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This week I was given the opportunity to read Words That Have Made History, Or Modeling The Dynamics Of Linguistic Changes by Maciej Eder. The reading discusses the use cases of different common words throughout time. The field is called quantitative linguistics and Google defines it as “the comparative study of the frequency and distribution of words and syntactic structures in different texts.” The article uses a version of trend searching in order to capture the frequency of different words used. Based on the data presented, it appears that use of language goes through cycles of rapid change followed by slower change. The article does not attempt to link these to specific events, however, I believe it would be an incredible extension to this project. In the post by EC called Simpson’s Paradox: Is the data telling the right story? The author states “They conclude that data has to be analyzed very carefully in order to make the correct conclusions.” I think this becomes very relevant because of the ambiguity of the word selection. The study could have chose completely different words and may have formed a different conclusion.
On the subject of quantitative linguistics, I have some experience using a really cool tool from Google search that is helping modern scientists solve solutions using data. The tool is called Google Trends and it uses the exact same methodology as this article.
One really great use case example is catching flu outbreaks by find the amount of times people search flu symptoms online. This allows hospitals, schools and business’s to be prepared and take precaution.
https://www.lifewire.com/google-flu-trends-1616299
Another interesting trend that you can follow is the correlation of searching frequency for Bitcoin and its price. Below I included two figures, that when you compare seem shockingly similar. The first is the search results for “Bitcoin” and the second is Bitcoins price.
I think using something like Google Trends for a project in the future would be a great way to incorporate unconventional and linguistic based data into an analysis.
Sources
Eder, Maciej. “Words that Have Made History, or Modeling the Dynamics of Linguistic Changes.” DH. 2018.
EC. “Simpson’s Paradox: Is the data telling the right story? ” 2018.
https://trends.google.com/trends/explore?q=%2Fm%2F05p0rrx
https://www.coindesk.com/price/
https://www.lifewire.com/google-flu-trends-1616299