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Morality and partisan social media engagement: a natural language examination of moral political messaging and engagement during the 2018 US midterm elections

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Abstract

Despite numerous studies examining the impact of moral vs. nonmoral political content on social media engagement, the specifics of how distinct moral messaging captivates public attention remain unexplored. Scrutinizing over 10,000 original tweets from 2018 US Senate election candidates, the present work addresses this gap through natural language processing combined with machine and deep learning to probe (a) Are certain types of moral messaging among US politicians more effective in garnering attention?; and (b) If so, does this pattern differ among Democrats and Republicans online? While an unequal distribution emerged, wherein morally charged content elicited more responses than nonmoral rhetoric on both sides of the political aisle, the results indicate varying sensitivities between the factions to certain moral messaging, with Democrats being driven not only by care/harm and fairness/cheating but also by those signaling betrayal, subversion, or even degradation, whereas Republicans were captivated by a broader spectrum of concerns, including care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity. Given that social media is becoming increasingly pivotal in politics today, such findings advance our understanding by untangling how advanced methodologies can dissect the intricacies of moralities in the digital realm, shedding light on the potential dynamics and strategies through which Democratic vs. Republican candidates adopt to expand their reach via social media politicking.

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Data availability

In accordance with Twitter’s data-sharing policy, we are unable to distribute the original dataset publicly. The associated codes for data collection and analysis are accessible via Supplementary Data Syntax.

Notes

  1. Throughout this study, we refer to the platform formerly known as Twitter, using terms such as “tweets” to maintain consistency with the data and literature relevant to the study’s timeframe. Since July 2023, significant updates have been implemented by the platform’s owning company, including the rebranding of “Twitter” to “X” and the change from “tweets” to “posts”, among other modifications. While we acknowledge these developments, the use of the original terminology here is posited to reflect the historical context of the data from the 2018 US midterm elections.

  2. To ensure the research adheres to transparency and reproducibility standards, this study was pre-registered, with the anonymized details available at https://aspredicted.org/WND_YT2.

  3. It is noteworthy that the delineation of moral foundations between liberals and conservatives, as proposed by MFT, does not imply an absolute or binary distinction. As clarified by Graham et al. [34], while conservatives might assign greater importance to binding foundations, liberals do not categorically neglect them. Even those who strongly identify as liberals recognize the importance of binding foundations (i.e., in-group loyalty, deference to authority, and purity), although they might not attribute the same level of prominence as their conservative counterparts. This nuanced perspective highlights a gradient, rather than a stark dichotomy, in moral prioritization across the political spectrum, a consideration that is worth exploring in more research.

  4. Referring to hate speech and offensive rhetoric extracted by Davidson et al. [56].

  5. Due to computational limitations and the challenges faced when running Transformer-based models (e.g., BERT) on available hardware, they were not utilized. However, lighter versions of these models (e.g., DistilBERT) were tested to compare model performance, which did not yield improvements over the selected model (i.e., BiLSTM) for the present results (see Supplementary Data Syntax for details).

  6. “RT” refers to direct retweets without additional comments, and “FA” denotes favorites.

  7. Here we utilized R’s lme4 and car packages to assess the mean variances in the moral foundations while accounting for potential variability introduced by the grouping variables, such as the different levels of candidates and their tweet posting dates.

  8. \(Mea{n_{{\rm{diff}}}} = Mea{n_{{\rm{positive}}}} - Mea{n_{{\rm{negative}}}}\).

References

  1. Hill, J. (2018, December 8). Sometimes I wish the Obamas wouldn’t ‘go high’ The Atlantic. Retrived March 7, 2023, from https://www.theatlantic.com/ideas/archive/2018/12/problem-when-they-go-low-we-go-high/577543/

  2. Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Pnas, 114(28), 7313–7318. https://doi.org/10.1073/pnas.1618923114

    Article  Google Scholar 

  3. Brady, W. J., Wills, J. A., Burkart, D., Jost, J. T., & Van Bavel, J. J. (2019). An ideological asymmetry in the diffusion of moralized content on social media among political leaders. Journal of Experimental Psychology, 148(10), 1802–1813. https://doi.org/10.1037/xge0000532

