.003)
log statuses	-0.035*** (0.002)
Constant	-5.272*** (0.085)
Observations	2,661,435
Log Likelihood	-709,024.600
Akaike Inf. Crit.	1,418,081.000
Bayesian Inf. Crit.	1,418,286.000
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$
Table 27: Threat..Jobs
countryEU	-0.339*** (0.043)
countryGB	0.438*** (0.017)
has_hashtag1	-0.233*** (0.028)
has Mention1	-0.158*** (0.024)
has_url1	-0.220*** (0.025)
isQuote_status1	0.488*** (0.024)
is_reply1	0.710*** (0.024)
is_verified1	-0.702*** (0.071)
log_chars	0.560*** (0.019)
log_followers	-0.090*** (0.007)
log_following	-0.013** (0.007)
log statuses	0.046*** (0.005)
Constant	-7.588*** (0.101)
Observations	2,661,435
Log Likelihood	-143,493.400
Akaike Inf. Crit.	287,018.800
Bayesian Inf. Crit.	287,223.500
Note:  ${}^{ * }\mathrm{p} < {0.1};{}^{* * }\mathrm{p} < {0.05};{}^{* *  * }\mathrm{p} < {0.01}$