Social-Annotate: Self-Healing Browser Extension to Annotate and Collect Social Media Data
- URL: http://arxiv.org/abs/2607.01460v1
- Date: Wed, 01 Jul 2026 20:31:53 GMT
- Title: Social-Annotate: Self-Healing Browser Extension to Annotate and Collect Social Media Data
- Abstract summary: Social-Annotate is a flexible browser extension that facilitates direct data collection on online platforms.<n>By injecting customizable forms into webpages, the tool captures annotations while users interact with the native environment.<n>Social-Annotate offers no-code design interface for the survey forms for non-technical users.
- Score: 4.5765295530618895
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Social Media Data Toolkit: Standardization and Anonymization of Social Network Datasets [5.06428614449742]
The Social Media Data Toolkit (projectname) is a Python framework designed for the standardization, anonymization, and enrichment of social network datasets.<n>projectname unifies diverse data structures into a generic schema comprising Communities, Accounts, Posts, Actions, and Entities.<n>The framework features an anonymization module to secure Personally Identifiable Information (PII) and an extendable enrichment layer that integrates Large Language Models (LLMs) and network analysis tools.
arXiv Detail & Related papers (2026-04-30T10:56:49Z) - ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship [55.402028219661986]
We develop tools to empower agents with external tools to execute diverse actions.<n>We first design a dozen user-centric tools simulating various multimedia applications, which can cover different types of social support behaviors.<n>Based on ComPASS-Bench, we further synthesize tool use records to fine-tune the Qwen3-8B model, yielding a task-specific ComPASS-Qwen.
arXiv Detail & Related papers (2026-04-20T14:49:16Z) - SocialX: A Modular Platform for Multi-Source Big Data Research in Indonesia [0.0]
SocialX is a modular platform for big data research in Indonesia.<n>It integrates heterogeneous data collection, language-aware preprocessing, and pluggable analysis into a unified, source-agnostic pipeline.
arXiv Detail & Related papers (2026-03-27T10:22:34Z) - MARSAD: A Multi-Functional Tool for Real-Time Social Media Analysis [7.268812063067803]
MARSAD is a multifunctional natural language processing (NLP) platform designed for real-time social media monitoring and analysis.<n>It enables researchers and non-technical users alike to examine both live and archived social media content.<n>It produces detailed visualizations and reports across various dimensions, including sentiment analysis, emotion analysis, propaganda detection, fact-checking, and hate speech detection.
arXiv Detail & Related papers (2025-12-01T07:31:37Z) - RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Social Media [48.63633320837672]
We introduce RedNote-Vibe, the first longitudinal (5-years) dataset for social media AIGT analysis.<n>This dataset is sourced from Xiaohongshu platform, containing user engagement metrics and timestamps spanning from the pre-LLM period to July 2025.<n>To detect AIGT in the context of social media, we propose PsychoLinguistic AIGT Detection Framework (PLAD), an interpretable approach.
arXiv Detail & Related papers (2025-09-26T08:36:45Z) - Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems [9.119607936530038]
Political discourse increasingly fragmented across different social networks.<n>To understand how narratives traverse fragmented ecosystems, we offer a structural lens for anticipating how narratives traverse ecosystems.<n>These findings offer implications for crossplatform governance, content moderation, and policy interventions.
arXiv Detail & Related papers (2025-05-22T16:53:52Z) - Multi-Platform Aggregated Dataset of Online Communities (MADOC) [64.45797970830233]
MADOC aggregates and standardizes data from Bluesky, Koo, Reddit, and Voat (2012-2024), containing 18.9 million posts, 236 million comments, and 23.1 million unique users.<n>The dataset enables comparative studies of toxic behavior evolution across platforms through standardized interaction records and sentiment analysis.
arXiv Detail & Related papers (2025-01-22T14:02:11Z) - Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases.
Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding.
This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
arXiv Detail & Related papers (2023-07-05T20:16:20Z) - GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training
Data Exploration [97.68234051078997]
We discuss how Pyserini can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts.
We include a Jupyter Notebook-based walk through the core interoperability features, available on GitHub.
We present GAIA Search - a search engine built following previously laid out principles, giving access to four popular large-scale text collections.
arXiv Detail & Related papers (2023-06-02T12:09:59Z) - Federated Social Recommendation with Graph Neural Network [69.36135187771929]
We propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem.
We devise a novel framework textbfFedrated textbfSocial recommendation with textbfGraph neural network (FeSoG)
arXiv Detail & Related papers (2021-11-21T09:41:39Z) - Curating Social Media Data [0.0]
We propose a data curation pipeline, namely CrowdCorrect, to enable analysts cleansing and curating social data.
Our pipeline provides an automatic feature extraction from a corpus of social media data using existing in-house tools.
The implementation of this pipeline also includes a set of tools for automatically creating micro-tasks to facilitate the contribution of crowd users in curating the raw data.
arXiv Detail & Related papers (2020-02-21T10:07:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.