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The Korean Society for Journalism & Communication Studies - Vol. 64 , No. 6
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Korean Journal of Journalism & Communication Studies - Vol. 64, No. 6, pp. 77-123
Abbreviation: KSJCS
ISSN: 2586-7369 (Online)
Print publication date 31 Dec 2020
Received 09 Oct 2020 Revised 30 Nov 2020 Accepted 02 Dec 2020
https://doi.org/10.20879/kjjcs.2020.64.6.003

소셜미디어 상에서의 언어 규범이 공유행위에 미치는 영향 : 남북정상회담 트윗에 대한 다층 허들모형 분석
주호준** ; 최수진***
경희대학교 언론정보학과 석사수료 (joo4456@khu.ac.kr)
경희대학교 언론정보학과 부교수 (sujinchoi@khu.ac.kr)

The Effect of Language Norms on Sharing Behavior in Social Media: Analysis of Multilevel Hurdle Model on Tweets of Inter-Korean Summit
Ho Jun Joo** ; Sujin Choi***
Master Candidate, The Graduate School of Journalism & Communication, Kyung Hee University (joo4456@khu.ac.kr)
Associate Professor, Department of Journalism & Communication, Kyung Hee University, corresponding author (sujinchoi@khu.ac.kr)
초록

공유행위는 일종의 커뮤니케이션 행위이며, 정보원의 사회적 영향력 및 여론 발생과 밀접하게 관련 있다. 본 연구는 트위터의 메시지가 어떻게 공유되고 확산하는지를 이용자의 공유행위를 중심으로 살펴보았다. 구체적으로, 언어 규범이 이용자의 공유행위에 영향을 미치는지 살펴보고자 언어학적 강도 변인과 힘없는 언어 변인을 도입하여 메시지의 공유된 횟수를 분석하였다. 또한, 선행연구 및 정보원과 이용자의 팔로워 관계 여부를 토대로 공유행위를 공유 여부와 공유 빈도로 구분하여 이들의 차이를 분석하였다. 이를 위해 제1차 남북정상회담 동안 작성되거나 공유된 트위터 메시지를 수집하였으며, 적극적으로 공론장에 참여한 정보원과 그의 메시지를 분석하였다. 그리고 분석자료의 다층적 특성과 0과잉 현상을 고려하여 다층 허들모형을 분석모형으로 선정하였다. 분석 결과, 공유 여부와 공유 빈도는 서로 다른 기제가 있음을 확인하였다. 언어학적 강도는 공유 빈도에 부정적으로 작용하였으며, 힘없는 언어는 정보원 수준 변인과 상호작용하여 공유 빈도에 긍정적인 영향을 미쳤다. 본 연구는 언어 규범이 이용자의 공유행위에 영향을 미치는 요인임을 발견하였으며 이를 언어기대이론(Language Expectancy Theory)을 통해 논의하였다.

Abstract

Sharing in social media is an act of communication, reflecting the social influence of the sources being shared and contributing to the formation of public opinion. This study explored the underlying mechanism of sharing on Twitter, focusing on the characteristics of languages used in Tweets. Linguistic characteristics have been rarely examined in the previous research as a potential factor that may influence sharing. According to the Language Expectancy Theory (LET), based on socio-cultural standards, people have certain expectations for norms of languages used by sources and the violation of these expectations affects the sources’ credibility, which in turn can exert significant influence on sharing. Thus, we investigated the effects of linguistic intensity and linguistic powerlessness and, their interactions with the source-level variables on users’ sharing behavior. Furthermore, we conceptually and analytically distinguished between the possibility (whether or not to be shared) and the frequency (to what extent shared) of a Tweet being shared. For these analyses, Tweets posted or shared during the first inter-Korean summit were collected, and among them, Twitter accounts that actively participated in the discussions of the summit and their Tweets were included in our multilevel hurdle model (which considers both the multilevel and the zero-inflation characteristics of the data and allows us to analyze binomial (possibility of being shared) and zero-truncated negative binomial (frequency of being shared) models together. Our findings suggest that the mechanisms between the possibility and the frequency of being shared are different. The former was significantly affected by the message-level variables only, whereas the latter was significantly influenced by both the source-level and message-level variables. This indicates that users mainly considered message content when they decided to share a Tweet that had never been shared previously; but considered not only the content of messages but also who the sources were when they decided to share a Tweet that had been shared already. We also found that linguistic variables were statistically significant predictors of the frequency of being shared. Linguistic intensity had a negative effect on the frequency of being shared, and the interaction between linguistic powerlessness and verified account (a proxy measure for source credibility) had a positive effect on the frequency of being shared. Considering the context that the data used in this study was collected from Twitter and addressed the first inter-Korea summit, Tweets that convey linguistically intense expressions might have prevented users from sharing them. However, when Twitter accounts were regarded as having high source credibility (being verified by Twitter), linguistically powerless language appearing in their Tweets might have caused a positive expectancy violation and encouraged users’ sharing. This indicates that, contrary to previous findings, powerless language might have been perceived as positive by users, showing consistency with language norms identified in the Tweets frequently shared about the inter-Korean summit. Overall, these findings suggest that language norms are important factors influencing users’ sharing behavior and have implications with the LET.

