The Dynamic Evolution and Structural Analysis of AIGC Topics:A Comparative Study Based on Weibo and Twitter
Artificial Intelligence-Generated Content(AIGC)marks a groundbreaking shift in information creation,driven by advances in AI technologies like Large Language Models(LLM)and Natural Language Processing(NLP).This evolution of AIGC,with its enhanced"human-like"capabilities and creativity,is reshaping the landscape of social information and communication ecosystems.However,from a constructivist perspective,varying social and cultural environments often lead to diverse technological cognitions,potentially deepening the"cognitive distance"between AIGC's technological functionalities and practical application.This gap poses two significant risks:firstly,during the stage of AIGC popularization,public perceptions of its utility remain fluid and prone to influence,potentially leading to misinterpretations of the technology and divergent impacts of technological trajectories and societal outcomes;secondly,current examinations of AIGC lack a cross-contextual,cross-platform perspective.Solely interpreting the technology within a single societal context may result in a parochial understanding,shrouded by a"cultural container",which impedes an objective assessment and grasping of the technology's universal value for humanity.Hence,deciphering the cognitive differences in AIGC perceptions across various contexts and identifying the drivers behind these variations are vital for fully releasing the positive social value of this technology.This paper conducts an in-depth analysis of AIGC discussions on Weibo and Twitter across five dimensions:topical trends,thematic modeling,theme evolution,sentiment analysis,and identification of key communicators.By incorporating quantitative analysis tools such as Dynamic Topic Modeling(DTM)and Text Impact Assessment Models,this study not only reveals the dynamic trajectory of AIGC discussions but also identifies key communicators playing pivotal roles in these discussions,offering new insights for related research.Findings indicate that on Weibo,users focus more on the economic and business values of AIGC,emphasizing discussions on the relationship between new technology and economic development,with topic evolution heavily influenced by capital,investment,and market forces.On Twitter,discussions about AIGC are more closely related to technical logic,with many cultural and industrial commentators placing greater emphasis on the technology's impact on industry,ethics,and future imaginations.The research posits that in China,AIGC discussions driven by capital and investment factors represent a situation intertwined with risks and opportunities.The opportunity lies in capital acting as a powerful social force,further accelerating the application and development of AIGC technology in China;the risk involves technology being increasingly infused with capital logic.Finally,the paper suggests that while balancing the economic value and humanistic value of AIGC,caution is needed against the"excessive"intervention of capital and the market in technology cognition and development,to prevent the technology from straying from its original intent of promoting holistic societal development.Additionally,macro-contexts are not static;future research should continue to focus on the structural changes in technology cognition and how these changes might influence the development of new technologies.
artificial intelligence-generated content(AIGC)topic constructioncommunication structuretechnological cognitionWeiboTwitterdynamic topic model(DTM)social media
万方数据
维普
