KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Social Inquiry / 2023 / Cilt: 16 Sayı: 2

Emotions on Social Media: A Sentiment Analysis Approach Based on Twitter (X) Data on the Russian-Ukraine War

Ayşen Temel Eginli(EGE ÜNİVERSİTESİ, İLETİŞİM FAKÜLTESİ)0000-0003-4830-4524
Neslihan Özmelek Taş(PAMUKKALE ÜNİVERSİTESİ)0000-0002-6348-2495
Sayfa
445–459
Yayın Tarihi
2023-12-31
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Therefore, this research can be a clue for the consequences of international war on the masses.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Twitter (X) is an important tool that reflects the feelings and attitudes of the public.
📊
Temel Bulgular & Veri:In this research, Twitter posts made with the Ukraine hashtag between March 1 and April 30, 2022, during the Russia-Ukraine War, were eliminated with the "war" filter, and the expressions were analyzed using the sentiment analysis method.
💡
Sonuç & Katkı:Therefore, this research can be a clue for the consequences of international war on the masses.
🔬Tür:Araştırma Makalesi
📍Kapsam:2022 Verileri
⚙️Değişkenler:social media · Twitter · emotion

Abstract

Twitter (X) is an important tool that reflects the feelings and attitudes of the public. For this reason, in this study, especially when it comes to events that concern society, Twitter provides an opportunity to both follow the agenda and understand the reactions through instant sharing. Twitter is a social media platform that allows the public to convey their feelings, thoughts, and attitudes to the masses. Twitter provides the opportunity to stay up-to-date and understand reactions through instant posts, especially for social events. In this research, Twitter posts made with the Ukraine hashtag between March 1 and April 30, 2022, during the Russia-Ukraine War, were eliminated with the "war" filter, and the expressions were analyzed using the sentiment analysis method. Various URLs were eliminated, and research was carried out on ten thousand tweets. The tweets obtained were categorized as positive, negative, and neutral. Accordingly, the expressions containing positive, negative, and neutral emotions were analyzed by determining the emotional inferences of the words in the tweets with an artificial intelligence algorithm and then detailed by the researchers with content analysis. In this sense, this study becomes important in understanding how the masses express their reactions through emotional social media platforms and what their emotions are in this process. Therefore, this research can be a clue for the consequences of international war on the masses.

🔖

Bu Makaleye Atıf Yap

Eginli, A. T., Taş, N. Ö. (2023). Emotions on Social Media: A Sentiment Analysis Approach Based on Twitter (X) Data on the Russian-Ukraine War. International Journal of Social Inquiry, Cilt: 16 Sayı: 2, 445–459. https://doi.org/10.37093/ijsi.1336016

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (62)

