Social identity correlates of social media engagement before and after the 2022 Russian invasion of Ukraine.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
01 Oct 2024
01 Oct 2024
Historique:
received:
13
01
2023
accepted:
29
08
2024
medline:
2
10
2024
pubmed:
2
10
2024
entrez:
1
10
2024
Statut:
epublish
Résumé
Despite the global presence of social media platforms, the reasons why people like and share content are still poorly understood. We investigate how group identity mentions and expressions of ingroup solidarity and outgroup hostility in posts correlate with engagement on Ukrainian social media (i.e., shares, likes, and other reactions) before and after the 2022 Russian invasion of Ukraine. We use a dataset of 1.6 million posts from Ukrainian news source pages on Facebook and Twitter (currently X) and a geolocated sample of 149 thousand Ukrainian tweets. Before the 2022 Russian invasion, we observe that outgroup mentions in posts from news source pages are generally more strongly associated with engagement than negative, positive, and moral-emotional language. After the invasion, social identity mentions become less strongly associated with engagement. Moreover, post-invasion ingroup solidarity posts are strongly related to engagement, whereas posts expressing outgroup hostility show smaller associations. This is the case for both news and non-news social media data. Our correlational results suggest that signaling solidarity with one's ingroup online is associated with more engagement than negativity about outgroups during intense periods of intergroup conflicts, at least in the context of the Russian-Ukrainian war.
Identifiants
pubmed: 39353902
doi: 10.1038/s41467-024-52179-8
pii: 10.1038/s41467-024-52179-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8127Subventions
Organisme : Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation)
ID : OPP1144
Informations de copyright
© 2024. The Author(s).
Références
Statista. Number of social media users worldwide from 2017 to 2027 (in billions). https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/ Accessed: 2022-11-28 (2022).
United Nations Department of Economic and Social Affairs, Population Division. World population prospects 2022: Summary of results. https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/files/wpp2022_summary_of_results.pdf Accessed: 2022-11-28 (2022).
Jost, J. T. et al. How social media facilitates political protest: Information, motivation, and social networks. Political Psychol. 39, 85–118 (2018).
doi: 10.1111/pops.12478
Allen, J., Watts, D. J. & Rand, D. G. Quantifying the impact of misinformation and vaccine-skeptical content on facebook. Science 384, eadk3451 (2024).
pubmed: 38815040
doi: 10.1126/science.adk3451
Schöne, J. P., Parkinson, B. & Goldenberg, A. Negativity spreads more than positivity on twitter after both positive and negative political situations. Affect. Sci. 2, 379–390 (2021).
pubmed: 36043036
pmcid: 9383030
doi: 10.1007/s42761-021-00057-7
Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A. & Van Bavel, J. J. Emotion shapes the diffusion of moralized content in social networks. Proc. Natl Acad. Sci. USA 114, 7313–7318 (2017).
pubmed: 28652356
pmcid: 5514704
doi: 10.1073/pnas.1618923114
Rathje, S., Van Bavel, J. J. & van der Linden, S. Out-group animosity drives engagement on social media. Proc. Natl Acad. Sci. USA 118, e2024292118 (2021).
pubmed: 34162706
pmcid: 8256037
doi: 10.1073/pnas.2024292118
Narayanan, A. Understanding Social Media Recommendation Algorithms. https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms , https://doi.org/10.7916/khdk-m460 Accessed: 2024-07-10 (2023).
Metzler, H. & Garcia, D. Social drivers and algorithmic mechanisms on digital media. Perspectives on Psychological Science 17456916231185057, https://doi.org/10.1177/17456916231185057 (2022).
Tajfel, H., Turner, J. C., Austin, W. G. & Worchel, S. An integrative theory of intergroup conflict. Organ. Identity: A Read. 18, 56–65 (1979).
Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N. & Westwood, S. J. The origins and consequences of affective polarization in the United States. Annu. Rev. Political Sci. 22, 129–146 (2019).
doi: 10.1146/annurev-polisci-051117-073034
Hornsey, M. J. Social identity theory and self-categorization theory: a historical review: Social identity theory and self-categorization theory. Soc. Personal. Psychol. Compass 2, 204–222 (2008).
doi: 10.1111/j.1751-9004.2007.00066.x
Brady, W. J., Crockett, M. J. & Van Bavel, J. J. The MAD model of moral contagion: the role of motivation, attention, and design in the spread of moralized content online. Perspect. Psychol. Sci. 15, 978–1010 (2020).
pubmed: 32511060
doi: 10.1177/1745691620917336
Van Bavel, J. J., Rathje, S., Harris, E., Robertson, C. & Sternisko, A. How social media shapes polarization. Trends Cogn. Sci. 25, 913–916 (2021).
pubmed: 34429255
doi: 10.1016/j.tics.2021.07.013
Brady, W. J. & Van Bavel, J. J. Social Identity Shapes Antecedents And Functional Outcomes Of Moral Emotion Expression In Online Networks. https://doi.org/10.31219/osf.io/dgt6u (2021).