    Article  Google Scholar 

  4. Auter, Z. J., & Fine, J. A. (2016). Negative campaigning in the social media age: Attack advertising on Facebook. Political Behavior, 38(4), 999–1020. https://doi.org/10.1007/s11109-016-9346-8

    Article  Google Scholar 

  5. Brady, W. J., & Crockett, M. J. (2019). How effective is online outrage? Trends in Cognitive Sciences, 23(2), 79–80. https://doi.org/10.1016/j.tics.2018.11.004

    Article  Google Scholar 

  6. Nai, A., & Maier, J. (2020). Dark necessities? Candidates’ aversive personality traits and negative campaigning in the 2018 American midterms. Electoral Studies 68 https://doi.org/10.1016/j.electstud.2020.102233

  7. Yuan, L., Wang, T., Ferraro, G., Suominen, H., & Rizoiu, M. A. (2023). Transfer learning for hate speech detection in social media. Journal of Computational Social Science, 6(2), 1081–1101.

    Article  Google Scholar 

  8. Lakoff, G. (2002). Moral politics: How liberals and conservatives think. University of Chicago Press.

  9. Druckman, J. N., Kifer, M. J., & Parkin, M. (2010). Timeless strategy meets new medium: Going negative on congressional campaign web sites, 2002–2006. Political Communication, 27(1), 88–103. https://doi.org/10.1080/10584600903502607

    Article  Google Scholar 

  10. Kannangara, S., & Wobcke, W. (2022). Determining political interests of issue-motivated groups on social media: Joint Topic models for issues, sentiment and stance. Journal of Computational Social Science, 5(1), 811–840.

    Article  Google Scholar 

  11. Gervais, B. T., Evans, H. K., & Russell, A. (2020). Tweeting for hearts and minds? Measuring candidates’ use of anxiety in tweets during the 2018 midterm elections. Political Science & Politics, 53(4), 652–656.

    Article  Google Scholar 

  12. Walter, A. (2018, September 28). Follow 2018 with us. The Cook Political Report. Retrived March 5, 2023, form https://www.cookpolitical.com/analysis/national/national-politics/follow-2018-us

  13. Wang, M. J., Yogeeswaran, K., Sivaram, S., & Nash, K. (2021). Examining spread of emotional political content among democratic and republican candidates during the 2018 US midterm elections. Humanities & Social Sciences Communications, 8(1), 1–12. https://doi.org/10.1057/s41599-021-00987-4

    Article  Google Scholar 

  14. Kuypers, J. A. (2020). President Trump and the news media: Moral foundations, framing, and the nature of press bias in America. Lexington Books.

  15. Wojcieszak, M., & Mutz, D. C. (2009). Online groups and political discourse: Do online discussion spaces facilitate exposure to political disagreement? Journal of Communication, 59(1), 40–56.

    Article  Google Scholar 

  16. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2), 139–157. https://doi.org/10.1023/A:1007607513941

    Article  Google Scholar 

  17. Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14(1), 91–118. https://doi.org/10.1146/annurev-clinpsy-032816-045037

    Article  Google Scholar 

  18. Sagi, E., & Dehghani, M. (2014). Measuring moral rhetoric in text. Social Science Computer Review, 32(2), 132–144. https://doi.org/10.1177/0894439313506837

    Article  Google Scholar 

  19. Hasan, A., Moin, S., Karim, A., & Shamshirband, S. (2018). Machine learning-based sentiment analysis for Twitter accounts. Mathematical and Computational Applications, 23(1), 11. https://doi.org/10.3390/mca23010011

    Article  Google Scholar 

  20. Hasan, M., Rundensteiner, E., & Agu, E. (2019). Automatic emotion detection in text streams by analyzing Twitter data. International Journal of Data Science and Analytics, 7(1), 35–51. https://doi.org/10.1007/s41060-018-0096-z

    Article  Google Scholar 

  21. Hoover, J., Portillo-Wightman, G., Yeh, L., Havaldar, S., Davani, A. M., Lin, Y., Kennedy, B., Atari, M., Kamel, Z., Mendlen, M., Moreno, G., Park, C., Chang, T. E., Chin, J., Leong, C., Leung, J. Y., Mirinjian, A., & Dehghani, M. (2020). Moral foundations Twitter corpus: A collection of 35k tweets annotated for moral sentiment. Social Psychological & Personality Science, 11(8), 1057–1071. https://doi.org/10.1177/1948550619876629

