摘要
气候政策体系中的一个重要环节是加强面向公众的气候变化传播。在传播中使用并创新框架策略能达到影响公众认知与行为,提升传播效果的目的。但现有气候变化传播框架策略研究在媒介选择和方法运用上存在不足,难以支撑框架策略在形态丰富性和识别准确性上的创新。从视频媒介YouTube上提取234个气候变化纪录片的字幕构建了130万单词量的语料库,并基于无监督机器学习的主题模型网络分析法识别其中的框架策略。结果发现,气候变化视频传播语料中存在“环境威胁框架”“人类威胁框架”和“危机可控框架”3种策略。前两者分别强调气候变化对环境和人类生存的威胁;后者则强调气候变化所造成负面后果可通过科学、技术及多主体共同行动来有效控制。这是已有文献中未曾发现的复杂框架策略。研究发现对推进中国气候变化传播实践具有重要启示。
Climate change communication to the public is an important component of the"dual-carbon"policy system.The use of framing strategies in communication can affect public perception and behavior,thereby improving the effectiveness of communication.However,the existing research on the framing strategies in climate change communication is insufficient in the use of media and method to support innovation and optimization of strategies.This paper extracts subscripts of 234 climate change documentaries from the video media YouTube to construct a corpus of 1.3 million words,which is analyzed by an unsupervised machine learning approach-analysis of topic model networks-to identify strategies.We find three strategies in climate change communication videos:environmental threat frame,human threat frame,and controllable frame.The first two emphasize the threat of climate change to the environment and human beings,respectively.The latter emphasizes that the negative impacts of climate change can be controlled through technology and multi-actor participation,and is a strategy not being identified in the existing literature.This paper has important implications for promoting the practice of climate communication in China.
作者
胡赛全
陈娅静
朱俊明
HU Saiquan;CHEN Yajing;ZHU Junming(School of Business,Hunan University,Changsha 410082,China;School of Public Policy&Management,Tsinghua University,Beijing 100084,China)
出处
《中国软科学》
CSSCI
CSCD
北大核心
2023年第3期63-73,共11页
China Soft Science
基金
国家自然科学基金青年项目“启发式决策视角下的公众环境知行差距研究”(751202001458)
湖南省自然科学基金面上项目“公众对助推型减碳政策的偏好研究”(2022JJ30174)
国家社会科学基金重大项目“自然资源高效利用与经济安全和高质量发展机制研究”(21&ZD104)。
关键词
气候变化传播
框架策略
主题模型网络分析
无监督机器学习
LDA主题模型
climate change communication
framing strategy
analysis of topic model networks
unsupervised machine learning
LDA topic model
