该研究考查了微信即时交流中人格判断的非语言线索,并运用透镜模型分析检验了这些线索在不同互动时长条件下对人格判断准确性的影响。结果表明：（1）除神经质外,大五人格其余四种特质总体的自我—他人一致性（SOA）均达到显著水平,平均SOA随互动时长增加而递增;（2）微信交流的非语言线索非常广泛,很多非语言线索在观察者的人格判断中是有效的和可用的;（3）在10min和20min组中,“字数”和“消息量”是外倾性的诊断性线索,“表情符号”和“文字重复”是宜人性与开放性的诊断性线索;（4）观察者对10min组的宜人性和外倾性线索以及20min组所有特质的线索敏感性均显著,且随互动时长增加而同步提高。
Abstract
Computer-mediated communication (CMC) refers to the cluster of interpersonal communication technologies that are based upon the transfer and storage of information among interconnected networks of computers. Common CMCs include email, instant messaging, social networking sites, etc. WeChat is one of the most common CMC forms in China. It is generally believed that CMC lacks abundant nonverbal cues, which makes it difficult to conduct effective emotional communication and to form accurate personality impression. However, numerous studies have found that there is no lack of non-verbal communication in CMC, and users have created many emotional cues to adapt to this new communication situation, such as “emoticons”, “word repetition” and so on.
Emoticons and other nonverbal cues can be used to enhance the perceived richness of CMC, and increase the accuracy of message interpretation. CMC nonverbal helps avoid the effects of ambiguity in online communication, by supporting interactors decipher intent from ambiguous messages. The purpose of this study is to explore the use of CMC nonverbal cues and whether they can affect the accuracy of personality judgment in WeChat communication situations by using the WeChat software platform.
Procedure: In the experiment, 146 participants were divided into three groups (50, 46, 50) and conducted pair-to-pair WeChat instant chat for 5min, 10min, and 20min respectively. All 146 participants evaluated each other's personality after the chat and were asked to submit screenshots of the chat transcripts via e-mail. Finally, we conducted classified statistics and lens model analysis on the non-verbal cues in chat materials, including “word count”, “message amount”, “emoticons”, “punctuation”, and “word repetition”.
The results showed that: (1) Except for neuroticism, the total self-other agreement (SOA) of the other four traits reached a significant level, and the average SOA increased with the length of interaction; (2) The frequency of non-verbal cues in WeChat communication was 55.8%; (3) In the 10min and 20min groups, “word count” and “message amount” were diagnostic cues of extroversion, while “emoticons” and “word repetition” were diagnostic cues of agreeableness and openness; (4) The sensitivity of observers to cues of agreeableness and extroversion in the 10min group and to cues of all traits in the 20min group were significant, and improved with the increase of interaction duration.
These results suggest that WeChat communication-based personality judgments have certain accuracy, and the non-verbal cues are closely related to the accuracy of personality judgments, especially extroversion, openness, and agreeableness. Non-verbal cues are common in WeChat communication. Individuals will use a lot of non-verbal information during WeChat interaction, and observers will infer the level of personality traits of the target based on this information. However, only by providing a long enough communication time(20min) can the target present effective nonverbal cues, which can be used by the observer to infer relevant traits and make accurate judgments.
关键词
微信交流 /
人格判断 /
非语言线索 /
透镜模型分析 /
自我—他人一致性
Key words
WeChat communication /
personality judgment /
nonverbal cues /
lens model analysis /
self-other agreement
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参考文献
[1] 陈少华. (2016). 人格判断的自我-他人一致性: 熟悉度和亲密度的作用. 心理学探新, 36(4), 343-348.
[2] 陈少华. (2017). 人格判断的透镜模型分析. 广州大学学报 (社会科学版), 16(8), 51-58.
[3] 王孟成, 戴晓阳, 姚树桥. (2011). 中国大五人格问卷的初步编制Ⅲ: 简式版的制定及信效度检验. 中国临床心理学杂志, 19(4), 454-457.
[4] Antheunis M. L., Schouten A. P., & Walther J. B. (2020). The hyperpersonal effect in online dating: Effects of text-based CMC vs. videoconferencing before meeting face-to-face. Media Psychology, 23(6), 820-839.
[5] Betz N., Hoemann K., & Barrett L. F. (2019). Words are a context for mental inference. Emotion, 19(8), 1463-1477.
[6] Bleske-Rechek A., Paulich K., Shafer P., & Kofman C. (2019). Grammar matters: The tainting effect of grammar usage errors on judgments of competence and character. Personality and Individual Differences, 141, 47-50.
[7] Brunswik, E. (1956). Perception and the representative design of psychological experiments. University of California Press..
[8] Chen, X., & Siu, K. W. M. (2017). Exploring user behaviour of emoticon use among Chinese youth. Behaviour and Information Technology, 36(6), 637-649.
[9] Forbes, F. J. M., & Buchanan, E. M. (2019). “Textisms”: The comfort of the recipient. Psychology of Popular Media Culture, 8(4), 358-364.
[10] Glikson E., Cheshin A., & van Kleef, G. A. (2018). The dark side of a smiley: Effects of smiling emoticons on virtual first impressions. Social Psychological and Personality Science, 9(5), 614-625.
