Scientific Truths are Not Self-Evident

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?

Data

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.

Background and Motivation

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?

Results and Analysis

Please see this blog post for further details on the result of this project: https://litdigitaldiversity.northeastern.edu/scientific-truths-are-not-self-evident/

References

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2. D. Duhaime. Clustering Semantic Vectors with Python, 2015.
3. W. N. Francis and H. Kucera. Brown corpus.
4. Ann Hillier, Ryan P. Kelly, and Terrie Klinger. Narrative style influences citation frequency in climate change science. PLOS ONE, 11(12):1–12, 12 2016.
5. Jeremy Howard et al. fastai. https://github.com/fastai/fastai, 2018.
6. Montan˜a C´amara Hurtado and Jos´e A. L´opez Cerezo. Political dimensions of scientific culture: Highlights from the ibero-american survey on the social perception of science and scientific culture. Public Understanding of Science, 21(3):369–384, 2012.
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8. Michael D. Jones. Cultural characters and climate change: How heroes shape our perception of climate science. Social Science Quarterly, 95(1):1– 39, 2014.
9. Jason S. Kessler. Scattertext: a browser-based tool for visualizing how corpora differ. 2017.
10. owygs156. K means clustering example with word2vec in data mining or machine learning. 2017.
11. A. Patel. Nlp analysis and visualizations of presidentinvaalit2018. News from the Lab, 2018.
12. Radim ˇReh˚uˇrek and Petr Sojka. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages 45–50, Valletta, Malta, May 2010. ELRA. http://is.muni.cz/publication/884893/en.
13. Nick Ruest. climatemarch tweets april 19-may 3, 2017, 2017.
14. Nick Ruest. marchforscience tweets april 12-26, 2017, 2017.
15. Mohammad Salehan, Dan J. Kim, and Jae-Nam Lee. Are there any relationships between technology and cultural values? a country-level trend study of the association between information communication technology and cultural values. Information Management, 55(6):725–745, 2018.
16. Mark A. Thompson. Space race: African american newspapers respond to sputnik and apollo 11. Master of Arts (History), 2007.
17. Arun Vishwanath and Hao Chen. Personal communication technologies as an extension of the self: A cross-cultural comparison of people’s associations with technology and their symbolic proximity with others. Journal of the American Society for Information Science and Technology, 59(11):1761– 1775, 2008.

Contact

Contact: dalton.n@husky.neu.edu