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.

通过研究推特上的未来学家预测的除了未来，并深入探究了语言提示对社交媒体用户的预测思维的影响，本研究使用尖端的自然语言处理模型构建了一个可扩展的 NLP 流程，并通过 LDA 和 BERTopic 方法识别了推特上未来学家的 15 个主题和 100 个不同主题，为未来学术研究和推特上的未来学家所预测提供了新的见解。