Markaların Sosyal Sorumluluk Projelerindeki Öz-Çelişkiyi Keşfetmek: Dove'un Real Beauty Sketches Kampanyasının Duygu Analizi

Abstract

Özbenlik çelişki teorisi, bireylerin duygusal tepkilerinin, gerçek benlikleriyle ideal ya da olması gereken benlikleri arasındaki uyum düzeyine bağlı olarak şekillendiğini öne sürmektedir. Benlikler arası yüksek tutarsızlık olumsuz duyguların ortaya çıkmasına neden olurken, ideal veya olması gereken benliğe yakınlık hissi, olumlu duygular deneyimlenmesini sağlar. Medyanın bireylerin öz-algılarını belirgin biçimde şekillendirmesi, hem geleneksel hem de dijital medyanın özbenlik çelişkisi üzerindeki rolünü artırmaktadır. Ticari reklamlar, tüketicileri satın almaya teşvik etme amacıyla gerçek benlik ile ideal veya olması gereken benlik arasındaki mesafenin fazla olduğu algısını pekiştirirken; sosyal sorumluluk kampanyaları ise öz-kabul ve duygusal esenliği teşvik ederek bu tutarsızlığı azaltmayı ve böylece tüketici sadakatini güçlendirmeyi hedeflemektedir. Bu çalışma, sosyal sorumluluk odaklı yaklaşımlara ilişkin kamuoyu algısını incelemek amacıyla, bir örnek olay olarak “Dove Real Beauty Sketches” kampanyasını ele almaktadır. Çalışmanın veri setini, özbenlik çelişkisi temasını ele alan bu kurumsal sosyal sorumluluk kampanyasına ait Dove ABD YouTube kanalında yayımlanan videonun altına, 2013 ile 2024 tarihleri arasında yapılan 9.940 kullanıcı yorumu oluşturmaktadır. Yorumlar, R programlama dili kullanılarak Syuzhet paketindeki NRC, AFINN ve Bing sözlükleriyle gerçekleştirilen duygu analizi yöntemiyle incelenmiştir. Elde edilen bulgular, kullanıcı yorumlarında en sık ifade edilen duyguların neşe ve güven olduğunu, ayrıca olumlu duyguların olumsuz duygulara kıyasla daha baskın bir şekilde dile getirildiğini ortaya koymuştur.

Keywords

• Sosyal Sorumluluk Kampanyaları
• Öz-Tutarsızlık Teorisi
• Sosyal Medya
• Duygu Analizi
• R Programlama

Exploring Self-Discrepancy in Brands’ Social Responsibility Projects: A Sentiment Analysis of Dove’s Real Beauty Sketches Campaign

Abstract

Self-discrepancy theory suggests that individuals’ emotional responses are influenced by the alignment between their actual self and their ideal or ought self. A high level of self-discrepancy often gives rise to negative emotions, whereas a perceived proximity to one's ideal or ought self-fosters positive affect. Media-driven standards significantly shape self-perceptions, making both traditional and digital media pivotal in influencing self-discrepancy. Commercial advertising typically widens this perceived gap to drive consumer behavior. At the same time, brand social responsibility campaigns aim to narrow it by fostering self-acceptance and emotional well-being, ultimately enhancing consumer loyalty. This study examines public perceptions of such initiatives through an analysis of the “Dove Real Beauty Sketches” campaign. The campaign is particularly relevant as it explicitly challenges internalized beauty standards and encourages individuals to reassess their self-perceptions. To assess audience engagement and emotional reactions, sentiment analysis was conducted on 9,940 comments posted on Dove’s U.S. YouTube channel between 2013 and 2024. Using R programming and the NRC, AFINN, and Bing lexicons from the Syuzhet package, the analysis revealed that joy and trust were the predominant emotions, with positive sentiments outweighing negative ones. These findings suggest that the campaign was largely successful in eliciting positive emotional responses. Interpreted through the lens of self-discrepancy theory, the dominance of positive affect may indicate a perceived reduction in the gap between viewers’ actual and ideal selves, highlighting the emotional resonance of social responsibility campaigns that promote self-acceptance.

