REVIEW 2 cited by
Labeled Datasets for Research on Information Operations
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Social media platforms have become a hub for political activities and discussions, democratizing participation in these endeavors. However, they have also become an incubator for manipulation campaigns, like information operations (IOs). Some social media platforms have released datasets related to such IOs originating from different countries. However, we lack comprehensive control data that can enable the development of IO detection methods. To bridge this gap, we present new labeled datasets about 26 campaigns, which contain both IO posts verified by a social media platform and over 13M posts by 303k accounts that discussed similar topics in the same time frames (control data). The datasets will facilitate the study of narratives, network interactions, and engagement strategies employed by coordinated accounts across various campaigns and countries. By comparing these coordinated accounts against organic ones, researchers can develop and benchmark IO detection algorithms.
Forward citations
Cited by 2 Pith papers
-
Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems
A new platform-agnostic network method reveals that 0.33% of users introduce nearly 70% of narratives that migrate between Truth Social and X during the 2024 U.S. election.
-
Social Media Information Operations
A tutorial that frames social media information operations as an optimization problem and surveys the analytics, threat models, and countermeasures that support it.
Discussion (0). Continue with ORCID to comment.