Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Social and Information Networks

arXiv:2001.08438 (cs)
[Submitted on 23 Jan 2020]

Title:The Pushshift Telegram Dataset

Authors:Jason Baumgartner, Savvas Zannettou, Megan Squire, Jeremy Blackburn
View PDF
Abstract:Messaging platforms, especially those with a mobile focus, have become increasingly ubiquitous in society. These mobile messaging platforms can have deceivingly large user bases, and in addition to being a way for people to stay in touch, are often used to organize social movements, as well as a place for extremists and other ne'er-do-well to congregate. In this paper, we present a dataset from one such mobile messaging platform: Telegram. Our dataset is made up of over 27.8K channels and 317M messages from 2.2M unique users. To the best of our knowledge, our dataset comprises the largest and most complete of its kind. In addition to the raw data, we also provide the source code used to collect it, allowing researchers to run their own data collection instance. We believe the Pushshift Telegram dataset can help researchers from a variety of disciplines interested in studying online social movements, protests, political extremism, and disinformation.
Subjects: Social and Information Networks (cs.SI); Computers and Society (cs.CY)
Cite as: arXiv:2001.08438 [cs.SI]
  (or arXiv:2001.08438v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.2001.08438

Submission history

From: Savvas Zannettou [view email]
[v1] Thu, 23 Jan 2020 10:37:33 UTC (98 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.SI
< prev   |   next >
new | recent | 2020-01
Change to browse by:
cs
cs.CY

References & Citations

DBLP - CS Bibliography

listing | bibtex
Savvas Zannettou
Jeremy Blackburn
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)