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Internet marketing --- Online social networks --- #KVHA:Content marketing --- #KVHA:Economie --- #KVHA:Marketing --- Electronic social networks --- Social networking Web sites --- Virtual communities --- Social media --- Social networks --- Sociotechnical systems --- Web sites --- Online marketing --- Web marketing --- World Wide Web marketing --- Electronic commerce --- Marketing --- sociale media --- marketing --- Communities, Online (Online social networks) --- Communities, Virtual (Online social networks) --- Online communities (Online social networks)

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Learn everything you need to know about frameworks that help developers to integrate their solutions with social networks or APIs, from general purpose (Facebook, Twitter, Google, Mastodon) to specialized (LinkedIn, Xing, WhatsApp, YouTube, Instagram, Flickr, TikTok) to vertical (eToro, Fitbit, Strava). This book will teach you how to add social media features to web applications or services developed using Java, Jakarta EE, or generally running on a Java Virtual Machine (JVM). Jam-packed with practical examples of social integration into enterprise applications, you’ll learn how to address common requirements such as social login, identity federation, single sign-on via social accounts, OpenID Connect, and mashups. You’ll also see how to leverage Java social frameworks like Facebook Business SDK, Twitter4J, Agorava, Keycloak, and Spring Security. Enterprise Social for the Java Platform is an excellent companion to books covering Jakarta EE Security, Spring Security, portals, and related frameworks. Upon completing it, you’ll be armed with the expertise you need to integrate your own Java enterprise applications with social media networks. You will: Harness the reach and power of social media platforms by integrating your enterprise Java applications with them Understand social media standards for different platforms Address common security issues.

Computer science --- Programming --- computers --- programmeertalen --- Java (informatica) --- computerkunde --- Java (Computer program language) --- Application software. --- Online social networks.

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Social Media: The Academic Library Perspective provides a step-by-step guide on social media as written by somebody who has already done the work. Made up of case studies written by authors at various institutions who provide different perspectives on their institution's use of social media, the book highlights successes and failures, while also focusing on tips for social media management in the academic library that anybody in the community can interpret and adapt. Social media platforms are dealt with systematically, making this an essential guide for librarians who want to use social media to the benefit of their library.Includes a step-by-step guide on the use of social media for academic libraries. Presents practical experience leveraged in the form of case studies. Provides quick, concise, and systematic recommendations for the use of social media. Written by academic librarians for academic librarians Social Media: The Academic Library Perspective provides a step-by-step guide on social media as written by somebody who has already done the work. Made up of case studies written by authors at various institutions who provide different perspectives on their institution's use of social media, the book highlights successes and failures, while also focusing on tips for social media management in the academic library that anybody in the community can interpret and adapt. Social media platforms are dealt with systematically, making this an essential guide for librarians who want to use social media to the benefit of their library.

Library management --- Marketing --- Mass communications --- Online social networks --- Social media. --- Academic libraries --- Library applications. --- Information technology. --- Libraries and community. --- Social Media

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"Showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing"--Journal home page.

Human computation --- Online social networks --- Computer networks --- Computer networks. --- Human computation. --- Online social networks. --- Electronic social networks --- Social networking Web sites --- Crowdsourcing --- Human-based computation --- Human computation systems --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Social networks --- Social computing --- Mathematics of social networks --- Computational aspects of social networks --- Therory of social computing --- Social media --- Web sites --- Distributed artificial intelligence --- Human-computer interaction --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Electronic data processing --- Network computers --- Sociotechnical systems --- Crowdsourcing (Distributed artificial intelligence) --- Distributed processing --- social computing --- complex networks --- social media --- Virtual communities --- Computer Science --- Computer. Automation --- Communities, Online (Online social networks) --- Communities, Virtual (Online social networks) --- Online communities (Online social networks)

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Collective view prediction is to judge the opinions of an active web user based on unknown elements by referring to the collective mind of the whole community. Content-based recommendation and collaborative filtering are two mainstream collective view prediction techniques. They generate predictions by analyzing the text features of the target object or the similarity of users’ past behaviors. Still, these techniques are vulnerable to the artificially-injected noise data, because they are not able to judge the reliability and credibility of the information sources. Trust-based Collective View Prediction describes new approaches for tackling this problem by utilizing users’ trust relationships from the perspectives of fundamental theory, trust-based collective view prediction algorithms and real case studies. The book consists of two main parts – a theoretical foundation and an algorithmic study. The first part will review several basic concepts and methods related to collective view prediction, such as state-of-the-art recommender systems, sentimental analysis, collective view, trust management, the Relationship of Collective View and Trustworthy, and trust in collective view prediction. In the second part, the authors present their models and algorithms based on a quantitative analysis of more than 300 thousand users’ data from popular product-reviewing websites. They also introduce two new trust-based prediction algorithms, one collaborative algorithm based on the second-order Markov random walk model, and one Bayesian fitting model for combining multiple predictors. The discussed concepts, developed algorithms, empirical results, evaluation methodologies and the robust analysis framework described in Trust-based Collective View Prediction will not only provide valuable insights and findings to related research communities and peers, but also showcase the great potential to encourage industries and business partners to integrate these techniques into new applications.

Computer science --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- Bayesian statistics --- computers --- informatiesystemen --- database management --- KI (kunstmatige intelligentie) --- computerkunde --- robots --- data acquisition --- AI (artificiële intelligentie) --- Online social networks.