KeywordsSharing behavior, Inter-Korean summit, Multilevel hurdle model, Language expectancy theory, Linguistic powerlessness
키워드: 공유행위, 남북정상회담, 다층 허들모형, 언어기대이론, 힘없는 언어
Acknowledgments

본 연구의 초고에 많은 조언을 해주신 연세대학교 언론홍보영상학부 백영민 교수님께 감사드립니다.

References
1. Anger, I. & Kittl, C. (2011). Measuring influence on Twitter. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, (pp. 31-34), NY, USA: ACM,
2. Appelman, A., & Sundar, S. S. (2015). Measuring message credibility. Journalism & Mass Communication Quarterly, 93(1), 59-79.
3. Areni, C. S., & Sparks, J. R. (2005). Language power and persuasion. Psychology and Marketing, 6, 507-525.
4. Aune, R. K., & T. Kikuchi. (1993). Effects of language intensity similarity on perceptions of credibility relational attributions, and persuasion. Journal of Language and Social Psychology, 12(3), 224-238.
5. Bhattacharya, S., Srinivasan, P., & Polgreen, P. (2014). Engagement with health agencies on Twitter. PLoS One, 9(11).
6. Blankenship, K., & Holtgraves, T. (2005). The role of different markers of linguistic powerlessness in persuasion. Journal of Language and Social Psychology, 24, 3-24
7. Boehmer, J., & Tandoc Jr., E. C. (2015). Why we retweet: Factors influencing intentions to share sport news on Twitter. International Journal of Sport Communication, 8(2), 212-232.
8. Bowers, J. W. (1963). Language intensity, social introversion, and attitude change. Speech Monographs, 30, 345-352.
9. boyd, d., Golder, S., & Lotan, G., (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In Proceedings of Annual Hawaii International Conference on System Sciences.
10. Bradac, J. J., & Mulac, A. (1984). A molecular view of powerful and powerless speech styles. Communication Monographs, 51, 307-319.
11. Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Machler, M., & Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zeroinflated generalized linear mixed modeling. The R Journal, 9(2), 378-400.
12. Buller, D. B., Burgoon, M., Hall, J. R., Levine, N., Taylor, A. M., Beach, B. H., Melcher, C., Buller, M. K., Bowen, S. L., Hunsaker, F. G., & Bergen, A. (2000). Using language intensity to increase the success of a family intervention to protect children from ultraviolet radiation: Predictions from language expectancy theory. Preventive Medicine, 30, 103-113.
13. Burgoon, M., (1995). Language expectancy theory: elaboration, explication, and extension. In C. R. Berger & M. Burgoon (Eds.), Communication and Social Influence Processes (pp. 29-51). Michigan State University Press.
14. Burgoon, M., Denning, V. P., & Roberts, L. (2002). Language expectancy theory. In J. P. Dillard & M. Pfau (Eds.), The persuasion book: Developments in theory and practice (pp. 117-136). Thousand Oaks, CA: Sage
15. Burrell, N. A., & Koper, R. J. (1998). The efficacy of powerful/powerless language on attitudes and source credibility. In M. Allen & R. W. Preiss (Eds.), Persuasion: Advances through meta-analysis (pp. 203-215). Cresskill, NJ: Hampton Press
16. Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. P. (2010), Measuring user influence in Twitter: The million follower fallacy. In Proceeding of 4th International AAAI Conference on Weblogs and Social Media.
17. Choi, S. (2014). Flow, diversity, form, and influence of political talk in social-media-based public forums. Human Communication Research, 40(2), 209-237.
18. Choi, S. (2017). Dynamics of political communication in online public forums: Integrative complexity, emotional expression, and public support. Korean Journal of Journalism & Communication Studies, 61(1), 78-108.
19. Chun, H. S. & Lyi, D. R. (2006). How affects language power on persuasion and argument strength in print advertisement? The Korean Journal of Advertising and Public Relations, 8(1), 7-27.
20. Chung, M. (2017). Not just numbers: The role of social media metrics in online news evaluations. Computers in Human Behavior, 75, 949-957.
21. Clementson, D. E., Pascual-Ferrá, P., & Beatty, M. J. (2016a). How language can influence political marketing strategy and a candidate's image: Effect of presidential candidates' language intensity and experience on college students' ratings of source credibility, Journal of Political Marketing, 15(4), 388-415.
