Toxicity in State Sponsored Information Operations
State-sponsored information operations (IOs) increasingly influence global discourse on social media platforms, yet their emotional and rhetorical strategies remain inadequately characterized in scientific literature. This study presents the first comprehensive analysis of toxic language deployment within such campaigns, examining 56 million posts from over 42 thousand accounts linked to 18 distinct geopolitical entities on X/Twitter. Using Google’s Perspective API, we systematically detect and quantify six categories of toxic content and analyze their distribution across national origins, lin- doi
- 10.1145/3720553.3746680
- isbn
- 979-8-4007-1534-1
- name
- Toxicity in State Sponsored Information Operations
- pages
- 86-90
- source
- supplied-bits-xml
- acm_url
- https://dl.acm.org/doi/10.1145/3720553.3746680
- authors
- Ashfaq Ali Shafin, Khandaker Mamun Ahmed
- doi_url
- https://doi.org/10.1145/3720553.3746680
- license
- © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
- summary
- State-sponsored information operations (IOs) increasingly influence global discourse on social media platforms, yet their emotional and rhetorical strategies remain inadequately characterized in scientific literature. This study presents the first comprehensive analysis of toxic language deployment within such campaigns, examining 56 million posts from over 42 thousand accounts linked to 18 distinct geopolitical entities on X/Twitter. Using Google’s Perspective API, we systematically detect and quantify six categories of toxic content and analyze their distribution across national origins, lin
- keywords
- Toxic Content, Information Operations, Influence Operations, Online Social Media, Social Media Analysis, Twitter/X
- arxiv_url
- https://arxiv.org/abs/2507.10936
- published
- 2025-09-15
- conference
- HT '25: 36th ACM Conference on Hypertext and Social Media, Chicago, IL, USA, September 15-19, 2025
- open_access
- false
- acm_html_url
- https://dl.acm.org/doi/full/10.1145/3720553.3746680
- ccs_concepts
- Human-centered computing~Social media
- displayAuthor
- Ashfaq Ali Shafin, Khandaker Mamun Ahmed
- proceedings_url
- https://dl.acm.org/doi/proceedings/10.1145/3720553
- displayPublishTime
- 2025-09-15
- acm_reference_format
- (empty)
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