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Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination (2401.05254v4)

Published 10 Jan 2024 in cs.CY and cs.CL

Abstract: While affective expressions on social media have been extensively studied, most research has focused on the Western context. This paper explores cultural differences in affective expressions by comparing valence and arousal on Twitter/X (geolocated to the US) and Sina Weibo (in Mainland China). Using the NRC-VAD lexicon to measure valence and arousal, we identify distinct patterns of emotional expression across both platforms. Our analysis reveals a functional representation between valence and arousal, showing a negative offset in contrast to traditional lab-based findings which suggest a positive offset. Furthermore, we uncover significant cross-cultural differences in arousal, with US users displaying higher emotional intensity than Chinese users, regardless of the valence of the content. Finally, we conduct a comprehensive language analysis correlating n-grams and LDA topics with affective dimensions to deepen our understanding of how language and culture shape emotional expression. These findings contribute to a more nuanced understanding of affective communication across cultural and linguistic contexts on social media.

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References (66)
  1. Personality, gender, and age in the language of social media: The Open-Vocabulary approach. PLoS One, 8(9):e73791.
  2. Psychology as the science of self-reports and finger movements: Whatever happened to actual behavior? Perspectives on psychological science, 2(4):396–403.
  3. Yoav Benjamini and Yosef Hochberg. 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing.
  4. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022.
  5. Hamparsum Bozdogan. 1987. Model selection and akaike’s information criterion (aic): The general theory and its analytical extensions. Psychometrika, 52(3):345–370.
  6. Remembering pictures: pleasure and arousal in memory. J. Exp. Psychol. Learn. Mem. Cogn., 18(2):379–390.
  7. John T Cacioppo and Wendi L Gardner. 1999. EMOTION.
  8. The affect system has parallel and integrative processing components: Form follows function. Journal of personality and Social Psychology, 76(5):839.
  9. Kenneth Church and Patrick Hanks. 1990. Word association norms, mutual information, and lexicography. Comput. Linguist., 16(1):22–29.
  10. Sheldon Cohen and Sarah D Pressman. 2006. Positive affect and health. Curr. Dir. Psychol. Sci., 15(3):122–125.
  11. Closed- and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations.
  12. Joshua A Fishman. 1980. Bilingualism and biculturism as individual and as societal phenomena.
  13. Mary Fong. 1998. Chinese immigrants’ perceptions of semantic dimensions of direct/indirect communication in intercultural compliment interactions with north americans. Howard journal of Communication, 9(3):245–262.
  14. Nico H Frijda. 1986. The Emotions. Maison des Sciences de l’Homme.
  15. A comparative study of users’ microblogging behavior on sina weibo and twitter. In User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings 20, pages 88–101. Springer.
  16. When arousal influences ad evaluation and valence does not (and vice versa). J. Consum. Psychol., 11(1):43–55.
  17. Culture and interpersonal communication. Sage Publications, Inc.
  18. Studying cultural differences in emoji usage across the east and the west. Proceedings of the International.
  19. Understanding and measuring psychological stress using social media. In Proceedings of the international AAAI conference on web and social media, volume 13, pages 214–225.
  20. Studying cultural differences in emoji usage across the east and the west. In Proceedings of the International AAAI Conference on Web and Social Media, volume 13, pages 226–235.
  21. Modelling the influence of personality and culture on affect and enjoyment in multimedia. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE.
  22. What twitter profile and posted images reveal about depression and anxiety. In Proceedings of the international AAAI conference on web and social media, volume 13, pages 236–246.
  23. The CP-QAE-I: A video dataset for exploring the effect of personality and culture on perceived quality and affect in multimedia.
  24. The influences of valence and arousal on judgments of learning and on recall. Mem. Cognit., 45(1):121–136.
  25. Estimating geographic subjective well-being from twitter: A comparison of dictionary and data-driven language methods. Proc. Natl. Acad. Sci. U. S. A., 117(19):10165–10171.
  26. Facebook versus twitter: Differences in Self-Disclosure and trait prediction. ICWSM, 12(1).
