Telegram channels covering Russia’s invasion of Ukraine: a comparative analysis of large multilingual corpora

Oleinik, Anton

doi:10.1007/s42001-023-00240-9

J Comput Soc Sc

2024

DOI: 10.1007/s42001-023-00240-9

|View full text |Cite

Telegram channels covering Russia’s invasion of Ukraine: a comparative analysis of large multilingual corpora

Anton Oleinik

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Bias in X (Twitter) and Telegram Based Intelligence Analysis: Exploring Challenges and Potential Mitigating Roles of AI

Karakikes,

Alexiadis,

Kotis

2024

SN COMPUT. SCI.

View full text Add to dashboard Cite

Bias in X (Twitter) and Telegram Based Intelligence Analysis: Exploring Challenges and Potential Mitigating Roles of AI

Karakikes,

Alexiadis,

Kotis

2024

SN COMPUT. SCI.

View full text Add to dashboard Cite

Detecting coordinated and bot-like behavior in Twitter: the Jürgen Conings case

De Clerck,

Fernandez Toledano,

Van Utterbeeck

et al. 2024

EPJ Data Sci.

View full text Add to dashboard Cite

Social media platforms can play a pivotal role in shaping public opinion during times of crisis and controversy. The COVID-19 pandemic resulted in a large amount of dubious information being shared online. In Belgium, a crisis emerged during the pandemic when a soldier (Jürgen Conings) went missing with stolen weaponry after threatening politicians and virologists. This case created further division and polarization in online discussions. In this paper, we develop a methodology to study the potential of coordinated spread of incorrect information online. We combine network science and content analysis to infer and study the social network of users discussing the case, the news websites shared by those users, and their narratives. Additionally, we examined indications of bots or coordinated behavior among the users. Our findings reveal the presence of distinct communities within the discourse. Major news outlets, conspiracy theory websites, and anti-vax platforms were identified as the primary sources of (dis)information sharing. We also detected potential coordinated behavior and bot activity, indicating possible attempts to manipulate the discourse. We used the rapid semantic similarity network for the analysis of text, but our approach can be extended to the analysis of images, videos, and other types of content. These results provide insights into the role of social media in shaping public opinion during times of crisis and underscore the need for improved strategies to detect and mitigate disinformation campaigns and online discourse manipulation. Our research can aid intelligence community members in identifying and disrupting networks that spread extremist ideologies and false information, thereby promoting a more informed and resilient society.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Telegram channels covering Russia’s invasion of Ukraine: a comparative analysis of large multilingual corpora

Cited by 2 publications

References 50 publications

Bias in X (Twitter) and Telegram Based Intelligence Analysis: Exploring Challenges and Potential Mitigating Roles of AI

Bias in X (Twitter) and Telegram Based Intelligence Analysis: Exploring Challenges and Potential Mitigating Roles of AI

Detecting coordinated and bot-like behavior in Twitter: the Jürgen Conings case

Contact Info

Product

Resources

About