What Twitter Data Tell Us about the Future?

• URL: http://arxiv.org/abs/2308.02035v1
• Date: Thu, 20 Jul 2023 14:02:47 GMT
• Title: What Twitter Data Tell Us about the Future?
• Authors: Alina Landowska, Marek Robak, Maciej Skorski
• Abstract summary: This study aims to investigate the futures projected by futurists on Twitter and explore the impact of language cues on anticipatory thinking. We present a compiled dataset of over 1 million publicly shared tweets by future influencers and develop a scalable NLP pipeline using SOTA models.
• Score: 1.7205106391379026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anticipation is a fundamental human cognitive ability that involves thinking about and living towards the future. While language markers reflect anticipatory thinking, research on anticipation from the perspective of natural language processing is limited. This study aims to investigate the futures projected by futurists on Twitter and explore the impact of language cues on anticipatory thinking among social media users. We address the research questions of what futures Twitter's futurists anticipate and share, and how these anticipated futures can be modeled from social data. To investigate this, we review related works on anticipation, discuss the influence of language markers and prestigious individuals on anticipatory thinking, and present a taxonomy system categorizing futures into "present futures" and "future present". This research presents a compiled dataset of over 1 million publicly shared tweets by future influencers and develops a scalable NLP pipeline using SOTA models. The study identifies 15 topics from the LDA approach and 100 distinct topics from the BERTopic approach within the futurists' tweets. These findings contribute to the research on topic modelling and provide insights into the futures anticipated by Twitter's futurists. The research demonstrates the futurists' language cues signals futures-in-the-making that enhance social media users to anticipate their own scenarios and respond to them in present. The fully open-sourced dataset, interactive analysis, and reproducible source code are available for further exploration.

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