extended-abstract

Multi-Means Analysis of Information Diffusion in Telegram

Authors:

Jennifer Neumann,

Peter M. FischerAuthors Info & Claims

Websci Companion '24: Companion Publication of the 16th ACM Web Science Conference

Pages 8 - 9

https://doi.org/10.1145/3630744.3658417

Published: 13 June 2024 Publication History

Get Access

Abstract

In this poster, we present an approach to study the structure of information diffusion cascades in Telegram, incorporating its group-oriented structure and multiple means of diffusion to gain a better coverage and higher trust.

References

[1]

Jason Baumgartner 2020. The Pushshift Telegram Dataset. In AAAI ICWS M, Vol. 14. 840–847.

Google Scholar

[2]

P. Bródka, K. Musial, and J. Jankowski. 2020. Interacting Spreading Processes in Multilayer Networks: A Systematic Review. IEEE Access 8 (2020), 10316–10341.

Crossref

Google Scholar

[3]

Jia-Chen Gu, Chongyang Tao, and Zhen-Hua Ling. 2022. Who Says What to Whom: A Survey of Multi-Party Conversations. In IJCAI. 5486–5493.

Google Scholar

[4]

Jonas Rieskamp 2024. From Virality to Veracity: Examining False Information on Telegram vs. Twitter. In HICSS.

Google Scholar

[5]

Io Taxidou 2016. Information Diffusion and Provenance of Interactions in Twitter: Is it only about Retweets?. In WWW Conference (companion). 113–114.

Digital Library

Google Scholar

[6]

MS Ulizko 2022. Critical Paths of Information Dissemination in Networks. Scientific Visualization 14, 2 (2022), 98–107.

Crossref

Google Scholar

[7]

Rongxin Zhu, Jey Han Lau, and Jianzhong Qi. 2021. Findings on Conversation Disentanglement. arXiv preprint arXiv:2112.05346 (2021).

Google Scholar

Index Terms

Multi-Means Analysis of Information Diffusion in Telegram
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

Recommendations

Correcting for missing data in information cascades
WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

Transmission of infectious diseases, propagation of information, and spread of ideas and influence through social networks are all examples of diffusion. In such cases we say that a contagion spreads through the network, a process that can be modeled by ...
Characteristics Estimation of Information Sources by Information Diffusion Analysis
WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01

This paper presents a novel method to estimate characteristics of information sources about a topic by analyzing their information diffusion subnetworks in blogspace. In an information diffusion network, each influential information source has an ...
Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Information diffusion has been widely studied in networks, aiming to model the spread of information among objects when they are connected with each other. Most of the current research assumes the underlying network is homogeneous, i.e., objects are of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Websci Companion '24: Companion Publication of the 16th ACM Web Science Conference

May 2024

128 pages

ISBN:9798400704536

DOI:10.1145/3630744

Editors:
Raphael Heiberger
University of Stuttgart, Germany
,
Ujwal Gadiraju
Delft University of Technology, The Netherlands
,
Marc Spaniol
Université de Caen Normandie, France
,
Katharina Kinder-Kurlanda
University of Klagenfurt, Austria
,
Agnieszka Faleńska
University of Stuttgart, Germany
,
Afra Mashhadi
University of Washington, USA
,
Jun Sun
GESIS, Germany
,
Sierra Kaiser
University of Stuttgart, Germany
,
Steffen Staab
University of Stuttgart, Germany

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2024

Check for updates

Author Tags

Qualifiers

Extended-abstract
Research
Refereed limited

Funding Sources

Conference

Websci '24

Sponsor:

SIGWEB

Websci '24: 16th ACM Web Science Conference

May 21 - 24, 2024

Stuttgart, Germany

Acceptance Rates

Websci Companion '24 Paper Acceptance Rate 27 of 58 submissions, 47%;

Overall Acceptance Rate 245 of 933 submissions, 26%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
26
Total Downloads

Downloads (Last 12 months)26
Downloads (Last 6 weeks)3

Reflects downloads up to 13 Feb 2025

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Index Terms

Recommendations

Correcting for missing data in information cascades

Characteristics Estimation of Information Sources by Information Diffusion Analysis

Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Login options

Full Access

View options

PDF

eReader

HTML Format

Share

Share this Publication link

Share on social media

Affiliations