[11] Hall J. A., Gunnery S. D., Letzring T. D., Carney D. R., & Colvin C. R. (2017). Accuracy of judging affect and accuracy of judging personality: How and when are they related? Journal of Personality, 85(5), 583-592.
[12] Hall J. A., Pennington N., & Lueders A. (2014). Impression management and formation on Facebook: A lens model approach. New Media and Society, 16(6), 958-982.
[13] Harris, R. B., & Paradice, D. (2007). An investigation of the computer-mediated communication of emotions. Journal of Applied Sciences Research, 3(12), 2081-2090.
[14] Hu Y., Zhao J. C., & Wu J. J. (2016). Emoticon-based ambivalent expression: A hidden indicator for unusual behaviors in Weibo. PLoS ONE, 11(1), 1-14.
[15] Kalman, Y. M., & Gergle, D. (2014). Letter repetitions in computer-mediated communication: A unique link between spoken and online language. Computers in Human Behavior, 34, 187-193.
[16] Krishnan, A., & Hunt, D. S. (2021). TTYL :-) nonverbal cues and perceptions of personality and homophily in synchronous mediated communication. Information, Communication and Society, 24(1), 85-101.
[17] Letzring T. D., Wells S. M., & Funder D. C. (2006). Information quantity and quality affect the realistic accuracy of personality judgment. Journal of Personality and Social Psychology, 91(1), 111-123.
[18] Li X., Chan K. W., & Kim S. (2019). Service with emoticons: How customers interpret employee use of emoticons in online service encounters. Journal of Consumer Research, 45(5), 973-987.
[19] Liu, S. Y., & Sun, R. J. (2020). To express or to end? Personality traits are associated with the reasons and patterns for using emojis and stickers. Frontiers in Psychology, 11, 1076.
[20] Marengo D., Giannotta F., & Settanni M. (2017). Assessing personality using emoji: An exploratory study. Personality and Individual Differences, 112, 74-78.
[21] Markey, P. M., & Wells, S. M. (2002). Interpersonal perception in internet chat rooms. Journal of Research in Personality, 36(2), 134-146.
[22] Moore, K., & McElroy, J. C. (2012). The influence of personality on Facebook usage, wall postings, and regret. Computers in Human Behavior, 28(1), 267-274.
[23] Novak P. K., Smailović J., Sluban B., & Mozetič I. (2015). Sentiment of emojis. PLoS ONE, 10(12), 1-22.
[24] Oleszkiewicz A., Karwowski M., Pisanski K., Sorokowski P., Sobrado B., & Sorokowska A. (2017). Who uses emoticons? Data from 86 702 Facebook users. Personality and Individual Differences, 119, 289-295.
[25] Park G., Schwartz H. A., Eichstaedt J. C., Kern M. L., Kosinski M., Stillwell D. J., & Seligman, M. E. P. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934-952.
[26] Qiu L., Lin H., Ramsay J., & Yang F. (2012). You are what you tweet: Personality expression and perception on twitter. Journal of Research in Personality, 46(6), 710-718.
[27] Riordan, M. A., & Kreuz, R. J. (2010). Cues in computer-mediated communication: A corpus analysis. Computers in Human Behavior, 26(6), 1806-1817.
[28] Rodrigues D., Lopes D., Prada M., Thompson D., & Garrido M. V. (2017). A frown emoji can be worth a thousand words: Perceptions of emoji use in text messages exchanged between romantic partners. Telematics and Informatics, 34(8), 1532-1543.
[29] Tossell C. C., Kortum P., Shepard C., Barg-Walkow L. H., Rahmati A., & Zhong L. (2012). A longitudinal study of emoticon use in text messaging from smartphones. Computers in Human Behavior, 28(2), 659-663.
[30] Troiano, G., & Nante, N. (2018). Emoji: What does the scientific literature say about them? A new way to communicate in the 21th century. Journal of Human Behavior in the Social Environment, 28(4), 528-533.
[31] Tseng, T. H., & Hsieh, S. H. (2019). Determinants of emoticon usage in mobile instant messaging: A construal level theory perspective. Behaviour and Information Technology, 38(3), 289-301.
[32] Tskhay, K. O., & Rule, N. O. (2014). Perceptions of personality in text-based media and OSN: A meta-analysis. Journal of Research in Personality, 49, 25-30.
[33] Vandergriff, I. (2013). Emotive communication online: A contextual analysis of computer-mediated communication (CMC) cues. Journal of Pragmatics, 51, 1-12.
[34] Wall H. J., Kaye L. K., & Malone S. A. (2016). An exploration of psychological factors on emoticon usage and implications for judgement accuracy. Computers in Human Behavior, 62, 70-78.
[35] Walther, J. B. (2007). Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition. Computers in Human Behavior, 23(5), 2538-2557.
[36] Walther J. B., Kashian N., Jang J. W., & Shin S. Y. (2016). Overattribution of liking in computer-mediated communication: Partners infer the results of their own influence as their partners' affection. Communication Research, 43(3), 372-390.
[37] Wu, T., & Zheng, Y. (2019). Is impression management through status updates successful? Meta-accuracy and judgment accuracy of big five personality traits based on status updates from social network sites in China. Frontiers in Psychology, 10, 1192.
基金
*本研究得到广州市哲学社会科学“十四五”规划项目（2021GZGJ186）的资助
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