[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84629-en":3,"doc-seo-84629-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84629,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","SocialAnnotate: Self-Healing Browser Extension for Annotating and Collecting Social Media Data","Human-annotated data underpins machine learning and social media analysis, yet traditional collection pipelines disconnect content from its native context and weaken ecological validity. SocialAnnotate introduces a browser extension for direct, on-platform annotation through injected customizable survey forms, enabling non-technical users to design data capture without coding. To avoid brittleness from changing page structures, the extension uses an LLM-powered self-healing agent that detects changes, regenerates valid selectors, and validates them in a live browser. The extensible platform supports multiple platforms and streamlines data collection and maintenance, supporting intervention studies via dynamic content manipulation.","SOCIAL-ANNOTATE: SELF-HEALING BROWSER EXTENSION TO ANNOTATE AND COLLECT SOCIAL MEDIA DATA  \narXiv :2607 .0 1460v 1 [ cs .CY] 1 Jul 2026  \nAli Najafi  \nSabanci University Türkiye  \n[ali. najafi@sabanciuniv. edu](ali. najafi@sabanciuniv. edu)  \nIsmail Uluturk  \nUniversity of South Florida USA [uluturki@gmail.com](uluturki@gmail.com)  \nOnur Varol  \nSabanci University Türkiye  \n[onur. varol@sabanciuniv. edu](onur. varol@sabanciuniv. edu)  \n􀂀 [varollab.com/social-annotate](varollab.com/social-annotate) § [github.com/ViralLab/social-annotate](github.com/ViralLab/social-annotate)  \nABSTRACT  \nHuman-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 SocialAnnotate, 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 targetselectors, and validates them within a live browser environment. Our extensible platform readily supports 12 platforms including social media like 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.  \nKeywords Social Media Analysis, Data Collection, Browser Extension, Self-Healing Agents, Large Language Models, Ecological Validity, Open Source Intelligence  \n1 Introduction  \nMachine learning applications rely heavily on high-quality annotated data to build supervised models and establish reliable ground truth. This is especially true in the domain of social media analysis, where models trained on user behavior and textual content have been historically deployed to detect automated activities and to study online conversations [1, 2, 3, 4, 5] . In recent years, the scope of these models has expanded to address complex information disorders, as malicious actors increasingly leverage autonomous, algorithm-guided social bots to manipulate public opinion and amplify misinformation [6, 7, 8] . Because these automated accounts closely mimic human interactions to steer social discourse, distinguishing them from genuine users requires robust, context-aware classification systems. Consequently, creating rich, accurately labeled datasets has become even more critical for training models to identify algorithmic amplification and preserve the integrity of digital ecosystems [9] .  \nSimilarly, at the post level, developing supervised models for complex content moderation tasks, such as hate speech detection and counter-speech generation [10], coordinated activity and bot detection [11, 12, 4] requires datasets rich in contextual and linguistic nuance. Identifying toxic or hateful content is notoriously challenging because it often relies on subtle cultural markers, implicit hostility, and evolving in-group or out-group dynamics that simplistic keyword filters frequently miss [13, 14] . To build fair and explainable detection on systems, researchers increasingly depend on human annotators to provide granular rationales and capture diverse community perspectives [14] . Furthermore, proactive moderation strategies, such as automated counter-speech generation, depend heavily o","cbCaibcKdKmp84nG","https://ap.wps.com/l/cbCaibcKdKmp84nG","pdf",25989788,1,13,"English","en",105,"# Abstract\n# Introduction\n## Annotated data needs in social media analysis\n## Challenges in labeling and dataset creation\n## Platform access risks and API changes","[{\"question\":\"What problem does SocialAnnotate address in social media data collection?\",\"answer\":\"Traditional pipelines often separate content from its original source, reducing ecological validity and creating heavy collection overhead. SocialAnnotate collects data directly within the native webpage environment.\"},{\"question\":\"How does SocialAnnotate reduce brittleness when webpages change?\",\"answer\":\"It integrates an LLM-powered self-healing agent that detects structural changes, regenerates valid target selectors, and validates them in a live browser environment.\"},{\"question\":\"Who can use SocialAnnotate and how are annotation forms created?\",\"answer\":\"Non-technical users can design annotation survey forms via a no-code design interface, while the extension injects customizable forms into host webpages during interaction.\"}]",1784197306,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"socialannotate-self-healing-browser-extension-for-annotating-and-collecting-social-media-data","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/socialannotate-self-healing-browser-extension-for-annotating-and-collecting-social-media-data/84629/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does SocialAnnotate address in social media data collection?","Question",{"text":75,"@type":76},"Traditional pipelines often separate content from its original source, reducing ecological validity and creating heavy collection overhead. SocialAnnotate collects data directly within the native webpage environment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SocialAnnotate reduce brittleness when webpages change?",{"text":80,"@type":76},"It integrates an LLM-powered self-healing agent that detects structural changes, regenerates valid target selectors, and validates them in a live browser environment.",{"name":82,"@type":73,"acceptedAnswer":83},"Who can use SocialAnnotate and how are annotation forms created?",{"text":84,"@type":76},"Non-technical users can design annotation survey forms via a no-code design interface, while the extension injects customizable forms into host webpages during interaction.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]