CognitiveSky: Scalable Sentiment and Narrative Analysis for Decentralized Social Media
This provides a tool for computational social science to monitor mental health discourse and other domains like disinformation detection, though it is incremental as it applies existing methods to a new platform.
The study tackled the challenge of analyzing public discourse on decentralized social media by introducing CognitiveSky, a scalable framework that uses transformer models to annotate sentiment, emotion, and narrative on Bluesky, achieving low operational cost and high accessibility with free-tier infrastructure.
The emergence of decentralized social media platforms presents new opportunities and challenges for real-time analysis of public discourse. This study introduces CognitiveSky, an open-source and scalable framework designed for sentiment, emotion, and narrative analysis on Bluesky, a federated Twitter or X.com alternative. By ingesting data through Bluesky's Application Programming Interface (API), CognitiveSky applies transformer-based models to annotate large-scale user-generated content and produces structured and analyzable outputs. These summaries drive a dynamic dashboard that visualizes evolving patterns in emotion, activity, and conversation topics. Built entirely on free-tier infrastructure, CognitiveSky achieves both low operational cost and high accessibility. While demonstrated here for monitoring mental health discourse, its modular design enables applications across domains such as disinformation detection, crisis response, and civic sentiment analysis. By bridging large language models with decentralized networks, CognitiveSky offers a transparent, extensible tool for computational social science in an era of shifting digital ecosystems.