Social media networks like Facebook, Twitter and Instagram exert an immense amount of influence on the way we connect with friends, receive our news, and learn information. But if we pause and take a second to examine our social networks from a quantitative lens, we can see the role we play in the sprawling modern media web. This GitHub repository was borne from my summer work as a movement building intern at the World Resources Institute. To truly catalyze change, we need to first understand the social networks that affect and amplify a movement's magnitude.
Twitter is a particularly interesting social media network, as it has an easily-understandable structure, allowing for principles of statistics and graph theory to be translated with little modification.
For a complete overview on how to use the tools in this repo, please visit use this Jupyter Notebook
- Note: If working locally, you must be able to access and edit a Jupyter Notebook - instructions can be found here.
Tools used in this repository to collect and analyze data include Gephi and NodeXL, and the GDELT database. Python scripts can be used in conjunction with these tools to better understand our role in social networks.
Here is a sample of some of the analyses and visualizations that can result from this repo:
A filtered k-core map of the forest and landscape restoration twitter network over the summer of 2017. Seeing how two seemingly unrelated networks are connected by just 2 nodes can be used by others in the network to leverage their connections and expand their audience.
An ego network of a single user is like playing six degrees of separation (Twitter Style!). Here, all connections (up to the 3rd degree) are shown in a network. In this way, we can see not only how our friends are connected, but also how our friends-of-friends are connected with one another.
Have fun exploring your networks, and please don't hesitate to shoot me an email at julian@vallyeason.com if you have any questions!