Lorenz-Spreen, P., Oswald, L., Lewandowsky, S. & Hertwig, R. A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nat. Hum. Behav. https://doi.org/10.1038/s41562-022-01460-1 (2022).
Fletcher, R., Cornia, A. & Nielsen, R. K. How polarized are online and offline news audiences? A comparative analysis of twelve countries. Int. J. Press/Politics 25, 169–195 (2020).
doi: 10.1177/1940161219892768
Kobayashi, T. Depolarization through social media use: evidence from dual identifiers in Hong Kong. N. Media Soc. 22, 1339–1358 (2020).
doi: 10.1177/1461444820910124
Asimovic, N., Nagler, J., Bonneau, R. & Tucker, J. A. Testing the effects of Facebook usage in an ethnically polarized setting. Proc. Natl Acad. Sci. USA 118, e2022819118 (2021).
pubmed: 34131075
pmcid: 8237683
doi: 10.1073/pnas.2022819118
Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010).
pubmed: 20550733
doi: 10.1017/S0140525X0999152X
Kubin, E. & von Sikorski, C. The role of (social) media in political polarization: a systematic review. Ann. Int. Commun. Assoc. 45, 188–206 (2021).
Nicholson, S. P. Polarizing cues: POLARIZING CUES. Am. J. Political Sci. 56, 52–66 (2012).
doi: 10.1111/j.1540-5907.2011.00541.x
Abramowitz, A. I. & Webster, S. The rise of negative partisanship and the nationalization of u.s. elections in the 21st century. Elect. Stud. 41, 12–22 (2016).
doi: 10.1016/j.electstud.2015.11.001
Landry, A., Orr, R. I. & Mere, K. Dehumanization and mass violence: a study of mental state language in nazi propaganda (1927–1945). PLoS ONE 17, e0274957 (2022).
pubmed: 36350823
pmcid: 9645591
doi: 10.1371/journal.pone.0274957
Homola, J., Pereira, M. M. & Tavits, M. Legacies of the third reich: concentration camps and out-group intolerance. Plos ONE 114, 573–590 (2020).
Brewer, M. B. The psychology of prejudice: Ingroup love and outgroup hate? J. Soc. Issues 55, 429–444 (1999).
doi: 10.1111/0022-4537.00126
Weisel, O. & Böhm, R. “ingroup love” and “outgroup hate” in intergroup conflict between natural groups. J. Exp. Soc. Psychol. 60, 110–120 (2015).
pubmed: 26339099
pmcid: 4518042
doi: 10.1016/j.jesp.2015.04.008
Greenwald, A. G. & Pettigrew, T. F. With malice toward none and charity for some: Ingroup favoritism enables discrimination. Am. Psychol. 69, 669–684 (2014).
pubmed: 24661244
doi: 10.1037/a0036056
van der Dennen, J. M. G. Ethnocentrism and In-group/Out-group Differentiation: A Review and Interpretation of the Literature. In The Sociobiology of Ethnocentrism: Evolutionary Dimensions of Xenophobia, Discrimination, Racism, and Nationalism. (eds Reynolds, V., Fagler, V. & Vine, I.) pp. 1–47 (The University of Georgia Press, Athens, 1986).
Inglehart, R., Moaddel, M. & Tessler, M. Xenophobia and in-group solidarity in Iraq: a natural experiment on the impact of insecurity. Perspect. Politics https://doi.org/10.1017/S1537592706060324 (2006).
Van Hauwaert, S. M. & Huber, R. A. In group solidarity or out group hostility in response to terrorism in France? Evidence from a regression discontinuity design. Eur. J. Political Res. 59, 936–953 (2020).
doi: 10.1111/1475-6765.12380
Garcia, D. & Rimé, B. Collective emotions and social resilience in the digital traces after a terrorist attack. Psychol. Sci. 30, 617–628 (2019).
pubmed: 30865565
doi: 10.1177/0956797619831964
Feinstein, Y.The Rally Phenomenon in Light of Competing Approaches to Public Opinion, p. 23–37 (Oxford University Press, 2022).
Sherif, M. Intergroup Conflict And Cooperation: The Robbers Cave Experiment, Vol. 10 (University Book Exchange, 1961).
