Analysis of 2011 Twitter's Trending Topics in Comparison to Top News Headlines. Analysis of Twitter's Trending Topics.ipynb contains code that processes and visualizes trending Twitter data based on the dataset described below. The Poster and Report are included describing the motivation for the project, why interactive visualizations are advantageous, and what is ultimately gained by analzying trending Twitter data compared to news headlines. Today, the field of ethical machine learning continues to gain momentum carving out a new niche for data processing and visualization tools, like Google's Facets API, utilized in this project.
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tweets/ - folder contaning all tweet data
Analysis of Twitter's Trending Topics.ipynb - Jupyter notebook file with full intsructions on how to run code. Contains an interactive visualization of the data processed.
For quick start up follow instructions below:
- Fork this repository.
- Open Google Colaboratory.
- File > open > Github (tab) > your-username/Twitter-Trending > Analysis of Twitter's Trending Topics.ipynb
- WITHOUT running any of the cells, scroll to the output of the last cell (Facets chart should already be displayed when opening).
- This output is interactive and you can use your own parameters for visualizing the data.
Note the above works well for those curious on simply viewing the visualization of the data without making changes. Alternatively, you can clone the repo and run locally to have more hands on controls of the data.
Dataset_README - README from the original dataset
Figure 1 and 2 - Example figures generated from interactive Google Facets data visualzation tool - are attached above.
Headlines.csv - headlines that were web scrapped. Use for a future project
TT-annoations.csv - csv file with hashes for each tweet
Twitter Trending Posdter.pdf - poster format of full written report
Twitter Trending Written Report.pdf - full written report of results