22. Clementson, D. E., Pascual-Ferrá, P., & Beatty, M. J. (2016b). When does a presidential candidate seem presidential and trustworthy? Campaign messages through the lens of language expectancy theory. Presidential Studies Quarterly, 46(3), 592-617.
23. Do, S. K. & Jin, S. S. (2018). A study on the aspects of using Korean hedging expressions from newspaper text. Bilingual Research, 70, 111-136.
24. Flanagin, A. J. & Metzger, M. J. (2007). The role of site features, user attributes, and information verification behaviors on the perceived credibility of web-based information. New Media & Society, 9(2), 319-342.
25. Fogg, B. J., Soohoo, C., Danielson, D. R., Marable, L., Stanford, J., & Trauber, E. R. (2003). How do users evaluate the credibility of Web sites? A study with over 2,500 participants. In Proceedings of the 2003 Conference on Designing for User Experiences, San Francisco, CA.
26. Gibbons, P., Bush, J., & Bradac, J. (1991). Powerful versus powerless language: Consequences for persuasion, impression formation, and cognitive response. Journal of Language and Social Psychology, 10, 115-133.
27. Go, E., Jung, E. H., & Wu, M. (2014). The effects of source cues on online news perception. Computers in Human Behavior 38, 358-367.
28. Hamilton, M. A., & Hunter, J. E. (1998). The effect of language intensity on receiver evaluations of message, source, and topic. In M. Allen & R. W. Preiss (Eds.), Persuasion: Advances through meta-analysis, (pp. 99-138). Cresskill, NJ: Hampton Press.
29. Hamilton, M. A., & Stewart, B. L. (1993). Extending an information processing model of language intensity effects. Communication Quarterly, 41, 231-246.
30. Hamilton, M. A., Hunter, J. E., & Burgoon, M. (1990). An empirical test of an axiomatic model of the relationship between language intensity and persuasion. Journal of Language and Social Psychology, 9, 235-255.
31. Holtgraves, T., & Lasky, B. (1999). Linguistic power and persuasion. Journal of Language and Social Psychology, 18, 196-205.
32. Hong, C., & Li, C. (2017). The effect of “anonymous reviewer”: A study of anonymity, affect intensity, and message valence in the cyberspace. Journal of Language and Social Psychology, 36(5), 504-524.
33. Hosman, L. A. (1989). The evaluative consequences of hedges, hesitations, and intensifiers: Powerful and powerless speech styles. Human Communication Research, 15, 383-406.
34. Hosman, L. A., Huebner, T. M., & Siltanen, S. A. (2002). The impact of power-of-speech style, argument strength, and need for cognition on impression formation, cognitive responses, and persuasion. Journal of Language and Social Psychology, 21(4), 361-379.
35. Hur, K., Kim, T. T., Karatepe, O. M., & Lee, G. (2017). An exploration of the factors influencing social media continuance usage and information sharing intentions among Korean Travellers. Tourism Management, 63, 170-178.
36. Hwang, H. & Rhee, J. W. (2014). Who is leading on tweeter? : The effects of communicator and message properties on opinion leadership. Korean Journal of Journalism & Communication Studies, 58(5), 5-35.
37. Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251.
38. Kim, E., Hou, J., Han, J. Y., & Himelboim, I. (2016). Predicting retweeting behavior on breast cancer social networks: Network and content characteristics. Journal of Health Communication, 21(4), 479-486.
39. Kim, M., Jang, H.-Y., Jo, Y.-M., & Shin, H.(2013). KOSAC: Korean Sentiment Analysis Corpus. In Proceedings of the Korean Information Science Society Conference, 650-652.
40. Kim, S.-S. & Bae, J.-A. (2014). A study on classification of SNS communicators: Focused on the comparison of Facebook and Twitter. Journal of Cybercommunication Academic Society, 31(4), 97-139.
41. Kwak, H., Lee, C., Park, H., & Moon, S. (2010, 4). What is Twitter, a social network or a news media? In Proceedings of the 19th International World Wide Web Conference, (pp. 591-600), Raleigh, NC.
42. Lee, C.-H. & Jung, N.-W. (2014). The impact of social media use and motive on social capital: Comparison of open/closed social media. Korean Journal of Communication & Information, 5-26.
43. Lee, H. & Chung, D. (2013). College Twitter users’ source credibility, attitude, relationship intention, and tweet credibility on Twitter account owner. Journal of Cybercommunication Academic Society, 30(2), 265-308.