Mackie, D. M. & Smith, E. R. Intergroup Emotions Theory: Production, Regulation, and Modification of Group-Based Emotions, vol. 58, p. 1–69 (Elsevier, 2018).
Mackie, D. M., Devos, T. & Smith, E. R. Intergroup emotions: explaining offensive action tendencies in an intergroup context. J. Personal. Soc. Psychol. 79, 602–616 (2000).
doi: 10.1037/0022-3514.79.4.602
Roozenbeek, J. Propaganda and Ideology in the Russian-Ukrainian War (Cambridge University Press, 2024).
Barrington, L. A new look at region, language, ethnicity and civic national identity in Ukraine. Eur.-Asia Stud. 74, 360–381 (2022).
doi: 10.1080/09668136.2022.2032606
Kulyk, V. National Identity in Ukraine: impact of Euromaidan and the war. Eur.-Asia Stud. 68, 588–608 (2016).
doi: 10.1080/09668136.2016.1174980
Kulyk, V. Shedding Russianness, recasting Ukrainianness: the post-Euromaidan dynamics of ethnonational identifications in Ukraine. Post-Sov. Aff. 34, 119–138 (2018).
doi: 10.1080/1060586X.2018.1451232
Onuch, O., Hale, H. E. & Sasse, G. Studying identity in Ukraine. Post-Sov. Aff. 34, 79–83 (2018).
doi: 10.1080/1060586X.2018.1451241
Roozenbeek, J. Identity discourse in local newspapers before, during, and after military conflict: a case study of Kramatorsk. Demokratizatsiya: J. Post-Sov. Democratization 28, 419–459 (2020).
Roozenbeek, J. J. Media and Identity in Wartime Donbas, 2014–2017 (2020).
Veira-Ramos, A. & Liubyva, T. Ukrainian Identities In Transformation. In Ukraine In Transformation, 203–228 (Springer, 2020).
Kyiv International Institute of Sociology. Attitude in Ukraine to Russia and in Russia to Ukraine. https://kiis.com.ua/?lang=eng&cat=reports&id=1078&page=4 Accessed: 2022-11-28 (2021).
Kyiv International Institute of Sociology & Hrushetsky, A. Dynamics Of The Population’s Attitude To Russia And The Emotional Background Due To The War: The Results Of A Telephone Survey Conducted On May 13–18, 2022. https://www.kiis.com.ua/?lang=eng&cat=reports&id=1112&page=1 Accessed: 2022-11-28 (2022).
Levada-Center. Attitude Towards Countries And Their Citizens. https://www.levada.ru/en/2022/09/16/attitude-towards-countries-and-their-citizens/ Accessed: 2022-11-28 (2022).
Statista. Share Of Users Of Selected Social Media Platforms In Ukraine In July 2021. https://www.statista.com/statistics/1278407/most-popular-social-media-ukraine/ Accessed: 2023-08-01 (2022).
Zasiekin, S. et al. Psycholinguistic aspects of translating liwc dictionary. East Eur. J. Psycholinguist. 5, 111–118 (2018).
Kailer, A. & Chung, C. K. The Russian LIWC2007 dictionary. Pennebaker Conglomerates (2011).
RBC.ru. Daily Users Of Social Media Before And After Bans Of Facebook, Instagram, And Twitter In Russia On February 24 And March 15, 2022, By Selected Platform (In Millions). https://www.statista.com/statistics/1297985/social-media-users-before-and-after-bans-russia/ Accessed: 2022-12-17 (2022).
Baldassarri, D. & Page, S. E. The emergence and perils of polarization. Proc. Natl Acad. Sci. USA 118, e2116863118 (2021).
pubmed: 34876528
pmcid: 8685894
doi: 10.1073/pnas.2116863118
Laurer, M., van Atteveldt, W., Casas, A. & Welbers, K. Less annotating, more classifying: Addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert-nli. Political Anal. https://doi.org/10.1017/pan.2023.20 (2023).
Robertson, C. E. et al. Negativity drives online news consumption. Nat. Hum Behav. https://doi.org/10.1038/s41562-023-01538-4 (2022).
Moss, S. M., Ulug, O. M. & Acar, Y. G. Doing research in conflict contexts: practical and ethical challenges for researchers when conducting fieldwork. Peace Confl.: J. Peace Psychol. 25, 86–99 (2019).
doi: 10.1037/pac0000334
Van Bavel, J. J. & Pereira, A. The partisan brain: an identity-based model of political belief. Trends Cogn. Sci. 22, 213–224 (2018).
pubmed: 29475636
doi: 10.1016/j.tics.2018.01.004
Jensen, E. A. Putting the methodological brakes on claims to measure national happiness through twitter: methodological limitations in social media analytics. PLoS ONE 12, e0180080 (2017).
pubmed: 28880882
pmcid: 5589095
doi: 10.1371/journal.pone.0180080
Rácz, A. Russia’s Hybrid War In Ukraine: Breaking The Enemy’s Ability To Resist (Finnish Institute of International Affairs, 2015).
