
Exploring the Relationship Between Online Public Opinion, User Discourse, and Social Bot Production and Information Dissemination
- 1 The University of Melbourne
* Author to whom correspondence should be addressed.
Abstract
The advent of the digital age and the subsequent rise of the Internet have greatly impacted the relationship between online opinion and user discourse in the early evolution of traditional media. However, it has introduced new challenges, namely the emergence of social bots that generate and disseminate information that aims to influence public opinion. Thus, the paper attempts to decipher whether online public opinion, user discourse, and the positions of social robots in the production and dissemination of information will reinforce or change each other. Through the adoption of questionnaire survey method, 414 Internet users who understand and have contacted the human-computer account on Douyin platform as the survey object, online questionnaire survey, it screens out 414 effective questionnaires for data analysis. Besides, the samples are analyzed for reliability, correlation, exploratory factor analysis (validity analysis), linear regression analysis (mediated effects analysis), and analysis of variance using the SPSS (Statistical Product Service Solutions) tool, and the data are obtained. At the same time, combined with text analysis in qualitative research, semiotic analysis is mainly used to analyze how sample data conveys the relationship between online public opinion, user discourse, and social robots. On this basis, the results indicate that all five hypotheses presented in the paper are verified, and two new relationships are explored.
Keywords
Online Public Opinion, User Discourse, Social Bot Production and Information Dissemination, Douyin, Spiral of Silence Theory
[1]. Habermas, J. (2019) The Public Sphere: An Encyclopedia Article (1974). In Routledge eBooks, 11–19.
[2]. Vaske, J.J. (2011) Advantages and Disadvantages of internet Surveys: Introduction to the special issue. Human Dimensions of Wildlife, 16(3): 149-153.
[3]. Garrod, J. (2016) The real world of the decentralized autonomous society. tripleC Communication Capitalism & Critique Open Access Journal for a Global Sustainable Information Society, 14(1).
[4]. Scheufle, D.A., and Moy, P. (2000). TWENTY-FIVE YEARS OF THE SPIRAL OF SILENCE: a CONCEPTUAL REVIEW AND EMPIRICAL OUTLOOK. International Journal of Public Opinion Research, 12(1): 3-28.
[5]. Noelle-Neumann, E. (1974) The spiral of silence a theory of public opinion. Journal of Communication, 24(2): 43-51.
[6]. Noelle-Neumann, E. (1993). The spiral of silence: Public Opinion--Our Social Skin. University of Chicago Press.
[7]. Matthes, J. (2014) Observing the “Spiral” in the spiral of silence. International Journal of Public Opinion Research, 27(2), 155-176.
[8]. Stoycheff, E. (2016). Under surveillance. Journalism & Mass Communication Quarterly, 93(2), 296-311.
[9]. Preiss, R.W. (2007) Mass Media Effects research: Advances Through Meta-analysis. Psychology Press.
[10]. Hakobyan, A. (2020) Digitalization of Communication and the Spiral of Silence Theory. Wisdom, 14(1), 19-30.
[11]. Harcup, T. and O’Neill, D. (2016) What is News? Journalism Studies, 18(12): 1470-1488.
[12]. Michela, D. V., Gaito, S., Quattrociocchi, W., Zignani, M., & Zollo, F. (2017, February 20). Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum.
[13]. Wright, S. (2021) Special issue: Discourses of Fake news: $hedited by Scott Wright.
[14]. Chen, L., Chen, J. and Xia, C. (2022). Social network behavior and public opinion manipulation. Journal of Information Security and Applications, 64, 103060.
[15]. Wischnewski, M., Ngo, T., Bernemann, R., Jansen, M. and Krämer, N. (2022). “I agree with you, bot!” How users (dis)engage with social bots on Twitter. New Media & Society, 26(3): 1505-1526.
[16]. Bakardjieva, M. (2015) Rationalizing Sociality: an unfinished script for Socialbots. The Information Society, 31(3), 244-256.
[17]. Ibrahim, Y. and Safieddine, F. (2020). Fake news in an era of social media: Tracking Viral Contagion. Rowman & Littlefield.
[18]. Breazeal, C. (2008) Persuasive robotics : how robots change our minds.
[19]. Bazarova, N. N., & Choi, Y. H. (2014). Self-Disclosure in social media: Extending the functional approach to disclosure motivations and characteristics on social network sites. Journal of Communication, 64(4), 635-657.
[20]. Schlosser, A.E. (2020). Self-disclosure versus self-presentation on social media. Current Opinion in Psychology, 31: 1-6.
[21]. Wang, M., Zuo, W. and Wang, Y. (2016). An improved density peaks-based clustering method for social circle discovery in social networks. Neurocomputing, 179: 219-227.
[22]. McGregor, S.C. (2019). Social media as public opinion: How journalists use social media to represent public opinion. Journalism, 20(8), 1070-1086.
[23]. Pang, H. and Zhang, K. (2024). Determining the influence of service quality on user identification, belongingness, and satisfaction on mobile social media: Insight from an emotional attachment perspective. Journal of Retailing and Consumer Services, 77: 103688.
[24]. Christensen, H.D. (2010) Roland Barthes: On semiology and taxonomy. Critical Theory for Library and Information Science, 15.
[25]. Lavrakas, P.J. (2008) Encyclopedia of Survey Research Methods. SAGE Publications.
[26]. Addington-Hall, J.M. (2007). Survey research: methods of data collection, questionnaire design and piloting. In Oxford University Press eBooks, 61-82.
[27]. Hoonakker, P. and Carayon, P. (2009). Questionnaire survey nonresponse: A comparison of postal mail and internet surveys. International Journal of Human-Computer Interaction, 25(5), 348-373.
[28]. Kuckartz, U. (2014) Qualitative text analysis: A guide to methods, practice and using software.
[29]. Ross, B., et al. (2019). Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks. European Journal of Information Systems, 28(4): 394–412.
[30]. Yang, A., et al. (2021). The influence of interdependence in Networked Publics spheres: How Community-Level Interactions affect the evolution of topics in online discourse. Journal of Computer-Mediated Communication, 26(3), 148-166.
Cite this article
Li,J. (2024). Exploring the Relationship Between Online Public Opinion, User Discourse, and Social Bot Production and Information Dissemination. Communications in Humanities Research,61,20-30.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 4th International Conference on Literature, Language, and Culture Development
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).