  1. Adwan, O. M. A. R., Al–Tawil, M., Huneiti, A., Shahin, R., Zayed, A. A., & Al–Dibsi, R. (2020). Twitter sentiment analysis approaches: A survey. International Journal of Emerging Technologies in Learning, 15(15), 79–93. https://doi.org/10.3991/ijet.v15i15.14467
  2. Almohaimeed, A. S. (2017). Using tweets sentiment analysis to predict stock market movement (Doctoral dissertation, Auburn University).
  3. Appel, O., Chiclana, F., & Carter, J. (2015). Main concepts, state of the art and future research questions in sentiment analysis. Acta Polytechnica Hungarica, 12(3), 87–108. http://dx.doi.org/10.12700/APH.12.3.2015.3.6
  4. Aslan, M. (2022). Rusya’nın Ukrayna’ya saldırısının siyasi ve askeri değerlendirmesi [Political and military evaluation of Russia’s attack on Ukraine]. SETA–Perspektif, 332, 1–4. https://setav.org/assets/uploads/2022/02/P332.pdf.
  5. Aslan, M. (2023). Rusya–Ukrayna savaşının bir yılı [One year of the Russia–Ukraine war]. Seta.
  6. Aydıngün, A. (2022). Rusya–Ukrayna savaşının sosyolojik açıdan tahlili [Sociological analysis of the Russia–Ukraine war] (AVİM Rapor No: 20). Avrasya İncelemeleri Merkezi. Terazi Yayıncılık.
  7. Bağcan, S., Tuncay, E., Savaş, S. (2021). Sosyal medyada demokrasiye lider müdahalesini gözlemlemek: 2020 ABD başkanlık seçimlerinde Donal Trump ve Joe Biden’ın tweetlerinin içerik ve duygu analizi açısından incelenmesi [Observing the leader’s intervention to democracy on social media: An analysis of the tweets of Trump and Biden on Twıtter in the 2020 US presidential elections in terms of content and sentiment analysis]. The Turkish Online Journal of Design Art and Communication, 11(3), 1073–1097. https://dergipark.org.tr/tr/pub/tojdac/issue/62647/911077
  8. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer‐Mediated Communication, 13(1), 210–230. https://doi.org/10.1111/j.1083–6101.2007.00393.x
  9. 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. Proceedings of the National Academy of Sciences, 114(28), 7313–7318. https://doi.org/10.1073/pnas.1618923114
  10. Burke, M., & Develin, M. (2016). Once more with feeling: Supportive responses to social sharing on Facebook. In Proceedings of the 19th ACM Conference on Computer–Supported Cooperative Work & Social Computing (pp. 1462–1474). https://dl.acm.org/doi/proceedings/10.1145/2818048
  11. Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
  12. Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206
  13. Chatterje–Doody, P. N., & Crilley, R. (2019). Making sense of emotions and affective investments in war: RT and the Syrian conflict on YouTube. Media and Communication, 7(3), 167. https://doi.org/10.17645/mac.v7i3.1911
  14. Chen, K. (2021). Research on the functions of users’ emotions in social media product design. In E3S Web of Conferences (Vol. 253, p. 02010). EDP Sciences.
  15. Chislova, N. M. (2014). Means of expressing the joy emotion in modern Internet communication. Theoretical and applied aspects of linguistics: Com of mat. of II international. Science– pract. conf. of young researcher. Moscow: M.A. Sholokhov Moscow state University for the Humanities.
  16. Comito, C. (2023). The role of social media in the battle against COVID-19. In P. Chatterjee & M. Esposito (Eds.), Artificial Intelligence in Healthcare and COVID-19 (pp. 105–124). Academic Press. https://doi.org/10.1016/B978-0-323-90531-2.00002-3
  17. Çelikaslan, M. N. (2022). Ukrayna–Rusya savaşı: Avrupalı bir milletin yıkımı [Ukraine–Russia war: the destruction of a European nation]. Türk Yönetim ve Ekonomi Araştırmaları Dergisi, 3(1), 53–65. https://sakajournals.org/ojs/index.php/tjmer/article/view/56
  18. Davutoğlu, A. (2010). Stratejik derinlik: Türkiye’nin uluslararası konumu [Strategic depth: Türkiye’s international position]. Küre Yayınları.
  19. Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3–4), 169–200. https://doi.org/10.1080/02699939208411068
  20. Ekman, P. (1999). Basic Emotions. In T. Dalgleish & M. Power (Eds.), Handbook of Cognition and Emotion (pp. 45–60). John Wiley & Sons.
  21. Esuli, A., & Sebastiani, F. (2007). SentiWordNet: A high–coverage lexical resource for opinion mining. Evaluation, 17(1), 1–26. http://nmis.isti.cnr.it/sebastiani/Publications/2007TR02.pdf
