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Existing attention and graph-based methods struggle to build meaningful connections because posts are fragmented and disjointed, especially under limited supervision. The study proposes an index attention mechanism guided by psycholinguistic prior knowledge and introduces the Index Attention Network (IAN) for targeted deep-semantic fusion, achieving strong results on Kaggle and competitive performance on Pandora.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/using-psycholinguistic-clues-to-index-deep-semantic-evidences-personality-detection-in-social-media-texts/293163/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/using-psycholinguistic-clues-to-index-deep-semantic-evidences-personality-detection-in-social-media-texts/293163.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is personality detection on social media more challenging than traditional personality testing?","Question",{"text":112,"@type":113},"It must infer dominant traits from multiple short, noisy posts rather than from a single controlled questionnaire or coherent writing sample, making semantic connections harder to construct.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What limitation do existing attention or graph-based methods face on social media text?",{"text":117,"@type":113},"They rely on well-defined relationships between posts, but social media content is inherently disjointed and sparse, so capturing useful connections is difficult under limited supervision.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the proposed Index Attention Network (IAN) address this problem?",{"text":121,"@type":113},"IAN uses an index attention mechanism guided by prior psycholinguistic knowledge to fuse scattered deep semantic signals and infer personality labels via targeted information fusion.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},293163,1789660541,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","RESEARCH  \nARTICLE  \nUsing Psycholinguistic Clues to Index Deep Semantic Evidences: Personality Detection in Social Media Texts  \nQirui Tang1, Wenkang Jiang2, Xinlong Pan3,*, Lei Lin4, Jizhao Zhu5, Yihua Du4,* and Donghong Sun6  \n1 School of Cyber Security, University of Chinese Academy of Sciences, Beijing 101408, China  \n2 Australian Institute for Machine Learning, The University of Adelaide, Adelaide 5005, Australia  \n3 Institute of Information Fusion, Naval Aviation University, Yantai 264001, China  \n4 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100085, China  \n5 School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China  \n6 Institute for Network Science and Cyberspace, Tsinghua University, Beijing 100084, China  \nAbstract  \nDetecting personalities in social media content isan important application of personality psychology. Most early studies apply a coherent piece of writing to personality detection, but today, the challenge is to identify dominant personality traits from a series of short, noisy social media posts. To this end, recent studies have attempted to individually encode the deep semantics of posts, often using attention-based methods, and then relate them, or directly assemble them into graph structures. However, due to the inherently disjointed and noisy nature of social media content, constructing meaningful connections remains challenging. While such methods rely on well-defined relationships between posts, effectively capturing these connections in fragmented  \nAcademic Editor:  \n Yulong Huang  \nSubmitted: 04 March 2025  \nAccepted: 15 April 2025  \nPublished: 26 April 2025  \nVol. 2, No. 2, 2025 .  \n 10.62762/CJIF.2025.820998  \n*Corresponding authors:  \n􀀀 Xinlong Pan  \n[airadar@126.com](airadar@126.com)  \n􀀀 Yihua Du  \n[yhdu@cashq.ac.cn](yhdu@cashq.ac.cn)  \nand sparse content is non-trivial, particularly under limited supervision or noisy input. To tackle this, we draw inspiration from the scanning reading technique—commonly recommended for efficiently processing large volumes of information—and propose an index attention mechanism as a solution. This mechanism leverages prior psycholinguistic knowledge as an “index” to guide attention, thereby enabling moreeffective information fusion across scattered semantic signals. Building on this idea, we introduce the Index Attention Network (IAN)—a novel framework designed to infer personality labels by performing targeted information fusion over deep semantic representations of individual posts. Through a series of experiments, IAN achieved state-of-the-art performance on the Kaggle dataset and performance comparable to graph convolutional networks (GCN) on the Pandora dataset. Notably, IAN delivered an average improvement of 13% in terms of macro-F1 scores with the Kaggle  \ndataset. The code for IAN is available at GitHub: [https://github.com/Once2gain/IAN](https://github.com/Once2gain/IAN).  \nKeywords: personality detection, attention mechanism,  \nCitation  \nTang, Q., Jiang, W., Pan, X., Lin, L., Zhu, J., Du, Y., & Sun, D. (2025) . Using Psycholinguistic Clues to Index Deep Semantic Evidences: Personality Detection in Social Media Texts. Chinese Journal of Information Fusion, 2(2), 112–126 .  \n© 2025 by the Authors. Published by Institute of Central Computation and Knowledge. This is an open access article under the CC BY license ([https://creati](https://creati)[vecommons.org/licenses/by/4.0/](vecommons.org/licenses/by/4.0/)) .  \nsocial media text mining, information fusion.  \n1 Introduction  \nPersonality testing is a common task in psychology. The traditional method of testing a subject’s personality is an artificially designed questionnaire, which is relatively reliable but not particularly efficient. To this end, researchers have proposed several different automated methods of testing personalities. Of these, analyzing user-generated content is one of the most important [1, 2] . Fortunately, social media provides vas","cbCaifJ8Phff67Mk","https://ap.wps.com/l/cbCaifJ8Phff67Mk","pdf",2784791,15,"English","# Abstract\n# Introduction\n## Language use and personality\n## Progression of Personality Detection in Texts","[{\"question\":\"Why is personality detection on social media more challenging than traditional personality testing?\",\"answer\":\"It must infer dominant traits from multiple short, noisy posts rather than from a single controlled questionnaire or coherent writing sample, making semantic connections harder to construct.\"},{\"question\":\"What limitation do existing attention or graph-based methods face on social media text?\",\"answer\":\"They rely on well-defined relationships between posts, but social media content is inherently disjointed and sparse, so capturing useful connections is difficult under limited supervision.\"},{\"question\":\"How does the proposed Index Attention Network (IAN) address this problem?\",\"answer\":\"IAN uses an index attention mechanism guided by prior psycholinguistic knowledge to fuse scattered deep semantic signals and infer personality labels via targeted information fusion.\"}]","Using Psycholinguistic Clues to Index Deep Semantic Evidences - Personality Detection in Social Media Texts | PDF",38]