Scalable AI-Driven Analytics for User Engagement and Stance Detection on Social Media

2026-05-27

AI Summary

This work addresses the lack of scalable, service-oriented frameworks for continuously monitoring user engagement with and stance toward harmful content—such as conspiracy theories—at the scale of social media platforms. We propose the first modular system that integrates a service-based architecture with a multi-stage AI analysis pipeline, combining real-time data stream processing, topic modeling, sentiment analysis, and stance detection to enable sustained, large-scale monitoring of user interactions. A key strength of our approach lies in its capacity to capture early amplification dynamics of such content. Evaluation on over 7 million comments reveals that 70% of conspiracy-related interactions occur within the first week of posting; our system effectively identifies highly active user clusters, most of whom express supportive stances, thereby demonstrating the framework’s scalability and practical utility.

Abstract

Social media platforms have become a major vector for the large-scale dissemination of misinformation and conspiracy content, posing significant risks to public trust, health, and societal stability. While prior work has primarily focused on analysing such content from a behavioural or content-centric perspective, there is a lack of scalable, service-oriented solutions that enable continuous monitoring and analysis of user engagement at platform scale. In this paper, we present a scalable AI-driven service framework for analysing user engagement and stance on social media content. Our system integrates data ingestion, filtering, topic modelling, sentiment analysis, and stance detection into a modular pipeline that can operate on large-scale, real-world datasets. We implement and evaluate our framework on a dataset comprising over 7 million user comments collected from nearly 50,000 YouTube videos associated with conspiracy narratives. Our analysis reveals that conspiracy content attracts up to 70% of total user engagement within the first week of publication, indicating strong early amplification dynamics. Furthermore, we identify a subset of highly active users who exhibit disproportionately high engagement across multiple videos and channels. Stance analysis shows that a majority of users express favourable positions toward conspiracy narratives, highlighting the role of user communities in reinforcing such content. The proposed framework demonstrates the feasibility of deploying scalable, service-oriented analytics for real-time monitoring of user engagement and behavioural patterns. These findings demonstrate the effectiveness of our framework in capturing large-scale engagement dynamics and highlight the importance of early-stage detection and service-based monitoring for mitigating the spread of harmful content.

Problem

• misinformation
• conspiracy content
• user engagement
• stance detection
• social media

Innovation

• scalable AI framework
• user engagement analytics
• stance detection
• service-oriented architecture
• social media monitoring