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Mapping Technological Futures: Anticipatory Discourse Through Text Mining

arXiv 2504.02853 ↗First posted 25 Mar 2025cs.SI cs.CL cs.CYcited by 0

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Abstract (arXiv)

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 textitpresent futures -- optimistic visions of transformative technologies like AI and IoT -- and influencing textitfuture 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 textitAnxiety. 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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References · 8

  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  2. 2022TweetNLP: Cutting-Edge Natural Language Processing for Social Mediacited by 71
  3. 2023Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usagecited by 14
  4. 2023Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmarkcited by 10
  5. 2021PyPlutchik: visualising and comparing emotion-annotated corporacited by 6
  6. 2022Assessment of Massively Multilingual Sentiment Classifierscited by 3
  7. 2023What Twitter Data Tell Us about the Future?cited by 3
  8. 2018Analyzing Self-Driving Cars on Twittercited by 1

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