Eady, G. et al. Exposure to the russian internet research agency foreign influence campaign on twitter in the 2016 us election and its relationship to attitudes and voting behavior. Nat. Commun. 14, 62 (2023).
pubmed: 36624094
pmcid: 9829855
doi: 10.1038/s41467-022-35576-9
Kramer, A. D., Guillory, J. E. & Hancock, J. T. Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl Acad. Sci. USA 111, 8788 (2014).
pubmed: 24889601
pmcid: 4066473
doi: 10.1073/pnas.1320040111
Bar-Tal, D., Halperin, E. & De Rivera, J. Collective emotions in conflict situations: Societal implications. J. Soc. Issues 63, 441–460 (2007).
doi: 10.1111/j.1540-4560.2007.00518.x
Hsu, T. W. et al. Social media users produce more affect that supports cultural values, but are more influenced by affect that violates cultural values. J. Pers. Soc. Psychol. https://doi.org/10.1037/pspa0000282 (2021).
Sociological Group “Rating”. National poll: Ukraine at war (march 1, 2022). https://ratinggroup.ua/en/research/ukraine/obschenacionalnyy_opros_ukraina_v_usloviyah_voyny_1_marta_2022.html?fbclid=IwAR2LvNLxicdO0OIdLoQF3NvHPpAfi7PbbV2GjlHv2IzTSinKBF74u6Gzfz4 Accessed: 2022-11-28 (2022).
Berinsky, A. J.In Time of War (University of Chicago Press, 2009).
Guess, A. M. et al. How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 381, 398–404 (2023).
pubmed: 37498999
doi: 10.1126/science.abp9364
Metz, R. Likes, Anger Emojis And rsvps: The Math Behind Facebook’s News Feed - And How It Backfired ∣ Cnn Business. https://edition.cnn.com/2021/10/27/tech/facebook-papers-meaningful-social-interaction-news-feed-math/index.html Accessed: 2024-07-10 (2021).
Melnyk, T. Zuckerberg’s Social Networks Ban Ukrainian Accounts For Posts About The War. What Are The Options To Bypass “Anti-war" Moderation (In Ukrainian). https://forbes.ua/innovations/moderatsiya-16122022-10561 Accessed: 2023-08-01 (2022).
Ferrara, E. & Yang, Z. Measuring emotional contagion in social media. PLoS ONE 10, e0142390 (2015).
pubmed: 26544688
pmcid: 4636231
doi: 10.1371/journal.pone.0142390
Analytics, B. Most Used Social Media Platforms In Russia As Of October 2022, By Monthly Publications (In Millions). https://www.statista.com/statistics/284447/social-media-platforms-by-publications-russia/ Accessed: 2022-12-17 (2022).
Suciu, P. Ukraine Is Winning On The Battlefield And On Social Media. https://www.forbes.com/sites/petersuciu/2022/10/13/ukraine-is-winning-on-the-battlefield-and-on-social-media/?sh=539ae31e4008 Accessed: 2022-11-28 (2022).
Matias, J. N. Humans and algorithms work together - so study them together. Nature 617, 248–251 (2023).
pubmed: 37165234
doi: 10.1038/d41586-023-01521-z
Knief, U. & Forstmeier, W. Violating the normality assumption may be the lesser of two evils. Behav. Res. Methods 53, 2576–2590 (2021).
pubmed: 33963496
pmcid: 8613103
doi: 10.3758/s13428-021-01587-5
Schielzeth, H. et al. Robustness of linear mixed-effects models to violations of distributional assumptions. Methods Ecol. Evol. 11, 1141–1152 (2020).
doi: 10.1111/2041-210X.13434
Platten, S., Haji, R., & Boyd, R. L. Humanizing and dehumanizing themes of Muslims surrounding 9/11: Computerized language analysis [Poster]. 81st Canadian Psychological Association Annual National Convention, Montréal, Quebec, Canada. (2020).
Conneau, A. et al. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8440–8451 (eds. Jurafsky, D., Chai, J., Schluter, N. & Tetreault, J.) https://doi.org/10.18653/v1/2020.acl-main.747 (Association for Computational Linguistics, Online, 2020).
Zakharchenko, A. P. Pr-message analysis as a new method for the quantitative and qualitative communication campaign study. Information & Media 42–61, https://doi.org/10.15388/IM.2022.93.60 (2022).