Keywords

• Social Responsibility Campaigns
• Self-Discrepancy Theory
• Social Media
• Sentiment Analysis
• R Programming

References

1. Alabid, N. N. (2024). A review of sentiment analysis in social media perspectives. Journal of Kufa for Mathematics and Computer, 11(2), 1–11. https://doi.org/10.31642/jokmc/2018/110201
2. Balande, B. B., Kolte, D. M., Manza, R. R., & Revate, S. S. (2023). Pre-processing techniques for performing hotel review sentiment analysis. In Proceedings of the International Conference on Future Technologies (pp. 1–6). https://doi.org/10.1109/incoft60753.2023.10425536
3. Bandura, A. (1977). Social learning theory. Prentice Hall.
4. Bandura, A. (2001). Social cognitive theory of mass communication. Media Psychology, 3(3), 265–299. https://doi.org/10.1207/S1532785XMEP0303_03
5. Bandura, A. (2021). Analysis of modeling processes. In A. Bandura (Ed.), Psychological modeling: Conflicting theories (pp. 1–62). Routledge. https://doi.org/10.4324/9781003110156-1
6. Baumeister, R. F. (1999). The self in social psychology. Psychology Press.
7. Bhuiyan, H., Ara, J., Bardhan, R., & Islam, M. R. (2017, September). Retrieving YouTube video by sentiment analysis on user comment. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 474–478). IEEE. https://doi.org/10.1109/ICSIPA.2017.8120658
8. Burrow, A. L., & Hill, P. L. (2011). Purpose as a form of identity capital for positive youth adjustment. Developmental Psychology, 47(4), 1196–1206. https://doi.org/10.1037/a0023818

