What Twitter reveals about the differences between cities and the monoculture of the Bay Area
Analysis of Twitter data in 13 English-speaking metropolitan areas (96K users and 180M tweets).
Plus 223K users that aren't in these areas which are collectively put in 'Other'.
For more information, see the blog post:
The blog post only contains a small, selected number of visualizations. For more visualization, download here.
I won't be distributing the data for this project to protect users' privacy. If you'd like to discuss the data, contact me through my website huyenchip.com.
In the metrotwitter_visualization
folder, you can find word clouds that represent the most popular words in bios in each city. The indi
folder visualizes each city independently. The duo
folder visualizes the difference between two cities.
In the metrotwitter_visualization
folder, you can find word clouds that represent the most popular words in tweets in each city. The indi
folder visualizes each city independently. The duo
folder visualizes the difference between two cities.
In the keywords
Jupyter notebook in this GitHub repo, there's the method rank_cities_by_keyword
to visualize any keyword you want, either using bios
or tweets
.
You can also plots multiple keywords on the same plot using the method rank_cities_by_multiple_keywords
.
Also in the keywords
Jupyter notebook, there's the method rank_keywords_in_city
to rank the popularity of keywords within a city.
Just to get a sense of how popular one vs another.