From 58d937b9b05be1763d56052aec17c02e7f7bdc61 Mon Sep 17 00:00:00 2001 From: Yueting Han Date: Mon, 15 Jan 2024 13:27:53 +0000 Subject: [PATCH] README Update --- .DS_Store | Bin 6148 -> 6148 bytes README.md | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/.DS_Store b/.DS_Store index 95f58e722b325cd7c8b070aae119f1b34d88a3e1..2f98918737702bf9e08e75e51d6424c51b6d8379 100644 GIT binary patch delta 112 zcmZoMXffEJ$`T*%{e*#mfrUYjA)O(Up(Hoo#U&{xKM5$t!NAbKuyxf@M^yO~yz&JZ bhQZ1CxdlKy3@lR&7$#q2HQ2m`B|sPe80H(x delta 112 zcmZoMXffEJ$`T(JwUdE?frUYjA)O(Up(Hoo#U&{xKM5$t0mRGv+K)P-%BSF!FUT+q ZPR`FQ0P102nUugV`68>q<}EA%!T{n}8x#Nl diff --git a/README.md b/README.md index 2c1d9e1..3f1ad6d 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ This repo documents the dataset and the programming code used in our paper title ## Dataset In this paper, we investigate a temporal dataset that describes the Facebook vaccination views campaign in network representations, involving nearly 100 million users on Facebook from across countries, continents and languages. -It was provided by [Johnson et al. (2020)](https://www.nature.com/articles/s41586-020-2281-1) and [Illari et al. (2022)](https://www.science.org/doi/10.1126/sciadv.abo8017) in spanning different time periods. The former contains two snapshots in February 2019 and October 2019 (before the COVID-19), which we study in our main paper. The latter contains another two snapshots in November 2019 and December 2020 (at the initial stage of the COVID-19), which we study in Supplementary Material. +It was provided by [Johnson et al. (2020)](https://www.nature.com/articles/s41586-020-2281-1) and [Illari et al. (2022)](https://www.science.org/doi/10.1126/sciadv.abo8017) spanning different time periods. The former contains two snapshots in February 2019 and October 2019 (before the COVID-19), which we study in our main paper. The latter contains another two snapshots in November 2019 and December 2020 (at the initial stage of the COVID-19), which we study in Supplementary Material. Both of them are openly available and documented in two papers separately of different formats (PDF & Excel), thus requiring intensive preprocessing. To make it easier for other researchers to use this dataset, here we reorganise both versions of this dataset in gpickle format (can be directly imported as attributed networks using Python) and in CSV format (for general use of the dataset).