    Article  Google Scholar 

  22. Skitka, L. J., & Bauman, C. W. (2008). Moral conviction and political engagement: Moral conviction. Political Psychology, 29(1), 29–54. https://doi.org/10.1111/j.1467-9221.2007.00611.x

    Article  Google Scholar 

  23. Fiske, A. P. (1992). The four elementary forms of sociality: Framework for a unified theory of social relations. Psychological Review, 99(4), 689–723. https://doi.org/10.1037/0033-295X.99.4.689

    Article  Google Scholar 

  24. Schwartz, S. H. (1992). Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. Advances in Experimental Social Psychology, 25(C), 1–65. https://doi.org/10.1016/S0065-2601(08)60281-6

    Article  Google Scholar 

  25. Haidt, J. (2003). The moral emotions. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.), Handbook of affective sciences (pp. 852–870). Oxford University Press.

  26. Haidt, J., & Graham, J. (2007). When morality opposes justice: Conservatives have moral intuitions that liberals may not recognize. Social Justice Research, 20(1), 98–116. https://doi.org/10.1007/s11211-007-0034-z

    Article  Google Scholar 

  27. Haidt, J., & Joseph, C. (2004). Intuitive ethics: How innately prepared intuitions generate culturally variable virtues. Daedalus, 133, 55–66. https://doi.org/10.1162/0011526042365555

    Article  Google Scholar 

  28. Haidt, J., & Joseph, C. (2011). How moral foundations theory succeeded in building on sand: A response to Suhler and Churchland. Journal of Cognitive Neuroscience, 23(9), 2117–2122.

    Article  Google Scholar 

  29. Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. H. (2011). Mapping the moral domain. Journal of Personality and Social Psychology, 101, 366–385.

    Article  Google Scholar 

  30. Graham, J., Haidt, J., Motyl, M., Meindl, P., Iskiwitch, C., & Mooijman, M. (2018). Moral foundations theory: On the advantages of moral pluralism over moral monism. Atlas of Moral Psychology, 211, 222.

    Google Scholar 

  31. Gilligan, C. (1977). In a different voice: Women’s conceptions of self and of morality. Harvard Educational Review, 47(4), 481–517.

    Article  Google Scholar 

  32. Kohlberg, L. (1969). Stage and sequence: The cognitive-developmental approach to socialization. In D. A. Goslin (Ed.), Handbook of socialization theory and research (pp. 347–480). Rand McNally.

  33. Haidt, J., & Graham, J. (2009). Planet of the Durkheimians, where community, authority, and sacredness are foundations of morality. In J. T. Jost, A. C. Kay, & H. Thorisdottir (Eds.), Social and psychological bases of ideology and system justification (pp. 371–401). Oxford.

  34. Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96(5), 1029–1046. https://doi.org/10.1037/a0015141

    Article  Google Scholar 

  35. Shogan, C. J. (2007). The moral rhetoric of American presidents. Texas A&M University.

  36. Wright, J. C., & Baril, G. (2011). The role of cognitive resources in determining our moral intuitions: Are we all liberals at heart? Journal of Experimental Social Psychology, 47(5), 1007–1012. https://doi.org/10.1016/j.jesp.2011.03.014

    Article  Google Scholar 

  37. Wang, S. N., & Inbar, Y. (2021). Moral-language use by U.S. political elites. Psychological Science, 32(1), 14–26. https://doi.org/10.1177/0956797620960397

    Article  Google Scholar 

  38. Reimer, N. K., Atari, M., Karimi-Malekabadi, F., Trager, J., Kennedy, B., Graham, J., & Dehghani, M. (2022). Moral values predict county-level COVID-19 vaccination rates in the United States. The American Psychologist, 77(6), 743–759. https://doi.org/10.1037/amp0001020

