从公共传播到智能传播:新闻实践及实务教学的范式变革
From Public Communication to Intelligent Communication:Paradigm Shifts in Journalism Practice and Pedagogy
张志安 1李欣颖 1贺涵甫2
作者信息
- 1. 复旦大学新闻学院,上海 200433
- 2. 同济大学设计与创意学院,上海 200092
- 折叠
摘要
平台算法机制全面介入新闻生产和商业运作,生成式人工智能在传媒领域的加速应用,使新闻实践及新闻实务教学经历了两次范式变革:第一次变革,由新闻传播转向公共传播,新闻传播的主体去中心化和再中心化,多元行动者涌入互联网平台从事信息采集和分发;第二次变革,由公共传播转向智能传播,技术革命造就的人机共生、人机协作时代已经来临,PGC、UGC和AIGC等协同内容生产的格局初步形成.为此,可从三个方面回应挑战:多元主体协同,坚守信息把关者角色;凸显人类价值,实现人机互补的价值匹配;立足新闻业根基,强化媒体公共传播价值.
Abstract
Deep learning algorithm hold the potential to disrupt the media ecosystem.Generative artificial intelligence,exemplified by ChatGPT,has formed new interactive relationships with users,while Internet platforms are adept at competing for user attention with superior computational capabilities.Platform algorithm mechanism has permeated news production and business operation,and the accelerated application of generative AI in the journalism industry has led to two paradigm shifts in journalism practice and teaching:The first shift is from journalism and communication to public communication,where the subjects of news dissemination have undergone decentralization and re-centralization,and diverse actors have entered Internet platforms to engage in information gathering and distribution.The second shift is from public communication to intelligent communication,as the technological revolution has ushered in an era of human-machine symbiosis and collaboration.Collaborative content production models,including PGC,UGC,and AIGC,have begun to take shape.To address these challenges,three aspects can be considered:(1)Collaborative multi-stakeholder engagement,upholding the role of information gatekeepers;(2)Highlighting human values and achieving a complementary value matching between humans and machines;(3)Anchoring in the foundations of the journalism,strengthening the public communication value of media.
关键词
新闻实务教学/公共传播/智能传播/数字新闻Key words
journalism practice teaching/public communication/intelligent communication/digital journalism引用本文复制引用
基金项目
国家社会科学基金重大项目(23&ZD213)
出版年
2024
NETL
NSTL
万方数据