🕸 COGNET

Authors: Esben Kran & Jonathan Hvithamar Rystrøm

Started: March 2021

Overview

Data analysis project on a whole Cognitive Science student year before and during the covid-19 pandemic lockdown in Denmark.

Introduction to experiment

During 1.5 years, 28 students communicated intensely on the social messaging app Facebook as part of the cognitive science bachelor’s program at Aarhus University. These students voluntarily gave us their highly anonymized data (see “Anonymization”). In a post-experimental questionnaire, 100% reported that it was their main communication tool in the study group and 87,5% reported that it was their main communication tool with everyone from the study.

The anonymized data contains a message per row between two of the 27 users (28 minus 1 dropout). The weight of a message is 1 for a direct message, while a group message is weighted as 1/(n-1) where n is the amount of users in the group (the sender inclusive).

Anonymization

The information present for each message is the date, time and the anonymized names of the receiver(s) and the sender. The participants downloaded their private messaging data (1-3GB) from Facebook in the period between the 1st of August 2020 and the 1st of January 2021. They personally ran a script that anonymized and extracted the relevant data to create a first-step encrypted dataset using the same hash key of their messages (2-10MB). These were sent to the authors that performed a second-step anonymization via another hash key. Both hash keys were subsequently deleted.

Main documents

Name	Description
data_load.py	Converts the compressed data folders to usable formats. Creates raw_consensual.csv, tidy_data.csv and dropout_dat.csv.
convert.r	Transforms the above messages-by-row data to different node-level network measures. Creates all_node_measures.csv.
brms_preprocessing.Rmd	Preprocesses data for brms. Creates brms_model_data.csv and disaster_dat.csv.
brms_analysis.Rmd	Bayesian analysis and visualization document using brms.
timeseries_visualization.Rmd	Visualizes all_node_measures.csv by week in a range of different narrative graphs.
network_eda.Rmd	Explores one week of data around the lockdown as a static network. Preliminary work for convert.r.
anonymize_messages.py	Anonymizes the raw Messenger data files (~1-3GB) from Facebook to focused files (3-10MB) used as input for data_load.py.

License

Released under MIT by @rysias and @esbenkc.