  22. Fidan, M., & Lokmanoğlu, E. (2023). Sosyal medya savaşlarında yeni bir kavram: Rusya–Ukrayna savaşı özelinde dijital ambargo [A new concept in social media wars: Russia–Ukraine war and digital embargo]. Bilecik Şeyh Edebali University Journal of Social Science, 8(1), 28–38. https://doi.org/10.33905/bseusbed.1200734
  23. Fujii, S., Kunii, Y., Nonaka, S., Hamaie, Y., Hino, M., Egawa, S., Kuriyama, S., & Tomita, H. (2023). Real–time prediction of medical demand and mental health status in Ukraine under Russian invasion using tweet analysis. The Tohoku Journal of Experimental Medicine, 259(3), 177–188. https://doi.org/10.1620/tjem.2022.J111
  24. Garcia, M. B., & Cunanan–Yabut, A. (2022). Public sentiment and emotion analyses of Twitter data on the 2022 Russian invasion of Ukraine. 9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), (pp. 242–247). https://doi.org/10.1109/ICITACEE55701.2022.9924136
  25. Giachanou, A., & Crestani, F. (2016). Like it or not: A survey of Twitter sentiment analysis methods. ACM Computing Surveys (CSUR), 49(2), 1–41. https://doi.org/10.1145/2938640
  26. Gonçalves, P., Araújo, M., Benevenuto, F., & Cha, M. (2013, October). Comparing and combining sentiment analysis methods. In Proceedings of the first ACM conference on Online social networks (pp. 27–38). https://dl.acm.org/doi/10.1145/2512938.2512951
  27. Hyvärinen, H., & Beck, R. (2018). Emotions trump facts: The role of emotions in on social media: A literature review. In Proceedings of the 51st Hawaii International Conference on System Sciences (pp. 1797–1806). http://dx.doi.org/10.24251/HICSS.2018.226
  28. Johnson, P. R., & Yang, S. (2009, August). Uses and gratifications of Twitter: An examination of user motives and satisfaction of Twitter use. In Communication Technology Division of the annual convention of the Association for Education in Journalism and Mass Communication in Boston, MA (Vol. 54).
  29. Joshi, P. (2017). Artificial intelligence with Python. Packt Publishing Ltd.
  30. Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003
  31. Kasmani, M. F., Sabran, R., & Ramle, N. (2014). Can Twitter be an effective platform for political discourse in Malaysia? A study of #PRU13. Procedia – Social and Behavioral Sciences, 155, 348–355. https://doi.org/10.1016/j.sbspro.2014.10.304
  32. Kayıkçı, Ş. (2022). SenDemonNet: Sentiment analysis for demonetization tweets using heuristic deep neural network. Multimedia Tools and Applications, 81(8), 11341-11378. https://doi.org/10.1007/s11042-022-11929-w
  33. Kharde, V., & Sonawane, P. (2016). Sentiment analysis of Twitter data: A survey of techniques. International Journal of Computer Applications, 139(11), 5–15. https://doi.org/10.5120/ijca2016908625
  34. Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications. https://doi.org/10.4135/9781071878781
  35. Lange–Ionatamishvili, E., & Svetoka, S. (2015). Strategic communications and social media in the Russia Ukraine conflict. In K. Geers (Ed.), Cyber war in perspective: Russian aggression against Ukraine (pp. 103–111). NATO Cooperative Cyber Defence Center of Excellence.
  36. Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Science, 50(11), 1477–1490. http://dx.doi.org/10.1287/mnsc.1030.0136
  37. Mæland, B., Brunstad, P., & Mæland, B. (2009). Enduring military boredom: From 1750 to the present. Springer.
  38. Malešević, S. (2010). The sociology of war and violence. Cambridge University Press.
  39. Malešević, S. (2021). Emotions and warfare: The social dynamics of close–range fighting. In Oxford Research Encyclopedia of Politics. UK.
  40. Mallick, P. K. (2022). Ukraine is winning the information war against Russia on social media: But experts say it is far from over. Centre for Land Warfare Studies.
  41. Moshagen, M., & Hilbig, B. E. (2022, March 7). Citizens’ psychological reactions following the Russian invasion of the Ukraine: A cross–national study. PsyArXiv. https://doi.org/10.31234/osf.io/teh8y
  42. Orehek, E., & Human, L. J. (2016). Self–expression on social media: Do tweets present accurate and positive portraits of ımpulsivity, self–esteem, and attachment style? Personality and Social Psychology Bulletin, 43(1), 60–70. https://doi.org/10.1177/0146167216675332
