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It introduces the social-media data, in which users self-report an investment approach, and defends the validity of those labels through textual and event-based checks. It then previews the volume decomposition, the sophistication and lead-lag evidence, and robustness, before positioning the paper against empirical and theoretical literatures. The body proceeds through data description, measure construction, a three-part empirical analysis (volume, sophistication, earnings announcements) and a short robustness section on attention and external validity. A one-paragraph conclusion restates the measurement contribution and the main split. An appendix covers an alternative measure and the classification method.",232 "writing_profile_keywords": [233 "social media data",234 "measurement validation",235 "disagreement decomposition",236 "trading volume",237 "textual analysis",238 "information diffusion",239 "fixed-effects panel regressions"240 ],241 "confidence": 0.8,242 "notes": "Statistical significance conventions and standard-error clustering are not visible in the excerpts. 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