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The proposed approach tests the hypothesis that user-generated tweets reflect the author’s psychometric traits, contextual signals, and irony-related textual features. Psychometric features are obtained through an emotion and personality detection module, while context features are derived with an SBERT- and T5-based pretrained language-model pipeline. A Gradient Boosting classifier combines these representations, reaching high accuracy on IROSTEREO training data and a lower score on the official PAN test set.",{"@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/identifying-ironic-content-spreaders-on-twitter-using-psychometrics-contextual-and-ironic-features-with-gradient-boosting-classifier-conference-paper/293554/",{"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/identifying-ironic-content-spreaders-on-twitter-using-psychometrics-contextual-and-ironic-features-with-gradient-boosting-classifier-conference-paper/293554.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address on Twitter?","Question",{"text":112,"@type":113},"It determines whether specific Twitter users consistently spread ironic content.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the framework extract psychometric features?",{"text":117,"@type":113},"It uses an emotion and personality detection module to extract the author’s psychometric characteristics.",{"name":119,"@type":110,"acceptedAnswer":120},"What models are used for contextual and irony-related features?",{"text":121,"@type":113},"Context-based features come from an SBERT and T5-based pretrained language model architecture, supported by contextual and irony embedding modules.","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},293554,1789665509,{"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":39,"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},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Identifying Ironic Content Spreaders on Twitter using Psychometrics, Contextual and Ironic Features with Gradient Boosting Classifier  \nNotebook for PAN at CLEF 2022  \nEhsan Tavan* 1 , Maryam Najafi* 1 and Reza Moradi* 1 1 NLP Department, Part AIResearch Center, Tehran, Iran  \nAbstract  \nThe study of irony detection on social networks has gained much attention in recent years. As part of this task, a collection of users’ tweets is provided, and the goal is to determine if these users are spreaders of irony or not. As we hypothesized that user-generated content is affected by the author’s psychometric characteristics, contextual information, and irony features in the text, we investigated the effects of incorporating this information to identify ironic spreaders. Using the emotion and personality detection module, we were able to extract the author’s psychometric features. A pre-trained language model based on SBERT and T5-based architecture has been employed to extract context-based features. Our paper describes a framework using the author’s psychometric, contextual, and ironic features in a Gradient Boosting classifier based on our theory. Experimental results in this paper demonstrate the importance of this combination in identifying ironic spreader users. As a result, we were able to achieve an accuracy of 95.00% and 93.81% with 5-fold and 10-fold cross-validation respectively on the IROSTEREO training dataset. However, on the official PAN test set, our system attained a 88.89% score.  \nKeywords  \nStereotype, Author Profiling, Irony Detection, Sarcasm Detection, Language Psychology, Deep Learning  \n1. Introduction  \nSocial media networks have become a platform for people with different intellectual, ideological, and psychological characteristics to share their thoughts, opinions, and interests. In view of the high importance and comprehensiveness of the information published in these networks, it is urgent to develop automatic tools for processing it. Furthermore, users often use informal text in addition to the usual linguistic complexities such as slang, idioms, typos, and grammatical errors. By contrast, people often convey their meaning in complex ways. Using Figurative Language (FL) is one way to emphasize one’s point of view.  \nFL utilizes linguistic features such as sarcasm and irony, which are common in user generated content on platforms including Facebook and Twitter. Despite some attempts to provide a good definition of irony, there is still no consensus in the research community. But almost all  \n*  \nThese authors contributed equally to this work  \nCLEF 2022 – Conference and Labs of the Evaluation Forum, September 5 –8, 2022, Bologna, Italy  \n$ [ehsan.tavan@partdp.ai](ehsan.tavan@partdp.ai) (E. Tavan*); [maryam.najafi@partdp.ai](maryam.najafi@partdp.ai) (M. Najafi*); [rezymo@gmail.com](rezymo@gmail.com) (R. Moradi*)  \n􀀚 0000-0003-1262-8172 (E. Tavan*); 0000-0002-0877-7063 (M. Najafi*); 0000-0003-0372-6993 (R. Moradi*)  \n © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) .  \nCWPEURorkroceshopedings http://ceurISSN 1613-ws-0073.org CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org))  \nresearchers agree on two ironic features. First, it is unclear exactly what the author meant by his words. In fact, it uses words contrary to the original meaning. The second is that irony arises from opinions, emotions, feelings, and thoughts [1, 2, 3, 4, 1] .  \nIrony and sarcasm are two related concepts that are sometimes used interchangeably in the literature, but there are subtle differences between them [1, 5] . Sarcasm’s rarity, infrequency, difficulty in detection, and ambiguous meaning are its most challenging aspects. A positive word that is used in sarcasm can imply a negative meaning. For example, in \"If voting by mail is good enough for Trump, it is good enough for me.\" despite the use of positive words, polarity is negative due to th","cbCaitBgqGy5gtsc","https://ap.wps.com/l/cbCaitBgqGy5gtsc","pdf",1724351,11,"English","# Abstract\n# Keywords\n# 1. Introduction\n## Figurative Language and irony/sarcasm distinctions\n## PAN and IROSTEREO tasks in author profiling\n## User representation and proposed three-module framework","[{\"question\":\"What problem does the paper address on Twitter?\",\"answer\":\"It determines whether specific Twitter users consistently spread ironic content.\"},{\"question\":\"How does the framework extract psychometric features?\",\"answer\":\"It uses an emotion and personality detection module to extract the author’s psychometric characteristics.\"},{\"question\":\"What models are used for contextual and irony-related features?\",\"answer\":\"Context-based features come from an SBERT and T5-based pretrained language model architecture, supported by contextual and irony embedding modules.\"}]","Identifying Ironic Content Spreaders on Twitter using Psychometrics, Contextual and Ironic Features with Gradient Boosting Classifier - Conference Paper | PDF",28]