9. Campbell, J. D., Chew, B., & Scratchley, L. S. (1991). Cognitive and emotional reactions to daily events: The effects of self‐esteem and self‐complexity. Journal of Personality, 59(3), 473–505. https://doi.org/10.1111/j.1467-6494.1991.tb00257.x
10. Choukas-Bradley, S., Nesi, J., Widman, L., & Galla, B. M. (2020). The appearance-related social media consciousness scale: Development and validation with adolescents and young adults. Body Image, 33, 164–174. https://doi.org/10.1016/j.bodyim.2020.02.017
11. Corcoran, K., Crusius, J., & Mussweiler, T. (2011). Social comparison: Motives, standards, and mechanisms. In D. Chadee (Ed.), Theories in social psychology (pp. 119–139). Wiley-Blackwell.
12. Corrigan, P. W., Druss, B. G., & Perlick, D. A. (2012). The impact of mental illness stigma on seeking and participating in mental health care. Psychological Science in the Public Interest, 15(2), 37–70. https://doi.org/10.1177/1529100614531398
13. Cwynar-Horta, J. (2016). The commodification of the body positive movement on Instagram. Stream: Culture/Politics/Technology, 8(2), 36–56. https://doi.org/10.21810/strm.v8i2.203
14. Dahl, D. W., Frankenberger, K. D., & Manchanda, R. V. (2003). Does it pay to shock? Reactions to shocking and nonshocking advertising content among university students. Journal of Advertising Research, 43(3), 268–280. https://doi.org/10.1017/S0021849903030332
15. Daszkiewicz, M. (2022). Supporting self-esteem and self-acceptance in commercial brand campaigns created during a pandemic: Social and marketing aspects. Ekonomia–Wroclaw Economic Review, 28(3), 109–127. https://doi.org/10.19195/2658-1310.28.3.7
16. Dove United States. (2013, April 14). Dove real beauty sketches [Video]. YouTube. https://www.youtube.com/watch?v=XpaOjMXyJGk
17. Eisend, M. (2010). A meta-analysis of gender roles in advertising. Journal of the Academy of Marketing Science, 38(4), 418–440. https://doi.org/10.1007/s11747-009-0181-x
18. Escalas, J. E., & Bettman, J. R. (2003). You are what they eat: The influence of reference groups on consumers' connections to brands. Journal of Consumer Psychology, 13(3), 339–348. https://doi.org/10.1207/S15327663JCP1303_14
19. Etcoff, N., Orbach, S., Scott, J., & D’Agostino, H. (2004). The real truth about beauty: A global report. Dove Self-Esteem Fund. http://www.clubofamsterdam.com/contentarticles/52%20Beauty/dove_white_paper_final.pdf
20. Feng, Y., Chen, H., & He, L. (2019). Consumer responses to femvertising: A data-mining case of Dove’s “Campaign for Real Beauty” on YouTube. Journal of Advertising, 48(3), 292–301. https://doi.org/10.1080/00913367.2019.1602858
21. Gazzola, P., Pavione, E., Pezzetti, R., & Grechi, D. (2020). Trends in the fashion industry: The perception of sustainability and circular economy. Sustainability, 12(7), 2809. https://doi.org/10.3390/su12072809
22. Gerber, J. P., Wheeler, L., & Suls, J. (2018). A social comparison theory meta-analysis 60+ years later. Psychological Bulletin, 144(2), 177–197. https://doi.org/10.1037/bul0000127
23. Grabe, S., Ward, L. M., & Hyde, J. S. (2008). The role of the media in body image concerns among women: A meta-analysis of experimental and correlational studies. Psychological Bulletin, 134(3), 460–476. https://doi.org/10.1037/0033-2909.134.3.460
24. Halliwell, E., & Dittmar, H. (2004). Does size matter? The impact of model's body size on women's body-focused anxiety and advertising effectiveness. Journal of Social and Clinical Psychology, 23(1), 104–122. https://doi.org/10.1521/jscp.23.1.104.26989
25. Heine, S. J., Lehman, D. R., Markus, H. R., & Kitayama, S. (1999). Is there a universal need for positive self-regard? Psychological Review, 106(4), 766–794. https://doi.org/10.1037/0033-295X.106.4.766
26. Higgins, E. T. (1987). Self-discrepancy: A theory relating self and affect. Psychological Review, 94(3), 319–340. https://doi.org/10.1037/0033-295X.94.3.319
27. Higgins, E. T. (1989a). Continuities and discontinuities in self-regulatory and self-evaluative processes: A developmental theory relating self and affect. Journal of Personality, 57(2), 407–444. https://doi.org/10.1111/j.1467-6494.1989.tb00488.x
28. Higgins, E. T. (1989b). Self-discrepancy theory: What patterns of self-beliefs cause people to suffer? Advances in Experimental Social Psychology, 22, 93–136. https://doi.org/10.1016/S0065-2601(08)60306-8
29. Higgins, E. T., Bond, R. N., Klein, R., & Strauman, T. (1986). Self-discrepancies and emotional vulnerability: How magnitude, accessibility, and type of discrepancy influence affect. Journal of Personality and Social Psychology, 51(1), 5–15. https://doi.org/10.1037/0022-3514.51.1.5
30. Hofmann, S., Sommermann, C., Kraus, M., Zschech, P., & Rosenberger, J. (2025). Hate speech and sentiment of YouTube video comments from public and private sources covering the Israel–Palestine conflict. arXiv. https://doi.org/10.48550/arXiv.2503.10648
31. Hymel, S., & Swearer, S. M. (2015). Four decades of research on school bullying: An introduction. American Psychologist, 70(4), 293–299. https://doi.org/10.1037/a0038928
32. Jin, H. (2022). From information to affirmation: An investigation on the echo chamber effect from YouTube comments under technology product reviews. arXiv. https://doi.org/10.48550/arXiv.2211.00483
33. Khanum, F., Lakshmi, P. S., & Reddy, H. V. (2024). Sentiment analysis using natural language processing, machine learning and deep learning. In Proceedings of the IEEE International Conference on Communication and Computing Technologies (I4C) (pp. 113–118). https://doi.org/10.1109/i4c62240.2024.10748425
