Telegram has become one of the most successful instant messaging services in recent years. In this paper, we developed a crawler to gather its public data. To the best of our knowledge, this paper is the first attempt to analyze the structural and topical aspects of messages published in Telegram instant messaging service using crawled data. We also extracted the mention graph and page rank of our data collection which indicates important differences between linking patterns of Telegram nodes and other usual networks. We also classified messages to detect advertisement and spam messages.
%0 Conference Paper
%1 10.1145/3132847.3133132
%A Dargahi Nobari, Arash
%A Reshadatmand, Negar
%A Neshati, Mahmood
%B Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
%C New York, NY, USA
%D 2017
%I Association for Computing Machinery
%K analysis antrag classification deconspire detection graph instant messaging network pagerank spam telegram
%P 2035–2038
%R 10.1145/3132847.3133132
%T Analysis of Telegram, An Instant Messaging Service
%U https://doi.org/10.1145/3132847.3133132
%X Telegram has become one of the most successful instant messaging services in recent years. In this paper, we developed a crawler to gather its public data. To the best of our knowledge, this paper is the first attempt to analyze the structural and topical aspects of messages published in Telegram instant messaging service using crawled data. We also extracted the mention graph and page rank of our data collection which indicates important differences between linking patterns of Telegram nodes and other usual networks. We also classified messages to detect advertisement and spam messages.
%@ 9781450349185
@inproceedings{10.1145/3132847.3133132,
abstract = {Telegram has become one of the most successful instant messaging services in recent years. In this paper, we developed a crawler to gather its public data. To the best of our knowledge, this paper is the first attempt to analyze the structural and topical aspects of messages published in Telegram instant messaging service using crawled data. We also extracted the mention graph and page rank of our data collection which indicates important differences between linking patterns of Telegram nodes and other usual networks. We also classified messages to detect advertisement and spam messages.},
added-at = {2020-09-10T09:50:02.000+0200},
address = {New York, NY, USA},
author = {Dargahi Nobari, Arash and Reshadatmand, Negar and Neshati, Mahmood},
biburl = {https://www.bibsonomy.org/bibtex/25f9cbc9df1d8ad1b94a444900b798d92/schwemmlein},
booktitle = {Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
doi = {10.1145/3132847.3133132},
interhash = {d74629af44c1cbdccaae3d76ed0edb0e},
intrahash = {5f9cbc9df1d8ad1b94a444900b798d92},
isbn = {9781450349185},
keywords = {analysis antrag classification deconspire detection graph instant messaging network pagerank spam telegram},
location = {Singapore, Singapore},
numpages = {4},
pages = {2035–2038},
publisher = {Association for Computing Machinery},
series = {CIKM '17},
timestamp = {2020-09-10T10:15:25.000+0200},
title = {Analysis of Telegram, An Instant Messaging Service},
url = {https://doi.org/10.1145/3132847.3133132},
year = 2017
}