  43. Öztürk, Ş. (2023). Rusya–Ukrayna savaşı üzerinden uluslararası sisteme dair bir değerlendirme [An assessment on the internatıonal system over the Russia–Ukraine war]. Dicle Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (32), 449–462. https://dergipark.org.tr/tr/pub/diclesosbed/issue/76043/1203938
  44. Pang, B., Lee, L., & Vaithyanathan, S. (2002, May 28). Thumbs up? Sentiment classification using machine learning techniques. Cornell University. ArXiv.org/abs/cs/0205070
  45. Panger, G. T. (2017). Emotion in social media. University of California, Berkeley.
  46. Plutchik, R. (2001). The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American Scientist, 89(4), 344–350. http://www.jstor.org/stable/27857503
  47. Ramirez–Sayago, E. (2020). Sentiment analysis from Twitter data regarding the COVID–19 pandemic. https://www.researchgate.net/publication/346453096_Sentiment_Analysis_from_Twitter_Data_Regarding_the_COVID–19_Pandemic
  48. Reich, S. M. (2010). Adolescents’ sense of community on Myspace and Facebook: A mixed–methods approach. Journal of Community Psychology, 38(6), 688–705. https://doi.org/10.1002/jcop.20389
  49. Robertson, S. P., Douglas, S., Maruyama, M., & Semaan, B. (2013). Political discourse on social networking sites: Sentiment, in–group/out–group orientation and rationality. Information Polity, 18(2), 107–126. http://dx.doi.org/10.3233/IP–130303
  50. Sabatovych, I. (2019). Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine. Global Media and Communication, 15(3), 275–283. https://doi.org/10.1177/1742766519872780
  51. Serrano–Puche, J. (2016). Internet and emotions: New trends in an emerging field of research. Media Education Journal, Comunicar, 46(24), 19–26. https://doi.org/10.3916/C46–2016–02
  52. Sönmez, A. S., Bıçakcı, H., & Yıldırım, C. (2015). Kırım sorunu bağlamında Rusya–Ukrayna ilişkilerinin analizi [Analysis of Russia–Ukraine relations in context of Crimea problem]. International Journal of Social Sciences and Education Research, 1(3), 656–670.) https://doi.org/10.24289/ijsser.279144
  53. Thelwall, M., Wilkinson, D., & Uppal, S. (2010). Data mining emotion in social network communication: Gender differences in Myspace. Journal of the American Society for Information Science and Technology, 61(1), 190–199. https://doi.org/10.1002/asi.21180
  54. Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012, July). A system for real–time Twitter sentiment analysis of 2012 US presidential election cycle. In Proceedings of the ACL 2012 System Demonstrations (pp. 115–120). https://aclanthology.org/P12–3020.pdf
  55. Waterloo, S. F., Baumgartner, S. E., Peter, J., & Valkenburg, P. M. (2018). Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp. New Media & Society, 20(5), 1813–1831. https://doi.org/10.1177/1461444817707349
  56. Webster, S. W., & Albertson, B. (2022). Emotion and politics: Noncognitive psychological biases in public opinion. Annual Review of Political Science, 25(1), 401–418. https://doi.org/10.1146/annurev–polisci–051120–105353
  57. Weeks, B. E., Ardèvol–Abreu, A., & Gil de Zúñiga, H. (2017). Online influence? Social media use, opinion leadership, and political persuasion. International Journal of Public Opinion Research, 29(2), 214–239. https://doi.org/10.1093/ijpor/edv050
  58. White, M. D., & Marsh, E. E. (2006). Content analysis: A flexible methodology. Library Trends, 55(1), 22–45. https://doi.org/10.1353/lib.2006.0053
  59. Widén, G., Lindström, J., Brännback, M., Huvila, I., & Nyström, A. G. (2015). Mixed emotions in active social media use–fun and convenient or shameful and embarrassing? In Conference 2015 Proceedings. http://hdl.handle.net/2142/73460
  60. Wilson, V., (2011). Research methods: Content analysis. Evidence Based Library and Information Practice, 6(4), 177–179. http://doi.org/10.1177/0165551504044668
  61. Yazıcı, F., & Apak, C. (2022). Rusya–Ukrayna savaşı haberleri üzerine bir inceleme: Sputnik Türkiye Twitter hesabı örneği [A review on the Russia–Ukraine war news: The example of Sputnik Türkiye Twitter account]. Erciyes Akademi, 36(3), 1055–1070. https://doi.org/10.48070/erciyesakademi.1143475
  62. Yu, H., & John–Baptiste, S. (2016). Emotion expression on social networking sites: A study of young persons’ use of Facebook and Twitter in the UK. In 5th Annual International Conference on Cognitive and Behavioral Psychology (pp. 81–86). http://dx.doi.org/10.5176/2251–1865_CBP16.20