34. Kim, J. C., & Chung, K. Y. (2021). Knowledge expansion of metadata using script mining analysis in multimedia recommendation. Multimedia Tools and Applications, 80, 34679–34695. https://doi.org/10.1007/s11042-020-08774-0
35. Kim, J., & Kim, M. (2025). From comparison to confidence: The Dove Self-Esteem Project and the transformation of beauty perceptions on social media. Behavioral Sciences, 15(4), 414–437. https://doi.org/10.3390/bs15040414
36. King, L. A., & Hicks, J. A. (2007). Whatever happened to “what might have been”? Regrets, happiness, and maturity. American Psychologist, 62(7), 625–636. https://doi.org/10.1037/0003-066X.62.7.625
37. Kotler, P., & Lee, N. (2008). Corporate social responsibility: Doing the most good for your company and your cause. Wiley.
38. Kumar, A., & Sebastian, T. M. (2012). Sentiment analysis: A perspective on its past, present, and future. International Journal of Intelligent Systems and Applications, 4(10), 1–14. https://doi.org/10.5815/ijisa.2012.10.01
39. Mahalakshmi, L., & Anbalagan, E. (2024). National language processing for sentiment analysis in social media—A comprehensive review. In Proceedings of the IEEE International Conference on Communication and Computing Technologies (ICCPCT) (pp. 504–508). https://doi.org/10.1109/iccpct61902.2024.10672661
40. Maison, D., & Adamczyk, D. (2020). The relations between materialism, consumer decisions, and advertising perception. Procedia Computer Science, 176, 2526–2535. https://doi.org/10.1016/j.procs.2020.09.320
41. Markus, H. R., & Kitayama, S. (2014). Culture and the self: Implications for cognition, emotion, and motivation. In R. S. Feldman (Ed.), College student development and academic life (pp. 264–293). Routledge.
42. Markus, H., & Nurius, P. (1986). Possible selves. American Psychologist, 41(9), 954–969. https://doi.org/10.1037/0003-066X.41.9.954
43. Matsumoto, D. (1990). Cultural similarities and differences in display rules. Motivation and Emotion, 14(3), 195–214. https://doi.org/10.1007/BF00995569
44. Mohammad, S. M., & Turney, P. D. (2013a). Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3), 436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
45. Mohammad, S. M., & Turney, P. D. (2013b). NRC emotion lexicon [Data set]. National Research Council Canada. https://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm
46. Moretti, M. M., & Higgins, E. T. (1990). Relating self-discrepancy to self-esteem: The contribution of discrepancy beyond actual-self ratings. Journal of Experimental Social Psychology, 26(2), 108–123. https://doi.org/10.1016/0022-1031(90)90071-S
47. Naldi, M. (2019). A review of sentiment computation methods with R packages. arXiv. https://doi.org/10.48550/arXiv.1901.08319
48. Nielsen, F. Å. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Proceedings of the ESWC 2011 Workshop on Making Sense of Microposts: Big things come in small packages (pp. 93–98). CEUR Workshop Proceedings, 718. https://ceur-ws.org/Vol-718/paper18.pdf
49. Olsson, M., & Martiny, S. E. (2018). Does exposure to counter stereotypical role models influence girls’ and women’s gender stereotypes and career choices? A review of social psychological research. Frontiers in Psychology, 9, 2264. https://doi.org/10.3389/fpsyg.2018.02264
50. Pavlenko, A. (2008). Emotion and emotion-laden words in the bilingual lexicon. Bilingualism: Language and Cognition, 11(2), 147–164. https://doi.org/10.1017/S1366728908003283
51. Perloff, R. M. (2014). Social media effects on young women’s body image concerns: Theoretical perspectives and an agenda for research. Sex Roles, 71(11–12), 363–377. https://doi.org/10.1007/s11199-014-0384-6
52. Richins, M. L. (1991). Social comparison and the idealized images of advertising. Journal of Consumer Research, 18(1), 71–83. http://www.jstor.org/stable/2489486
53. Singh, R., & Tiwari, A. (2021). YouTube comments sentiment analysis. International Journal of Scientific Research in Engineering and Management (IJSREM), 5(5), 1–11. https://ritikasingh95.github.io/Documents/Publications/YOUTUBE%20COMMENTS%20SENTIMENT%20ANALYSIS.pdf
54. Slater, A., & Tiggemann, M. (2015). Media exposure, extracurricular activities, and appearance-related comments as predictors of female adolescents’ self-objectification. Psychology of Women Quarterly, 39(3), 375–389. https://doi.org/10.1177/0361684314554606
55. Strauman, T. J., & Higgins, E. T. (1987). Automatic activation of self-discrepancies and emotional vulnerability. Journal of Personality and Social Psychology, 53(6), 1004–1014. https://doi.org/10.1037/0022-3514.53.6.1004
56. Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2(1), 325–347. https://doi.org/10.1146/annurev-linguistics-011415-040518
57. Thelwall, M. (2018). Social media analytics for YouTube comments: Potential and limitations. International Journal of Social Research Methodology, 21(3), 303–316. https://doi.org/10.1080/13645579.2017.1381821
58. Tidytext Mining. (n.d.). Sentiment analysis. Retrieved October 3, 2024, from https://www.tidytextmining.com/sentiment
59. Tiggemann, M., & McGill, B. (2004). The role of social comparison in the effect of magazine advertisements on women's mood and body dissatisfaction. Journal of Social and Clinical Psychology, 23(1), 23–44. https://doi.org/10.1521/jscp.23.1.23.26991
60. Ting-Toomey, S. (2005). The matrix of face: An updated face-negotiation theory. In W. B. Gudykunst (Ed.), Theorizing about intercultural communication (pp. 71–92). Sage.
61. Wong, Y. J., & Tsai, P. C. (2007). Cultural models of shame and guilt. In J. L. Tracy, R. W. Robins, & J. P. Tangney (Eds.), The self-conscious emotions: Theory and research (pp. 209–223). Guilford Press.