Scalable AI-Driven Analytics for User Engagement and Stance Detection on Social Media
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911726198325248 |
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| author | Seneviratne, Thammitage Piyumi Wathsala Ikram, Muhammad Vatsalan, Dinusha Asghar, Hassan Kaafar, Mohamed Ali |
| author_facet | Seneviratne, Thammitage Piyumi Wathsala Ikram, Muhammad Vatsalan, Dinusha Asghar, Hassan Kaafar, Mohamed Ali |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_29199 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable AI-Driven Analytics for User Engagement and Stance Detection on Social Media Seneviratne, Thammitage Piyumi Wathsala Ikram, Muhammad Vatsalan, Dinusha Asghar, Hassan Kaafar, Mohamed Ali Social and Information Networks 68T05, 68M11, 62P25 H.3.5; I.2.7; J.4; H.4.3; I.5.4 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. |
| title | Scalable AI-Driven Analytics for User Engagement and Stance Detection on Social Media |
| topic | Social and Information Networks 68T05, 68M11, 62P25 H.3.5; I.2.7; J.4; H.4.3; I.5.4 |
| url | https://arxiv.org/abs/2605.29199 |