44. Lee, J. H. & Chan, K. K. (2012). Analysis of the information diffusion process on Twitter: Effects of influentials and hyperlinks. Korean Journal of Journalism & Communication Studies, 56(3), 238-265.
45. Lee, M. J. & Kang, H. A. (2018). Korean expressions of mitigation in product reviews. Hanminjok Emunhak, 82, 63-90.
46. Lee, W.-T., Cha, M. Y., & Yang, H. R. (2011). Network properties of social media influentials: Focusing on the Korean Twitter community. Journal of Communication Research, 48(2), 44-79.
47. Liebrecht, C. (2015). Intens krachtig. Stilistische intensiveerders in evaluatieve teksten [Intensely powerful. Language intensity in evaluative texts] (Unpublished doctoral dissertation). Radboud University Nijmegen, Netherlands.
48. Liu, Z., Liu, L., & Li, H. (2012). Determinants of information retweeting in microblogging. Internet Research, 22(4), 443-466.
49. Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413-439.
50. Metzger, M. J., Flanagin, A. J., Eyal, K., Lemus, D. R., & McCann, R. M. (2003). Credibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment. In P. J. Kalbfleisch (Ed.), Communication Yearbook (Vol. 27, pp. 293-335). Mahwah, NJ: Erlbaum.
51. Morris, M. R., Counts, S., Roseway, A., Hoff, A., & Schwarz, J. (2012). Tweeting is believing? Understanding microblog credibility perceptions. In Proceedings from the conference on computer-supported cooperative work and social computing,. Seattle, WA.
52. Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A. C. (2011). Bad news travel fast: a content-based analysis of interestingness on Twitter. In Proceedings of the 3rd international web science conference, (pp. 1-7), New York: ACM.
53. O'Barr, W. M. (1982). Linguistic evidence: Language power and strategy in the courtroom. New York: Academic Press.
54. Park, E. L. & Cho, S. (2014). KoNLPy: Korean natural language processing in python. In Proceedings of the 26th Annual Conference on Human and Cognitive Language Technology, 133-136.
55. Park, J.-R. (2008). Linguistic politeness and face-work in computer mediated communication, Part 1: A theoretical framework. Journal of the American Society for Information Science and Technology, 59(13), 2051-2059.
56. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models, (2nd ed.). Thousand Oaks, CA: Sage.
57. Yu, J. & Keum, H. (2018). Twitter as a risk information source : The effect of update recency and social endorsement on credibility judgments. Korean Journal of Broadcasting and Telecommunication Studies, 32(1), 33-65.
58. Shin, J. H. & Kim, Y. H. (2018). 2018 Korea media panel survey (Policy Data 18-10-02). Chungcheong: Korean Information Society Development Institute.
59. Shin, M. S. (2006). A study on hedge in Korean academic texts. Korean Language, 38, 151-180.
60. Shin, Y. J. (2011). A comparison of hedge usage by native-speaking Koreans and Chinese learners of Korean in introduction sections of Master's theses. Discourse and Cognition, 18(1), 63-77.
61. Smith, V., Siltanen, S. A., & Hosman, L. A. (1998). The effects of powerful and powerless speech styles and speaker expertise on impression formation and attitude change. Communication Research Reports, 15(1), 27-35.
62. Stieglitz, S., & Dang-Xuan, L. (2012). Political communication and influence through microblogging-An empirical analysis of sentiment in Twitter messages and retweet behavior. In 45th Hawaii International Conference on System Sciences, (pp. 3500-3509).
63. Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media-Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217-248.
64. Suh, B., Hong, L., & Pirolli, P., (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. Proceedings of the IEEE Second International Conference on Social Computing, Minneapolis, USA, 177-184.
65. Tinati, R., Carr, L., Hall, W., & Bentwood, J. (2012). Identifying communicator roles in Twitter. In WWW 2012 Companion Proceedings of the 21st international conference companion on World Wide Web, (pp. 1161-1168). New York, NY: ACM.
66. Vatcheva, K. P., Lee, M., McCormick, J. B., & Rahbar, M. H. (2016). Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology, 6(2).
67. Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York: Springer.