不同类型社交机器人的话语研究：以双碳议题为例
Study on the Discourse of Different Types of Social Bots: A Case Study of Dual-Carbon Issues
摘要: 气候变化与人工智能的双重挑战下，社交机器人作为舆论空间的重要参与者，其传播作用存在争议。本研究围绕双碳议题，旨在探寻不同类型社交机器人在推特平台上的话语影响程度。在筛选出推特平台上的社交机器人并对其进行分类后，本研究在风险的社会放大框架下，以情感分析和主题建模为路径，析出普通机器人、新闻机器人和桥接机器人在情感表达和议程设置方面的差异，以厘清不同类型社交机器人对人类用户的话语影响。在情感分析层面，三类机器人情绪分布差异显著，其中桥接机器人表现出明显的人机同构倾向；在主题层面，三类机器人的话语特征与其功能定位深度契合，分层次共同构建双碳话题。
Abstract: Against the dual challenges of climate change and the artificial intelligence, social bots, as important participants in the public opinion space, have controversial communication roles. Focusing on the dual-carbon issue, this study aims to explore the extent of discourse influence of different types of social bots on the Twitter platform. After identifying and classifying social bots on the Twitter platform, Under the Social Amplification of Risk Framework (SARF), this study adopts sentiment analysis and topic modeling as research approaches to identify the differences in emotional expression and agenda-setting among regular bots, news bots, and bridging bots, thereby clarifying the discourse influence of different types of social bots on human users. At the level of sentiment analysis, there are significant differences in the emotional distribution of the three types of bots, among which bridging bots show an obvious tendency of human-machine isomorphism. At the topic level, the discourse characteristics of the three types of bots are highly consistent with their functional positioning, and they jointly construct the dual-carbon topic in a hierarchical manner.
文章引用：林必超. 不同类型社交机器人的话语研究：以双碳议题为例[J]. 新闻传播科学, 2025, 13(12): 1993-2001. https://doi.org/10.12677/jc.2025.1312282

参考文献

[1] Kirilenko, A.P., Molodtsova, T. and Stepchenkova, S.O. (2015) People as Sensors: Mass Media and Local Temperature Influence Climate Change Discussion on Twitter. Global Environmental Change, 30, 92-100. [Google Scholar] [CrossRef] 
[2] 张洪忠, 赵蓓, 石韦颖. 社交机器人在Twitter参与中美贸易谈判议题的行为分析[J]. 新闻界, 2020(2): 46-59.
[3] 张洪忠, 段泽宁, 韩秀. 异类还是共生: 社交媒体中的社交机器人研究路径探讨[J]. 新闻界, 2019(2): 10-17.
[4] 徐健, 欧翔, 邹禧乾. 与智能“共济”: 灾难传播中社交机器人对用户参与的影响路径探究[J]. 新闻界, 2023(12): 36-51+65.
[5] 相德宝, 倪佳律. 推特社交机器人情绪传染研究: 消极情绪加剧情绪传染[J]. 当代传播, 2024(2): 29-34.
[6] Stieglitz, S. and Linh, D.-X. (2013) Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior. Journal of Management Information Systems, 29, 217-248. [Google Scholar] [CrossRef] 
[7] Kušen, E. and Strembeck, M. (2018) Why So Emotional? An Analysis of Emotional Bot-Generated Content on Twitter. Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk, Funchal, 20-21 March 2018, 13-22. [Google Scholar] [CrossRef] 
[8] Ferrara, E. (2020) #Covid-19 on Twitter: Bots, Conspiracies, and Social Media Activism.
[9] Dunn, A.G., Surian, D., Dalmazzo, J., Rezazadegan, D., Steffens, M., Dyda, A., et al. (2020) Limited Role of Bots in Spreading Vaccine-Critical Information among Active Twitter Users in the United States: 2017-2019. American Journal of Public Health, 110, S319-S325. [Google Scholar] [CrossRef] [PubMed]
[10] Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H. and Grimme, C. (2020) Demystifying Social Bots: On the Intelligence of Automated Social Media Actors. Social Media + Society, 6, 1-14. [Google Scholar] [CrossRef] 
[11] Sayyadiharikandeh, M., Varol, O., Yang, K., Flammini, A. and Menczer, F. (2020) Detection of Novel Social Bots by Ensembles of Specialized Classifiers. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 19-23 October 2020, 2725-2732. [Google Scholar] [CrossRef] 
[12] Bessi, A. and Ferrara, E. (2018) Social Bots Distort the 2016 U.S. Presidential Election Online Discussion. ACM Transactions on Internet Technology, 18, 1-19.
[13] Davis, C.A., Varol, O., Ferrara, E., Flammini, A. and Menczer, F. (2016) BotOrNot: A System to Evaluate Social Bots. Proceedings of the 25th International Conference Companion on World Wide Web, Montréal, 11-15 April 2016, 273-274. [Google Scholar] [CrossRef] 
[14] 中国公众低碳意识与低碳行为网络调查报告[N]. 中国改革报, 2023-12-13(06).
[15] 童桐, 李涵沁, 黄思南. 如何做好“双碳”目标下的气候议题对外传播[J]. 对外传播, 2022(4): 28-32.
[16] Chu, J., Zhu, Y. and Ji, J. (2023) Characterizing the Semantic Features of Climate Change Misinformation on Chinese Social Media. Public Understanding of Science, 32, 845-859. [Google Scholar] [CrossRef] [PubMed]
[17] Kasperson, R.E., Renn, O., Slovic, P., Brown, H.S., Emel, J., Goble, R., et al. (1988) The Social Amplification of Risk: A Conceptual Framework. Risk Analysis, 8, 177-187. [Google Scholar] [CrossRef] 
[18] Yang, X., Liu, Y. and Zhang, Y. (2021) Social Bots’ Role in Climate Change Discussion on Twitter: Measuring Standpoints, Topics, and Interaction Strategies. Journal of Environmental Informatics, 38, 1-12.
[19] Liao, C., Li, Y. and Zhang, M. (2023) Exposure to Social Bots Amplifies Perceptual Biases and Regulation Propensity. Scientific Reports, 13, Article No. 20707.
[20] Alieva, I., Ng, L.H.X. and Carley, K.M. (2022) Investigating the Spread of Russian Disinformation about Biolabs in Ukraine on Twitter Using Social Network Analysis. 2022 IEEE International Conference on Big Data (Big Data), Osaka, 17-20 December 2022, 1770-1775. [Google Scholar] [CrossRef] 
[21] Ng, L.H.X. and Carley, K.M. (2024) Deflating the Chinese Balloon: Types of Twitter Bots in US-China Balloon Incident. EPJ Data Science, 12, Article No. 63.

为你推荐