Russia Conflict on Twitter: Social factors and polarity on users’ interactions
Resumen
In the aftermath of armed conflicts, societal expressions unfold through diverse communication channels, with Twitter. Individuals share these expressions, aiming for broader societal consumption, fostering interaction across impacted entities—individuals, businesses, organizations, and governments. This analytical endeavor aims to analyze interaction patterns responding to sociocultural factors and sentimentally charged content on Twitter in the context of the Russia-Ukraine conflict. This research employed a sequential mixed approach to examine social factors in user publications on Twitter and assess their impact on interactions, considering sentimental polarity. The qualitative phase involved netnographic exploration of a total of 2578 tweets, collected from users World Trade Organization since February 24, 2022, until March 31, 2022. The subsequent quantitative phase analyzed the relationship between social factors, sentimental polarity, and user interactions through decision tree analysis. The results show that notably, the categories MET-Mention (35.82%) and MSG-Message (35.82%) emerged as the most frequent Two interactions were the most common (52.5%). The primary theme discussed in the messages was Information with 52.99% of the twits. Negative polarity emerged as the factor triggering more engagement, resulting in higher interaction levels. The majority of interactions (52.5%) were characterized by two interactions. In conclusion, the dominance of the information category underscores the pivotal role of social media in disseminating information during global events. Furthermore, negative sentiment, is associated with conflict-related concerns, correlated with higher interaction levels.
Citas
Atad, E., Lev-On, A., & Yavetz, G. (2023). Diplomacy under fire: Engagement with governmental versus non-governmental messages on social media during armed conflicts. Government Information Quarterly, 40(3). https://doi.org/10.1016/j.giq.2023.101835
Bozdag, C., & Smets, K. (2017). Understanding the images of Alan Kurdi with “small data”: A qualitative, comparative analysis of tweets about refugees in Turkey and Flanders (Belgium). International Journal of Communication, 11, 4046-4069.
Chen, L., Chen, J., & Xia, C. (2022). Social network behavior and public opinion manipulation. Journal of Information Security and Applications, 64.
Coelho, R., Oliveira, D., & and Almeida, M. (2016). Does social media matter for post typology? Impact of post content on Facebook and Instagram metrics. Online Information Review, 40(4), 458-471. https://doi.org/10.1108/OIR-06-2015-0176
Dar, A., & Deb, S. (2022). Prevalence of trauma among young adults exposed to stressful events of armed conflicts in South Asia: Experiences from Kashmir. Psychological Trauma: Theory, Research, Practice, and Policy, 14(4), 633–641. https://doi.org/10.1037/tra0001045
Endam, R., & Wasum, F. (2022). Russian-Ukraine 2022 War: A Review of the Economic Impact of RussianUkraine Crisis on the USA. Advances in Social Sciences Research Journal, 9(3), 144-153. https://doi.org/10.14738/assrj.93.12005
Fahmy, S., Taha, B., & Karademir, H. (2022). Journalistic Practices on Twitter: A Comparative Visual Study on the Personalization of Conflict Reporting on Social Media. Online Media and Global Communication, 1(1), 23-59. https://doi.org/10.1515/omgc-2022-0008
Garcia, M., & Cunanan-Yabut, A. (2022). Public Sentiment and Emotion Analyses of Twitter Data on the 2022 Russian Invasion of Ukraine,” 2022 9th. International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), (pp. 242-247). Semarang, Indionesia. https://doi.org/10.1109/ICITACEE55701.2022.9924136.