    Article  Google Scholar 

  39. Feygina, I., Jost, J. T., & Goldsmith, R. E. (2010). System justification, the denial of global warming, and the possibility of system-sanctioned change. Personality and Social Psychology Bulletin, 36(3), 326–338. https://doi.org/10.1177/0146167209351435

    Article  Google Scholar 

  40. Kidwell, B., Farmer, A., & Hardesty, D. M. (2013). Getting liberals and conservatives to go green: Political ideology and congruent appeals. Journal of Consumer Research, 40(2), 350–367. https://doi.org/10.1086/670610

    Article  Google Scholar 

  41. Feinberg, M., & Willer, R. (2013). The moral roots of environmental attitudes. Psychological Science, 24(1), 56–62.

    Article  Google Scholar 

  42. Feinberg, M., & Willer, R. (2015). From gulf to bridge: When do moral arguments facilitate political influence? Personality & Social Psychology Bulletin, 41(12), 1665–1681. https://doi.org/10.1177/0146167215607842

    Article  Google Scholar 

  43. Feinberg, M., & Willer, R. (2019). Moral reframing: A technique for effective and persuasive communication across political divides. Social and Personality Psychology Compass, 13(12). https://doi.org/10.1111/spc3.12501

  44. Sahly, A., Shao, C., & Kwon, K. H. (2019). Social media for political campaigns: An examination of Trump’s and Clinton’s frame building and its effect on audience engagement. Social Media + Society, 5(2), 205630511985514. https://doi.org/10.1177/2056305119855141

    Article  Google Scholar 

  45. Makice, K. (2009). Twitter API: Up and running: Learn how to build applications with the Twitter API. O’Reilly Media, Inc.

  46. Hopp, F. R., Fisher, J. T., Cornell, D., Huskey, R., & Weber, R. (2021). The extended moral foundations dictionary (eMFD): Development and applications of a crowd-sourced approach to extracting moral intuitions from text. Behavior Research Methods, 53(1), 232–246. https://doi.org/10.3758/s13428-020-01433-0

    Article  Google Scholar 

  47. Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A. (2011). Bad news travel fast: A content-based analysis of interestingness on Twitter. Proceedings of the 3rd International Web Science Conference, 15–17.

  48. Cunha, E., Magno, G., Gonçalves, M. A., Cambraia, C., & Almeida, V. (2014). He votes or she votes? Female and male discursive strategies in Twitter political hashtags. PloS One, 9(1), e87041–e87041. https://doi.org/10.1371/journal.pone.0087041

    Article  Google Scholar 

  49. Javed, N., & Muralidhara, B. L. (2015). Automating corpora generation with semantic cleaning and tagging of tweets for multi-dimensional social media analytics. International Journal of Computer Applications, 127(12), 11–16.

    Article  Google Scholar 

  50. Brady, W. J., Crockett, M. J., & Van Bavel, J. J. (2020). The MAD model of moral contagion: The role of motivation, attention, and design in the spread of moralized content online. Perspectives on Psychological Science, 15(4), 978–1010. https://doi.org/10.1177/1745691620917336

    Article  Google Scholar 

  51. Burton, J. W., Cruz, N., & Hahn, U. (2021). Reconsidering evidence of moral contagion in online social networks. Nature Human Behaviour, 5(12), 1629–1635. https://doi.org/10.1038/s41562-021-01133-5

    Article  Google Scholar 

  52. Hadarics, M., Szabó, Z. P., & Kende, A. (2020). The relationship between collective narcissism and group-based moral exclusion: The mediating role of intergroup threat and social distance. Journal of Social and Political Psychology, 8(2), 788–804.

    Article  Google Scholar 

  53. Lewis, P. G. (2019). Moral foundations in the 2015-16 U.S. presidential primary debates: The positive and negative moral vocabulary of partisan elites. Social Sciences (Basel), 8(8), 233. https://doi.org/10.3390/socsci8080233

    Article  Google Scholar 

  54. Iliev, R., Dehghani, M., & Sagi, E. (2015). Automated text analysis in psychology: Methods, applications, and future developments. Language and Cognition, 7(2), 265–290. https://doi.org/10.1017/langcog.2014.30

    Article  Google Scholar 

  55. Mooijman, M., Hoover, J., Lin, Y., Ji, H., & Dehghani, M. (2018). Moralization in social networks and the emergence of violence during protests. Nature Human Behaviour, 2(6), 389–396. https://doi.org/10.1038/s41562-018-0353-0

    Article  Google Scholar 

  56. Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. Proceedings of the 11th International Conference on Web and Social Media, 512, 515.

