Assisting the Social Scientist through Human-Machine Consensus: Causal Discovery Approach to Unravel Cyberbullying

Assisting the Social Scientist through Human-Machine Consensus: Causal Discovery Approach to Unravel Cyberbullying | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assisting the Social Scientist through Human-Machine Consensus: Causal Discovery Approach to Unravel Cyberbullying Andrea Baños-Ramos, María Reneses, Jaime Pérez, Gabriel Valverde, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6426731/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Cyberbullying is a pressing issue among minors that demands thoughtful intervention strategies. While causal inference has become popular in many scientific domains, we argue that the goal in social sciences should not be to automate causal discovery, but to support expert-driven understanding through simple, explainable models. We present a framework that builds probabilistic graphical causal models using a consensus-based process between domain experts and structure learning algorithms implemented in GeNIe (PC, Bayesian Search, and GTT). These tools allow easy incorporation of expert constraints, which is essential given the limited sample sizes and measurement challenges typical in social sciences. Rather than emphasizing the discovery of novel causal structures, we show how combining expert insights with machine learning improves model validity, interpretability, and usefulness. Our method provides a principled way to explore interventional questions while acknowledging the strengths and limits of both human and automated reasoning. Biological sciences/Psychology/Human behaviour Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 May, 2025 Reviews received at journal 05 May, 2025 Reviews received at journal 02 May, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers invited by journal 24 Apr, 2025 Editor assigned by journal 24 Apr, 2025 Editor invited by journal 21 Apr, 2025 Submission checks completed at journal 19 Apr, 2025 First submitted to journal 11 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6426731","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448071224,"identity":"741bf682-92f2-4d22-86d1-15742780664a","order_by":0,"name":"Andrea Baños-Ramos","email":"","orcid":"","institution":"Comillas Pontifical University","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Baños-Ramos","suffix":""},{"id":448071226,"identity":"7dc96be7-8f6d-4000-b4ba-7c6209613416","order_by":1,"name":"María Reneses","email":"","orcid":"","institution":"Comillas Pontifical University","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Reneses","suffix":""},{"id":448071227,"identity":"ab130357-f1c6-48fd-9363-19b99af3bc7e","order_by":2,"name":"Jaime Pérez","email":"","orcid":"","institution":"AZTI","correspondingAuthor":false,"prefix":"","firstName":"Jaime","middleName":"","lastName":"Pérez","suffix":""},{"id":448071228,"identity":"d4195f6e-ac1d-4cb7-8c9b-d10f3162104a","order_by":3,"name":"Gabriel Valverde","email":"","orcid":"","institution":"Celering. 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