Ghounane, N. (2020). Moodle or Social Networks: What Alternative Refuge Is Appropriate to Algerian EFL Students to Learn during COVID-19 Pandemic. Arab World English Journal, 11(3), 21-41. https://doi.org/10.24093/awej/vol11no3.2
Giesler, M., & Thompson, C. J. (2016). Process Theorization in Cultural Consumer Research. Journal of Consumer Research, 43(4), 497-508. https://doi.org/10.1093/jcr/ucw047
Hagemann, L., & Abramova, O. (2023). Sentiment, we-talk and engagement on social media: insights from Twitter data mining on the US presidential elections 2020. Internet Research. doi:https://doi.org/10.1108/INTR-12-2021-0885
Hernández - Sampieri, R., & Mendoza - Torres, C. (2018). Metodología de la Investigación. Las rutas cuantitativa, cualitativa y mixta. Mc Graw Hill Education.
Jin, Y., Liu, B. F., & Austin, L. L. (2014). Examining the role of social media in effective crisis management: The effects of crisis origin, information form, and source on publics’ crisis responses. Communication research, 41(1), 74-94. https://doi.org/10.1177/0093650211423918
Johnson, N., Turnbull, B., & Reisslein, M. (2022). Social media influence, trust, and conflict: An interview based study of leadership perceptions. Technology in Society, 68.
Kabalmay, J., Avenzora, R., Darusman, D., & Zulbairnarni, N. (2022). Social Values Analysis Toward Ecotourism Development in The Kei Islands. Jurnal Manajemen Hutan Tropika, 28(2), 101-101. https://journal.ipb.ac.id/index.php/jmht/upcoming/view/39533
Kozinets, R. V. (2002a). Can consumers escape the market? Emancipatory illuminations from burning man. Journal of Consumer research, 29(1), 20-38. https://doi.org/10.1086/339919
Kozinets, R. V. (2002b). The field behind the screen: Using netnography for marketing research in online communities. Journal of marketing research, 39(1), 61-72. doi:https://doi.org/10.1509/jmkr.39.1.61.18935
Kucheriava, Y. (2022). Russia’s Invasion of Ukraine: A WTO Perspective. Global Trade and Customs Journal, 17(10), 417 – 430. https://doi.org/10.54648/gtcj2022060
Lee, C., Cheang, Y., & Moslehpour, M. (2022). Predictive Analytics in Business Analytics: Decision Tree. Advances in Decision Sciences, 26(1), 1 - 29. https://ideas.repec.org/a/aag/wpaper/v26y2022i1p1-30.html
Leeman, Y., & van Koeven, E. (2019). New immigrants. An incentive for intercultural education? Education Inquiry, 10(3), 189-207. https://doi.org/10.1080/20004508.2018.1541675
Lin, F., Li, X., Jia, N., Feng, F., Huang, H., Huang, J., . . . Song, J.-P. (2023). The impact of Russia-Ukraine conflict on global food security. Global Food Security, 36.
Linvill, D. L., Henderson, W. J., & Mikkilineni, S. (2021). Divisive Social Movement on Social Media: Examining# ADOS. Southern Communication Journal, 86(4), 349-361. https://doi.org/10.1080/1041794X.2021.1919917
Ngo, V., Huynh, T., Nguyen, P., & Nguyen, H. (2022). Public sentiment towards economic sanctions in the Russia–Ukraine war. Scottish Journal of Political Economy, 69, 564–573. https://doi.org/10.1111/sjpe.12331
Nitoiu, C., & Pasatoiu, F. (2023). Public diplomacy and the persistence of the conflict and cooperation dichotomy in EU-Russia relations. Journal of Contemporary European Studies, 31(1), 21-34. https://doi.org/10.1080/14782804.2022.2100983
Olimat, S. N. (2020). Words as Powerful Weapons: Dysphemism in Trump’s Covid-19 Speeches. 3L: Language, Linguistics, Literature®, 26(3), 17-29. https://doi.org/10.17576/3L-2020-2603-02
Perez-Cepeda, M., & Arias-Bolzmann, L. (2020). Influence of Ecuadorian Homosexual Subculture in Consumption Culture: Study about Information Consumption on Twitter. Journal of Promotion Management, 26(5), 704-725. https://doi.org/10.1080/10496491.2020.1729317
Perez-Cepeda, M., & Arias-Bolzmann, L. G. (2021). Refugee information consumption on Twitter. Journal of Business Research, 123, 529-537. https://doi.org/10.1016/j.jbusres.2020.10.029
Perez-Cepeda, M., & Arias-Bolzmann, L. G. (2022). Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter. Journal of Business Research, 140, 384-393. https://doi.org/10.1016/j.jbusres.2021.11.008
Pohl, J., Seiler, M. V., Assenmacher, D., & Grimme, C. (2022). A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022. Available at SSRN.
