A model of Continuous Opinion, Content Filtering and Bounded Information Uptake to describe the formation of echo chambers and filter bubbles in social networks.

The model describes the formation of opinions in social networks by considering content filtering and a bounded amount of information that can be processed (assigned as parameter u). The model is implemented as an agent based model. With the network structure and the parameter u as input variables.

The algorithm reads:

1. Randomly choose an agent and its neighbors
2. Determine the u closest opinions of its neighbors
3. Take the mean of these u closest opinions and assign it to the chosen agent
4. Repeat the above steps until convergence is reached

The module 'COntent.py' defines the Model class. The model can be executed as shown in the 'COntent_run.py' script.