Social-Annotate: Self-Healing Browser Extension to Annotate and Collect Social Media Data

2026-07-01

AI Summary

This study addresses the limitations of traditional social media data collection methods, which are often cumbersome, context-disconnected, and prone to failure due to frequent changes in page structures—thereby compromising ecological validity. To overcome these challenges, this work proposes a self-healing browser extension that enables non-technical researchers to conduct code-free annotation and intervention experiments directly within native platform interfaces. The system innovatively integrates a large language model–driven self-healing mechanism capable of automatically detecting and repairing selectors to support robust capture of dynamic content. It also features a no-code form designer and a cross-platform injection framework. Validated across twelve major social media platforms, the approach significantly reduces data collection and maintenance costs while empowering researchers to carry out high-quality studies efficiently.

Abstract

Human-annotated data remains foundational for machine learning and social media analysis. However, traditional data collection often relies on cumbersome pipelines that isolate content from its original source, compromising ecological validity. To address these challenges, we present Social-Annotate, a flexible browser extension that facilitates direct data collection on online platforms. By injecting customizable forms into webpages, the tool captures annotations while users interact with the native environment. Social-Annotate offers no-code design interface for the survey forms for non-technical users. Since injecting custom elements directly into host platforms creates a brittle dependency on evolving interfaces, we integrate a self-healing agent powered by large language models. This automated pipeline autonomously detects structural changes, regenerates valid target selectors, and validates them within a live browser environment. Our extensible platform readily supports 12 platforms including social media like $\mathbb{X}$, Instagram, TikTok and P2P messaging platforms WhatsApp and Telegram. Social-Annotate significantly reduces data collection overhead and developer maintenance, enabling researchers of all technical backgrounds to focus on data analysis rather than engineering. Moreover, Social-Annotate provides an ecosystem for conducting intervention studies by dynamic content manipulation.

Problem

• social media data collection
• ecological validity
• browser extension
• self-healing
• human annotation

Innovation

• self-healing
• browser extension
• social media annotation
• large language models
• ecological validity