Polyzos, E. (2023). Escalating Tension and the War in Ukraine: Evidence Using Impulse Response Functions on Economic Indicators and Twitter Sentiment. Research in International Business and Finance, 66, 102044. https://doi.org/10.2139/ssrn.4058364
Rogstad, I. (2016). Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2), 142-158.
Samuel, A., & Sharma, D. K. (2017). A spatial, temporal and sentiment based framework for indexing and clustering in twitter blogosphere. Journal of Intelligent & Fuzzy Systems, 32(5), 3619-3632. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169297
Schillinger, D., Chittamuru, D., & Ramírez, A. S. (2020). From “infodemics” to health promotion: A novel framework for the role of social media in public health. American journal of public health, 110(9), 1393-1396. https://doi.org/10.2105/AJPH.2020.30574
Sielska, Z. (2023). Emotions During the War: Interplay Between Emotions and Information. In A. Turska-Kawa, A. Kasińska-Metryka, & K. (. Pałka-Suchojad, War in Ukraine. Media and Emotions. Cham: Palgrave Macmillan.
Soleimanvandi - Azar, N., Irandoost, S. F., Ahmadi, S., Xosravi, T., Ranjbar, H., Mansourian, M., & Lebni, J. Y. (2021). Explaining the reasons for not maintaining the health guidelines to prevent COVID-19 in high-risk jobs: A qualitative study in Iran. BMC public health, 21(1), 1-15. https://doi.org/10.1186/s12889-021-10889-4
Sufi, F. (2023). Social Media Analytics on Russia–Ukraine Cyber War with Natural Language Processing: Perspectives and Challenges. Information, 14, 485. https://doi.org/10.3390/info14090485
Tao, W., & Peng, Y. (2023). Differentiation and unity: A Cross-platform Comparison Analysis of Online Posts’ Semantics of the Russian–Ukrainian War Based on Weibo and Twitter. Communication and the Public, 8(2), 105-124. https://doi.org/10.1177/2057047323
Vyas, P., Vyas, G., & Dhiman, G. (2023). RUemo—The Classification Framework for Russia-Ukraine War-Related Societal Emotions on Twitter through Machine Learning. Algorithms, 16(69). https://doi.org/10.3390/a16020069
Wadhwani, G.K., Varshney, P.K., Gupta, A. et al. (2023). Sentiment Analysis and Comprehensive Evaluation of Supervised Machine Learning Models Using Twitter Data on Russia–Ukraine War. N COMPUT. SCI., 4(346). https://doi.org/10.1007/s42979-023-01790-5
Wills, E. R., & Fecteau, A. (2016). Humor and identity on Twitter:# muslimcandyheartrejects as a digital space for identity construction. Journal of Muslim Minority Affairs, 36(1), 32-45. https://doi.org/10.1080/13602004.2016.1153825
Woo, C. W., Brigham, M. P., & Gulotta, M. (2020). Twitter Talk and Twitter Sharing in Times of Crisis: Exploring Rhetorical Motive and Agenda-Setting in the Ray Rice Scandal. Communication Studies, 71(1), 40-58. https://doi.org/10510974.2019.1661866
Zimmer, M., & Proferes, N. J. (2014). A topology of Twitter research: Disciplines, methods, and ethics. Aslib Journal of Information Management, 66(3), 250-261. https://doi.org/10.1108/AJIM-09-2013-0083
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