This repository contains code and data related to the paper "Learning Opinion Dynamics From Social Traces" by Corrado Monti, Gianmarco De Francisci Morales and Francesco Bonchi, published at KDD 2020. If you use the provided data or code, we would appreciate a citation to the paper:

@inproceedings{monti2020learningopinion,
  title={Learning Opinion Dynamics From Social Traces},
  author={Monti, Corrado and De Francisci Morales, Gianmarco and Bonchi, Francesco},
  booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}

Here you will find instructions about (i) using the algorithm we designed (ii) obtaining the Reddit data set we collected (iii) how to reproduce our experiments.

In order to use our model, we provide our implementation in src/model. To use our model on your data, just clone this repo, install the dependencies, and then our model:

git clone https://github.com/corradomonti/learnable-opinion-dynamics.git
cd learnable-opinion-dynamics
pip install -r requirements.txt
pip install src/

You can then use it in Python:

>>> from model import learn_opinion_dynamics

Check its documentation online or with:

>>> help(learn_opinion_dynamics)

For instance, to use it on a simple graph, with 3 nodes, 2 features and 2 time steps, and then print the estimated positions of each feature:

>>> u_v_t_weights = ([0, 1, 0, 1], [1, 0, 1, 1])
>>> v_a_t_weights = ([0, 0, 0, 1], [2, 1, 0, 1], [0, 0, 1, 1], [2, 1, 1, 1])
>>> res = learn_opinion_dynamics(N=3, Q=2, T=2, u_v_t_weights=u_v_t_weights, v_a_t_weights=v_a_t_weights)
>>> print(res.w)

In data/reddit, we provide the Reddit data set we gathered.

To build this data, we consider the 51 subreddits most similar to r/politics according to this; the time stamps are the months between January 2008 and December 2017; for the users, we consider only those posting a minimum of 10 comments per month on r/politics for at least half of the considered months, which gives us 375 users.

Our input files are:

• edges_user.tsv.bz2 contains the interactions among considered users. Each row (t, u, v, w) indicates that user v replied to user u during time step t, for w times.

• edges_feature.tsv.bz2 contains the user-subreddit interactions. Each row (t, u, a, w) indicates that user u participated in subreddit a during time step t, for w times.

Our validation data is:

• feature_scores.tsv.bz2 contains the summary statistics for scores received by each user on each subreddit in each timestep. Specifically, it contains the sum of positive scores, the number of positively scored comments, the sum of negative scores, and the number of negatively scored comments.

• interaction_scores.tsv.bz2 contains data about each interaction between considered users.

All the data files are TSV compressed with bz2 and can be easily opened with pandas:

>>> pd.read_csv("edges_user.tsv.bz2", names=("yearmonth", "parent", "author", "count"), header=None, sep='\t')
>>> pd.read_csv("edges_feature.tsv.bz2", names=("yearmonth", "author", "subreddit", "count"), header=None, sep='\t')
>>> pd.read_csv("feature_scores.tsv.bz2", sep='\t')
>>> pd.read_csv("interaction_scores.tsv.bz2", sep='\t')

In order to reproduce our experiments, we provide our scripts in src/experiments. They need a larger set of dependencies, listed in src/experiments/requirements.txt. In particular, we use MLflow to organize parameters and experiment results. To run both sets of experiments, do:

cd src/experiments/
pip install -r requirements.txt
mlflow ui --port 8765 &
python experiments_synthetic.py
python experiments_reddit.py

Then, you can use the MLflow User Interface at localhost:8765 to inspect the results of each experiment as they are produced.