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001450524 001__ 1450524
001450524 003__ OCoLC
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001450524 019__ $$a1348489385
001450524 020__ $$a9783031082429$$q(electronic bk.)
001450524 020__ $$a3031082427$$q(electronic bk.)
001450524 020__ $$z9783031082412
001450524 020__ $$z3031082419
001450524 0247_ $$a10.1007/978-3-031-08242-9$$2doi
001450524 035__ $$aSP(OCoLC)1348380635
001450524 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dN$T$$dUKAHL
001450524 049__ $$aISEA
001450524 050_4 $$aHM742
001450524 08204 $$a302.230285$$223/eng/20221031
001450524 24500 $$aSocial media analysis for event detection /$$cTansel Özyer, editor.
001450524 264_1 $$aCham :$$bSpringer,$$c[2022]
001450524 264_4 $$c©2022
001450524 300__ $$a1 online resource (vi, 229 pages) :$$billustrations.
001450524 336__ $$atext$$btxt$$2rdacontent
001450524 337__ $$acomputer$$bc$$2rdamedia
001450524 338__ $$aonline resource$$bcr$$2rdacarrier
001450524 4901_ $$aLecture notes in social networks
001450524 5050_ $$aIntro -- Contents -- A Network-Based Approach to Understanding International Cooperation in Environmental Protection -- 1 Introduction -- 2 Concepts and Methodology -- 2.1 Network Data -- 2.2 Research Design and Methods -- 3 Research Findings -- 3.1 One-Mode Analysis of Environmental Agreements and Participating Parties -- 3.2 Two-Mode Analysis of Environmental Agreements and Participating Parties -- 4 Discussion and Future Work -- References -- Critical Mass and Data Integrity Diagnostics of a Twitter Social Contagion Monitor -- 1 Introduction -- 2 Background
001450524 5058_ $$a2.1 Social Movements and Contagion Models -- 2.2 Social Movements and Social Media -- 2.3 Social Media Monitoring Tools -- 3 Methods and Data -- 3.1 IRB Compliance -- 3.2 Risks of Collecting Data on Social Movements -- 3.3 Data Ingestion -- 3.3.1 Streaming Contagion Monitor -- 3.3.2 Regional Contagion Monitor -- 3.3.3 Productionalized Streaming Contagion Monitor -- 3.4 Candidate Hashtag Selection -- 3.4.1 Streaming Contagion Monitor -- 3.4.2 Regional Contagion Monitor -- 3.4.3 Productionalized Contagion MonitorTM -- 3.5 Contagion Analysis -- 3.5.1 Streaming Contagion Monitor
001450524 5058_ $$a3.5.2 Regional Contagion Monitor -- 3.5.3 Productionalized Contagion Monitor -- 3.6 Reporting -- 3.7 Coordination Framework -- 3.7.1 Introduction -- 3.7.2 Implementation: Data Ingestion -- 3.7.3 Implementation: Outputs -- 3.7.4 Integration -- 4 Results -- 4.1 Framework for Evaluating the Contagion Monitors -- 4.2 Evaluation of Complexity of Contagion (R1) -- 4.2.1 Mechanical Turk Annotation -- 4.2.2 Analysis of Annotated Hashtags -- 4.2.3 Spam Filtering -- 4.2.4 Linear Regression -- 4.2.5 Classification -- 4.3 Analysis of Critical Mass for Virality (R2) -- 4.4 Analysis of Data Size (R3)
001450524 5058_ $$a4.5 A Fruitful Case Study -- 4.6 Results with Coordination Framework Metrics -- 5 Discussion and Conclusion -- Appendix -- References -- TenFor: Tool to Mine Interesting Events from Security Forums Leveraging Tensor Decomposition -- 1 Introduction -- 2 Background and Datasets -- 2.1 Datasets -- 2.2 Background -- 3 Our Approach -- 3.1 Step 1: Tensor-Based Clustering -- 3.2 Step 2: Profiling the Clusters -- 3.3 Step 3: Investigation of Clusters -- 4 Results and Evaluation -- 4.1 Step by Step Output Provided by TenFor -- 4.2 Evaluation of TenFor with Real Data
001450524 5058_ $$a4.3 Evaluation of TenFor with Synthetic Data -- 5 Discussion -- 6 Related Work -- 7 Conclusion -- References -- Profile Fusion in Social Networks: A Data-Driven Approach -- 1 Introduction -- 2 Related Works -- 3 ULSN System Architecture -- 3.1 Re-Usability, Code and Implementation Details -- 4 Data -- 4.1 Data Validation -- 4.2 Dataset Overview -- 4.3 Content Analysis -- 4.4 Temporal Analysis -- 5 User Profile Linkage -- 5.1 Problem Formulation -- 5.2 User Matching Approach -- 5.3 Classification Approach -- 5.4 Clustering-Based Optimization -- 6 Experiments -- 6.1 User Matching Results
001450524 506__ $$aAccess limited to authorized users.
001450524 520__ $$aThis book includes chapters which discuss effective and efficient approaches in dealing with various aspects of social media analysis by using machine learning techniques from clustering to deep learning. A variety of theoretical aspects, application domains and case studies are covered to highlight how it is affordable to maximize the benefit of various applications from postings on social media platforms. Social media platforms have significantly influenced and reshaped various social aspects. They have set new means of communication and interaction between people, turning the whole world into a small village where people with internet connect can easily communicate without feeling any barriers. This has attracted the attention of researchers who have developed techniques and tools capable of studying various aspects of posts on social media platforms with main concentration on Twitter. This book addresses challenging applications in this dynamic domain where it is not possible to continue applying conventional techniques in studying social media postings. The content of this book helps the reader in developing own perspective about how to benefit from machine learning techniques in dealing with social media postings and how social media postings may directly influence various applications.
001450524 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 31, 2022).
001450524 650_0 $$aSocial media$$xData processing.
001450524 650_0 $$aDeep learning (Machine learning)
001450524 650_0 $$aArtificial intelligence.
001450524 655_0 $$aElectronic books.
001450524 7001_ $$aÖzyer, Tansel,$$eeditor.$$1https://isni.org/isni/0000000367466836
001450524 77608 $$iPrint version:$$z3031082419$$z9783031082412$$w(OCoLC)1317836125
001450524 830_0 $$aLecture notes in social networks.
001450524 852__ $$bebk
001450524 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-08242-9$$zOnline Access$$91397441.1
001450524 909CO $$ooai:library.usi.edu:1450524$$pGLOBAL_SET
001450524 980__ $$aBIB
001450524 980__ $$aEBOOK
001450524 982__ $$aEbook
001450524 983__ $$aOnline
001450524 994__ $$a92$$bISE