分析150万条推文，揭示科技未来讨论中的情绪与叙事模式。
Mapping Technological Futures: Anticipatory Discourse Through Text Mining
- 基于BERTopic等文本挖掘技术，从400位意见领袖推文中识别出100个科技未来话题。
- 乐观情绪如希望主导整体讨论，尤其在机器学习等技术领域，而气候与地缘政治话题引发焦虑。
- 研究揭示意见领袖如何塑造公众对科技未来的认知，适合关注科技传播与社会影响的研究者。
新兴技术（如人工智能）的高波动性和不确定性在社交媒体上引发广泛讨论。本研究分析了2021至2023年间来自400位关键意见领袖（KOLs）在X平台发布的150万条推文，采用BERTopic建模、情感、情绪与态度分析等先进文本挖掘方法，识别出100个反映科技驱动未来愿景的独立话题。研究发现，KOLs在构建‘当下未来’（如AI与物联网带来的变革愿景）和影响‘未来现实’（即这些设想塑造当前社会与地缘政治争论）中扮演双重角色。积极情绪如‘希望’占据主导，尤其在‘机器学习、数据科学与深度学习’相关话题中；而‘气候变化’和‘战争、乌克兰、特朗普人群’等议题则引发显著‘焦虑’。通过将技术视为解决社会挑战的方案，KOLs成为社会叙事的中介，连接想象中的未来与现实。这些发现凸显其在高度不确定时期引导公众注意力的关键作用，深化了对技术语境下预期性话语的理解。
原文摘要 · Abstract (English)
The volatility and unpredictability of emerging technologies, such as artificial intelligence (AI), generate significant uncertainty, which is widely discussed on social media. This study examines anticipatory discourse surrounding technological futures by analysing 1.5 million posts from 400 key opinion leaders (KOLs) published on the X platform (from 2021 to 2023). Using advanced text mining techniques, including BERTopic modelling, sentiment, emotion, and attitude analyses, the research identifies 100 distinct topics reflecting anticipated tech-driven futures. Our findings emphasize the dual role of KOLs in framing \textit{present futures} -- optimistic visions of transformative technologies like AI and IoT -- and influencing \textit{future presents}, where these projections shape contemporary societal and geopolitical debates. Positive emotions such as Hope dominate, outweighing Anxiety, particularly in topics like ``Machine Learning, Data Science, and Deep Learning,'' while discussions around ``Climate Change'' and ``War, Ukraine, and Trump People'' elicit \textit{Anxiety}. By framing technologies as solutions to societal challenges, KOLs act as mediators of societal narratives, bridging imagined futures and current realities. These insights underscore their pivotal role in directing public attention with emerging technologies during periods of heightened uncertainty, advancing our understanding of anticipatory discourse in technology-mediated contexts.
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