Mapping technological futures: anticipatory discourse through text mining

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 (2021–2023). 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 emphasise the dual role of KOLs in framing present futures-optimistic visions of transformative technologies like AI and IoT-and influencing 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 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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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2025-06-21
DOI
https://doi.org/10.1057/s41599-025-05083-5
Citations
1
Primary Topic
Climate Change Communication and Perception
Type
article
Field-Weighted Citation Impact
4.20

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article

Mapping technological futures: anticipatory discourse through text mining

Maciej Skórski, Alina Landowska, Krzysztof Rajda
1 citations
Humanities and Social Sciences Communications
Climate Change Communication and Perception
4.20
article

Mapping technological futures: anticipatory discourse through text mining

Maciej Skórski, Alina Landowska, Krzysztof Rajda
article en
1 citations

Abstract

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 (2021–2023). 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 emphasise the dual role of KOLs in framing present futures-optimistic visions of transformative technologies like AI and IoT-and influencing 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 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.

Humanities and Social Sciences CommunicationsVol. 12(1)
Brandon Regional Health Authority (CA), Uniwersytet SWPS (PL), Czech Technical University in Prague (CZ), Brandon University (CA)
České Vysoké Učení Technické v Praze
Openalex Percentile: Top 7%
Climate Change Communication and Perception
4.20
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Mapping technological futures: anticipatory discourse through text mining — Maciej Skórski, Alina Landowska, et al. · Humanities and Social Sciences Communications (2025) | TGRS Research Map | TGRS