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Mapping the Technological Future: A Topic, Sentiment, and Emotion Analysis in Social Media Discourse

arXiv 2407.17522 ↗First posted 20 Jul 2024cs.AI cs.CL cs.SIcited by 0

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Abstract (arXiv)

People worldwide are currently confronted with a number of technological challenges, which act as a potent source of uncertainty. The uncertainty arising from the volatility and unpredictability of technology (such as AI) and its potential consequences is widely discussed on social media. This study uses BERTopic modelling along with sentiment and emotion analysis on 1.5 million tweets from 2021 to 2023 to identify anticipated tech-driven futures and capture the emotions communicated by 400 key opinion leaders (KOLs). Findings indicate positive sentiment significantly outweighs negative, with a prevailing dominance of positive anticipatory emotions. Specifically, the 'Hope' score is approximately 10.33% higher than the median 'Anxiety' score. KOLs emphasize 'Optimism' and benefits over 'Pessimism' and challenges. The study emphasizes the important role KOLs play in shaping future visions through anticipatory discourse and emotional tone during times of technological uncertainty.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 82 references that are in the map. First posted on arXiv 20 Jul 2024.

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References · 7

  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  2. 2022TweetNLP: Cutting-Edge Natural Language Processing for Social Mediacited by 71
  3. 2023Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmarkcited by 10
  4. 2021PyPlutchik: visualising and comparing emotion-annotated corporacited by 6
  5. 2020Making Decisions under Model Misspecificationcited by 3
  6. 2022Assessment of Massively Multilingual Sentiment Classifierscited by 3
  7. 2023What Twitter Data Tell Us about the Future?cited by 3

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