Published December 13, 2021
| Version 1.0
Dataset
Open
Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"
Creators
- 1. Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
- 2. Istituto dei Sistemi Complessi (ISC-CNR), Via dei Taurini 19, I-00185 Roma, Italy.
- 3. Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA AND Centro Fermi, Via Panisperna 89 A, Roma, Italy.
Description
These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper "Universality, criticality and complexity of information propagation on social media". The analysis is reported in https://www.nature.com/articles/s41467-022-28964-8 Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th.
Files
twitter_hashtags.txt.zip
Additional details
Funding
- NRT: Interdisciplinary Training in Complex Networks and Systems 1735095
- National Science Foundation
- CAREER: Network Theory of Critical Interdependent Infrastructures 1552487
- National Science Foundation