  27. Assessing employee wellbeing in schools using a multifaceted approach: Associations with physical health, life satisfaction, and professional thriving. Psychology, 05(06):500–513.
  28. The relation between valence and arousal in subjective experience. Psychol. Bull., 139(4):917–940.
  29. The relation between valence and arousal in subjective experience varies with personality and culture. Journal of personality, 85(4):530–542.
  30. The relation between valence and arousal in subjective experience varies with personality and culture. J. Pers., 85(4):530–542.
  31. P J Lang. 1994. The varieties of emotional experience: a meditation on James-Lange theory. Psychol. Rev., 101(2):211–221.
  32. Predicting active users’ personality based on Micro-Blogging behaviors. PLoS One, 9(1):e84997.
  33. Exploring (dis-) similarities in emoji-emotion association on twitter and weibo. In Companion proceedings of the 2019 world wide web conference, pages 461–467.
  34. Studying politeness across cultures using english twitter and mandarin weibo. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2):1–15.
  35. Zhongguo Li and Maosong Sun. 2009. Punctuation as implicit annotations for chinese word segmentation. Comput. Linguist., 35(4):505–512.
  36. Nangyeon Lim. 2016. Cultural differences in emotion: differences in emotional arousal level between the east and the west. Integrative medicine research, 5(2):105–109.
  37. Emotional experience and interaction design. Affect and emotion in human-computer interaction: From theory to applications, pages 116–129.
  38. What does social media say about your stress? undefined.
  39. Exploring extreme events on social media: A comparison of user reposting/retweeting behaviors on twitter and weibo.
  40. Marco Lui and Timothy Baldwin. 2012. langid.py: An off-the-shelf language identification tool. In Proceedings of the ACL 2012 System Demonstrations, pages 25–30.
  41. Lin Ma. 2013. Electronic word-of-mouth on microblogs: A cross-cultural content analysis of twitter and weibo. Intercultural Communication Studies, 22(3).
  42. Hazel R Markus and Shinobu Kitayama. 1991. Cultural variation in the self-concept. In The self: Interdisciplinary approaches, pages 18–48. Springer.
  43. David Matsumoto. 1990. Cultural similarities and differences in display rules. Motivation and emotion, 14:195–214.
  44. Evaluating the predictability of medical conditions from social media posts. PLoS One, 14(6):e0215476.
  45. Saif Mohammad. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Stroudsburg, PA, USA. Association for Computational Linguistics.
  46. James W Neuliep. 2012. The relationship among intercultural communication apprehension, ethnocentrism, uncertainty reduction, and communication satisfaction during initial intercultural interaction: An extension of anxiety and uncertainty management (aum) theory. Journal of Intercultural Communication Research, 41(1):1–16.
  47. The current status of research on the structure of evaluative space. Biological psychology, 84(3):422–436.
  48. The Cognitive Structure of Emotions. Cambridge University Press.
  49. Modelling valence and arousal in facebook posts.
  50. Our twitter profiles, our selves: Predicting personality with twitter.
  51. James A Russell. 1980. A circumplex model of affect.
  52. Huntsville, hospitals, and hockey teams: Names can reveal your location.
  53. Developing age and gender predictive lexica over social media.
  54. Personality, gender, and age in the language of social media: The open-vocabulary approach. PLoS One, 8(9):e73791.
  55. Gideon Schwarz. 1978. Estimating the dimension of a model. aos, 6(2):461–464.
  56. Shula Sommers. 1984. Reported emotions and conventions of emotionality among college students. Journal of Personality and Social Psychology, 46(1):207.
  57. Effects of recall bias and nonresponse bias on Self-Report estimates of angling participation. N. Am. J. Fish. Manage., 13(2):217–222.
  58. Characterizing depression issues on sina weibo. Int. J. Environ. Res. Public Health, 15(4).
  59. Jeanne L Tsai. 2007. Ideal affect: Cultural causes and behavioral consequences. Perspectives on Psychological Science, 2(3):242–259.
  60. Cultural variation in affect valuation. J. Pers. Soc. Psychol., 90(2):288–307.
  61. Eugene Winograd and Ulric Neisser. 2006. Affect and Accuracy in Recall: Studies of ’Flashbulb’ Memories. Cambridge University Press.
  62. Positive affect promotes well-being and alleviates depression: The mediating effect of attentional bias. Psychiatry Res., 228(3):482–487.
  63. Structure of self-reported current affect: Integration and beyond. J. Pers. Soc. Psychol., 77(3):600–619.
  64. Predicting author age from weibo microblog posts. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 2990–2997.
  65. You are where you go.
  66. Measuring individual video QoE.

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