Update: Use this colab notebook to reproduce our results

If you use this code or part of it, please cite the original reference:

Minici, M., Cinus, F., Monti, C., Bonchi, F., & Manco, G. (2022, October). Cascade-based echo chamber detection. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 1511-1520). doi: https://doi.org/10.1145/3511808.3557253

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Before running any experiment, we suggest you install the provided conda environment.

How to install the conda environment: conda env create --file environment.yml

Running an experiment

In both sections, the parameters highlighted in bold are the ones you should modify in order to fully reproduce our results.

Synthetic Experiments

In order to run a synthetic experiment, you can use python grid-synthetic.py if you want to use default settings, otherwise, you can specify several options:

• seed, to change the initial random seed for stochasticity;
• N, to change the number of initial users;
• eta, to change the polarity of each community;
• s, to change the social prior size;
• h, to change the echo-chamber prior size;
• B, you can ignore this parameter;
• items_per_node, to change the number of items per user;
• lr, learning rate of ECD procedure;
• epochs, number of epochs of ECD procedure;
• oversampling, whether you want to oversample the minority class --- either edges or propagations;
• reweighting, whether you want to reweight the two classes w.r.t. their cardinality;
• annealing, whether you want to add an annealing procedure;
• ablation, whether you want to exclude links or propagations from the ECD procedure;
• training_type, you can ignore this parameter;
• model_type, you can ignore this parameter;
• device, whether you want the experiment to be run on GPU. If no GPU is available, then pass an empty string.

Real-World Experiments

In order to run a synthetic experiment, you can use python grid-real.py if you want to use default settings, otherwise, you can specify several options:

• seed, to change the initial random seed for stochasticity;
• dataset, to change the dataset to analyze. Pick one between brexit and vaxNoVax.
• K, to change the number of communities you want to find;
• s, to change the social prior size;
• h, to change the echo-chamber prior size;
• • B, you can ignore this parameter;
• lr, learning rate of ECD procedure;
• epochs, number of epochs of ECD procedure;
• oversampling, whether you want to oversample the minority class --- either edges or propagations;
• reweighting, whether you want to reweight the two classes w.r.t. their cardinality;
• annealing, whether you want to add an annealing procedure;
• link_removal, you can ignore this parameter!
• prop_removal, whether you want to exclude some propagations from the dataset;
• prop_removal_perc, the percentage of propagations you want to remove;
• stance_detection_exp, it is a boolean flag --- i.e.: True or False --- that when you want to analyze the stance detection capability of ECD if set to True, removes all propagations of the analyzed users.
• ablation, whether you want to exclude links or propagations from the ECD procedure;
• training_type, you can ignore this parameter;
• model_type, you can ignore this parameter;
• device, whether you want the experiment to be run on GPU. If no GPU is available, then pass an empty string.