    Google Scholar 

  57. Gisev, N., Bell, J. S., & Chen, T. F. (2013). Interrater agreement and interrater reliability: Key concepts, approaches, and applications. Research in Social and Administrative Pharmacy, 9(3), 330–338. https://doi.org/10.1016/j.sapharm.2012.04.004

    Article  Google Scholar 

  58. Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 8(1), 216–225.

  59. Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.

    Article  Google Scholar 

  60. Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248.

    Article  Google Scholar 

  61. Chen, T., Xu, R., He, Y., & Wang, X. (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications, 72, 221–230. https://doi.org/10.1016/j.eswa.2016.10.065

    Article  Google Scholar 

  62. Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The performance of LSTM and BiLSTM in forecasting time series. Proceedings of the IEEE International Conference on Big Data, 3285–3292. https://doi.org/10.1109/BigData47090.2019.9005997

  63. Metaxas, P., Mustafaraj, E., Wong, K., Zeng, L., O’Keefe, M., & Finn, S. (2015). What do retweets indicate? Results from user survey and meta-review of research. Proceedings of the International AAAI Conference on Web and Social Media, 9(1), 658–661.

    Article  Google Scholar 

  64. Yan, Y., Toriumi, F., & Sugawara, T. (2021). Understanding how retweets influence the behaviors of social networking service users via agent-based simulation. Computational Social Networks, 8(1), 1–21.

    Article  Google Scholar 

  65. Boynton, G. R. (2013). The political domain goes to Twitter: Hashtags, retweets and URLs. Open Journal of Political Science, 4(01), 8.

    Article  Google Scholar 

  66. Meier, F., Elsweiler, D. C., & Wilson, M. L. (2014). More than liking and bookmarking? Towards understanding Twitter favouriting behaviour. Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 8(1), 346–355.

    Article  Google Scholar 

  67. Jang, Y., Park, C. H., & Seo, Y. S. (2019). Fake news analysis modeling using quote retweet. Electronics, 8(12), 1377.

    Article  Google Scholar 

  68. Zade, H., Williams, S., Tran, T. T., Smith, C., Venkatagiri, S., Hsieh, G., & Starbird, K. (2024). To reply or to quote: Comparing conversational framing strategies on Twitter. ACM Journal on Computing and Sustainable Societies, 2(1), 1–27.

    Article  Google Scholar 

  69. Van Bavel, J. J., Packer, D. J., Haas, I. J., & Cunningham, W. A. (2012). The importance of moral construal: Moral vs. non-moral construal elicits faster, more extreme, universal evaluations of the same actions. PloS One, 7(11), e48693–e48693. https://doi.org/10.1371/journal.pone.0048693

    Article  Google Scholar 

  70. Van Bavel, J. J., Harris, E. A., Pärnamets, P., Rathje, S., Doell, K. C., & Tucker, J. A. (2021). Political psychology in the digital (mis)information age: A model of news belief and sharing. Social Issues and Policy Review, 15(1), 84–113. https://doi.org/10.1111/sipr.12077

    Article  Google Scholar 

  71. Bates, D. M. (2010). Lme4: Mixed-effects modeling with R. Springer.

  72. Brown, V. A. (2021). An introduction to linear mixed-effects modeling in R. Advances in Methods and Practices in Psychological Science, 4(1).

  73. De Boeck, P., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuerlinckx, F., & Partchev, I. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software, 39, 1–28.

    Article  Google Scholar 

  74. Lüdecke, D. (2015). Package ‘sjPlot’. R Package Version, 1(9).

  75. Wang, M. J., Yogeeswaran, K., Sivaram, S., & Nash, K. (2023). Tweets on the political left and right: A natural language examination of the spread of moral and emotional messaging during the 2018 US midterm elections. University of Canterbury.

