Synergizing Contextual Semantics and Moral Knowledge Graphs: A Dual-Path Architecture for Moral Foundation Prediction

Synergizing Contextual Semantics and Moral Knowledge Graphs: A Dual-Path Architecture for Moral Foundation Prediction | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 August 2025 V1 Latest version Share on Synergizing Contextual Semantics and Moral Knowledge Graphs: A Dual-Path Architecture for Moral Foundation Prediction Authors : P Sam Sahil , Anupam Jamatia 0000-0001-6244-8626 [email protected] , and Kunal Chakma 0000-0002-5648-0918 Authors Info & Affiliations https://doi.org/10.22541/au.175440469.97011244/v1 353 views 161 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper introduces a hybrid dual-path architecture that bridges the gap between interpretable lexicon-based methods and opaque LLMs by integrating a contextual encoder (RoBERTa-MLP) with a graph-based encoder (GAT-eMFD) that captures structured moral knowledge. We further extend this into a multimodal framework, MOTIV, which incorporates textual, spatial, temporal, and behavioral data to contextualize moral expression. We evaluate our approach on three diverse corpora—MFTC (Twitter/X, 35,108 tweets), MFRC (Reddit, 16,123 comments), and the geotagged MOTIV dataset (1,483 Geo-tagged tweets)—unified under a consistent moral foundation schema. Our dual-path model sets a new state of the art, achieving a Macro F1-score of 0.69 on MFTC (a 3% improvement over the BERT baseline of 0.67) and 0.40 on MFRC. Ablation studies confirm that the performance gains arise from the fusion of contextual semantics and moral knowledge graphs. However, severe class imbalance in the datasets particularly affecting underrepresented foundations like Fairness and Purity remains a critical limitation, as reflected in 0.00 F1 scores for these classes. This work offers two key contributions: (1) demonstrating the effectiveness of combining deep semantics with structured moral reasoning, and (2) presenting a multimodal paradigm for analyzing moral discourse. These findings advance moral foundation prediction and highlight ongoing challenges in achieving fair and reliable moral inference in AI systems. Supplementary Material File (willeyexpertsystem_sahilea.pdf) Download 1.28 MB Information & Authors Information Version history V1 Version 1 05 August 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computational social science dual-path architecture graph attention network large language model moral foundation prediction moral foundations theory multimodal fusion Authors Affiliations P Sam Sahil HKBK College of Engineering View all articles by this author Anupam Jamatia 0000-0001-6244-8626 [email protected] National Institute of Technology Agartala View all articles by this author Kunal Chakma 0000-0002-5648-0918 National Institute of Technology Agartala View all articles by this author Metrics & Citations Metrics Article Usage 353 views 161 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation P Sam Sahil, Anupam Jamatia, Kunal Chakma. Synergizing Contextual Semantics and Moral Knowledge Graphs: A Dual-Path Architecture for Moral Foundation Prediction. Authorea . 05 August 2025. DOI: https://doi.org/10.22541/au.175440469.97011244/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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