[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-381348-105":53,"doc-detail-381348-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","making-sense-of-social-media-streams-through-semantics-a-survey","Making Sense of Social Media Streams through Semantics: a Survey","","Using semantic technologies to mine social media and enable intelligent information access addresses an emerging, difficult research problem. Conventional search cannot handle the increasingly complex information-seeking behavior that evolves into sense making, learning, investigation, and social search within media streams. Social streams differ from curated news by being large-scale, short-lived, noisy, context-dependent, and dynamic.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/making-sense-of-social-media-streams-through-semantics-a-survey/381348/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/making-sense-of-social-media-streams-through-semantics-a-survey/381348.png","ImageObject",442,249,{"name":88,"@type":89},"Stanley","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-19","2026-09-24",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",7,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"Why are traditional search methods insufficient for social media streams?","Question",{"text":109,"@type":110},"Social media streams create complex information-seeking behavior such as sense making, learning, investigation, and social search, which traditional search methods cannot address effectively.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"What are the paper’s five key research questions?",{"text":114,"@type":110},"They cover ontologies and Web of Data for representing semantics, semantic annotation for implicit meaning, extracting reliable information from noisy streams, modelling users’ digital identity and activities, and semantic-based information access for complex user behavior.",{"name":116,"@type":107,"acceptedAnswer":117},"How is the survey organized across the paper?",{"text":118,"@type":110},"It starts with background on social media (section 2), then ontologies (section 3), semantic annotation methods (section 4), user/network/activity modelling and personalization (section 5), intelligent information access (section 6), and concludes with outstanding challenges and future directions (section 7).","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},381348,1791744595,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":8},2336477405376,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Semantic Web 0 (0) 1 1  \nIOS Press  \nMaking Sense of Social Media Streams through Semantics: a Survey  \nEditor(s): Andreas Hotho, University of Würzburg, Germany  \nSolicited review(s): Harald Sack, Universität Potsdam, Germany; Ashutosh Jadhav, Wright State University, USA  \nOpen review(s): Anonymous Reviewer  \nKalina Bontcheva a ;􀀃 Dominic Rout a  \na Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield, United Kingdom  \nE-mail: [Initial.Surname@dcs.shef.ac.uk](Initial.Surname@dcs.shef.ac.uk)  \nAbstract.  \nUsing semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature.  \nThis paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches for mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.  \nKeywords: semantic annotation, semantic-based user modelling, semantic search, information visualisation, social media streams  \n1. Introduction  \nThe widespread adoption of social media is based on tapping into the social nature of human interactions, by making it possible for people to voice their opinion, become part of a virtual community and collaborate remotely. If we take micro-blogging as an example, Twitter has 100 million active users, posting over 230 million tweets a day 1.  \nEngaging actively with such high-value, high-volume, brief life-span media streams has now become a daily challenge for both organisations and ordinary people. Automating this process through intelligent, semantic-  \n*[Corresponding author. E-mail: K.Bontcheva@dcs.shef.ac.uk](Corresponding author. E-mail: K.Bontcheva@dcs.shef.ac.uk).  \n1[http://www.guardian.co.uk/technology/pda/2011/sep/08/twitter](http://www.guardian.co.uk/technology/pda/2011/sep/08/twitter)active-users (Visited May 7, 2012)  \nbased information access methods is therefore increasingly needed. This is an emerging research area, combining methods from many fields, in addition to semantic technologies, namely natural language processing, social science, machine learning, personalisation, and information retrieval.  \nTraditional search methods are no longer able to address the more complex information seeking behaviour in social media, which has evolved towards sense making, learning and investigation, and social search [107] . Semantic technologies have the potential to help people cope better with social media-induced information overload. Automatic semantic-based methods that adapt to individual's information seeking goals and summarise briefly the relevant social media, could ultimately support information interpretation and decision making over large-scale, dynamic media streams.  \n1570-0844/0-1900/$27.50 􀀍c 0 – IOS Press and the authors. All rights reserved  \n2 Bontcheva and Rout / Semantics of Social Media Streams  \nUnlike carefully authored news and other textual web content, social media streams pose a number of new challenges for semantic technologies, due to their large-scale, noisy, irregular, and social nature. In this paper we discuss the following key research questions, examined through a survey of state-of-the-art approaches:  \n1. What ontologies and Web of Data resources ca","cbCaiszmLZMcMvts","https://ap.wps.com/l/cbCaiszmLZMcMvts","pdf",1427179,31,"English","# Introduction\n## Social Media Streams: Characteristics, Challenges and Opportunities","[{\"question\":\"Why are traditional search methods insufficient for social media streams?\",\"answer\":\"Social media streams create complex information-seeking behavior such as sense making, learning, investigation, and social search, which traditional search methods cannot address effectively.\"},{\"question\":\"What are the paper’s five key research questions?\",\"answer\":\"They cover ontologies and Web of Data for representing semantics, semantic annotation for implicit meaning, extracting reliable information from noisy streams, modelling users’ digital identity and activities, and semantic-based information access for complex user behavior.\"},{\"question\":\"How is the survey organized across the paper?\",\"answer\":\"It starts with background on social media (section 2), then ontologies (section 3), semantic annotation methods (section 4), user/network/activity modelling and personalization (section 5), intelligent information access (section 6), and concludes with outstanding challenges and future directions (section 7).\"}]","Making Sense of Social Media Streams through Semantics: a Survey | PDF",1790244934]