Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"

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.

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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.

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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.