  76. Broockman, D. E., & Skovron, C. (2018). Bias in perceptions of public opinion among political elites. American Political Science Review, 112(3), 542–563.

    Article  Google Scholar 

  77. Kivikangas, J. M., Fernández-Castilla, B., Järvelä, S., Ravaja, N., & Lönnqvist, J. (2021). Moral foundations and political orientation: Systematic review and meta-analysis. Psychological Bulletin, 147(1), 55–94. https://doi.org/10.1037/bul0000308

    Article  Google Scholar 

  78. Hackenburg, K., Brady, W. J., & Tsakiris, M. (2023). Mapping moral language on US presidential primary campaigns reveals rhetorical networks of political division and unity. PNAS Nexus, 2(6). https://doi.org/10.1093/pnasnexus/pgad189

  79. Brady, W. J., & McLoughlin, K. L. (2022). How social media contexts affect the expression of moral emotions. In S. C. Matz (Ed.), The psychology of technology (pp. 239–265). American Psychological Association.

  80. Jaffe, S. (2018). Trump’s second Supreme Court nominee, Brett Kavanaugh. The Lancet, 392(10144), 267–268.

    Article  Google Scholar 

  81. Traynor, J. (2019). Supreme Court justice Brett Kavanaugh and accusations of sexual assault in the media. Political Analysis, 20(1), 4.

    Article  Google Scholar 

  82. Bekafigo, M. A., Stepanova, E. V., Eiler, B. A., Noguchi, K., & Ramsey, K. L. (2019). The effect of group polarization on opposition to Donald Trump. Political Psychology, 40(5), 1163–1178. https://doi.org/10.1111/pops.12584

    Article  Google Scholar 

  83. Padró-Solanet, A., & Balcells, J. (2022). Media diet and polarisation: Evidence from Spain. South European Society & Politics, 27(1), 75–95. https://doi.org/10.1080/13608746.2022.2046400

    Article  Google Scholar 

  84. Iyengar, S., & Simon, A. F. (2000). New perspectives and evidence on political communication and campaign effects. Annual Review of Psychology, 51(1), 149–169. https://doi.org/10.1146/annurev.psych.51.1.149

    Article  Google Scholar 

  85. Jungherr, A., Schroeder, R., & Stier, S. (2019). Digital media and the surge of political outsiders: Explaining the success of political challengers in the United States, Germany, and China. Social Media + Society, 5(3), 205630511987543. https://doi.org/10.1177/2056305119875439

    Article  Google Scholar 

  86. Kirkpatrick, E. M. (1971). Toward a more responsible two-party system: Political science, policy science, or pseudo-science? American Political Science Review, 65(4), 965–990.

    Article  Google Scholar 

  87. Epstein, L. D. (2020). Political parties in western democracies. Routledge.

  88. Abramowitz, A. I., & Saunders, K. L. (2008). Is polarization a myth? The Journal of Politics, 70(2), 542–555.

    Article  Google Scholar 

  89. Bankert, A. (2022). Negative partisanship among independents in the 2020 U.S. presidential elections. Electoral Studies, 78, 102490. https://doi.org/10.1016/j.electstud.2022.102490

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, 8140, New Zealand

    Meng-Jie Wang & Kumar Yogeeswaran

  2. Department of Psychology, University of Alberta, Alberta, Canada

    Kyle Nash

  3. Independent Researcher, Christchurch, New Zealand

    Sivanand Sivaram

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Wang MJ, Yogeeswaran K, Sivaram S and Nash K conceived of the presented idea and designed the research; Wang MJ collected and visualized the data; Wang MJ and Sivaram S analyzed the data; Wang MJ, Yogeeswaran K and Nash K interpreted the results and wrote the original paper; Sivaram S reviewed and edited the paper; Yogeeswaran K and Nash K provided supervision; all authors approved the manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors read and approved the final manuscript.

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Wang, MJ., Yogeeswaran, K., Nash, K. et al. Morality and partisan social media engagement: a natural language examination of moral political messaging and engagement during the 2018 US midterm elections. J Comput Soc Sc 7, 1699–1726 (2024). https://doi.org/10.1007/s42001-024-00288-1

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  • DOI: https://doi.org/10.1007/s42001-024-00288-1

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