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Title: A Large Open Dataset from the Parler Social Network
Parler is as an ``alternative'' social network promoting itself as a service that allows to ``speak freely and express yourself openly, without fear of being deplatformed for your views.'' Because of this promise, the platform become popular among users who were suspended on mainstream social networks for violating their terms of service, as well as those fearing censorship. In particular, the service was endorsed by several conservative public figures, encouraging people to migrate from traditional social networks. After the storming of the US Capitol on January 6, 2021, Parler has been progressively deplatformed, as its app was removed from Apple/Google Play stores and the website taken down by the hosting provider. This paper presents a dataset of 183M Parler posts made by 4M users between August 2018 and January 2021, as well as metadata from 13.25M user profiles. We also present a basic characterization of the dataset, which shows that the platform has witnessed large influxes of new users after being endorsed by popular figures, as well as a reaction to the 2020 US Presidential Election. We also show that discussion on the platform is dominated by conservative topics, President Trump, as well as conspiracy theories like QAnon.  more » « less
Award ID(s):
1945058
NSF-PAR ID:
10252386
Author(s) / Creator(s):
; ; ; ; ; ;
Editor(s):
Budak, Ceren; Cha, Meeyoung; Quercia, Daniele; Xie, Lexing
Date Published:
Journal Name:
Proceedings of the International AAAI Conference on Weblogs and Social Media
Volume:
15
ISSN:
2334-0770
Page Range / eLocation ID:
943--951
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. 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