This research project seeks to analyze the perception of science in culture. Techniques from NLP such as word embeddings (word2vec) and sentiment analysis are used. More specifically, this project is trying to exame what are the factors that affect the perception of science in our culture, how have those factors evolved over the last half century (or so), and why?
The data for this project is not included in this repository due to restrictions on Twitter data. The two datasets used in this project are the widely available Brown corpus and Twitter data relating to #ClimateMarch and #MarchForScience hashtags. Roughly two million tweets were hydrated using twarc.
Original motivation: There is a rise fears over AI and science. Why does this happen and how has history evolved to promote this kind of ideology? Further, what are the linguistic characteristics that drive this perception?
Please see this blog post for further details on the result of this project: https://litdigitaldiversity.northeastern.edu/scientific-truths-are-not-self-evident/
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