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001449394 019__ $$a1343998970
001449394 020__ $$a9783031078699$$q(electronic bk.)
001449394 020__ $$a3031078691$$q(electronic bk.)
001449394 020__ $$z3031078683
001449394 020__ $$z9783031078682
001449394 0247_ $$a10.1007/978-3-031-07869-9$$2doi
001449394 035__ $$aSP(OCoLC)1344158393
001449394 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP$$dOCLCQ$$dN$T$$dOCLCF$$dOCLCQ
001449394 049__ $$aISEA
001449394 050_4 $$aRA652.2.P82
001449394 08204 $$a614.4$$223/eng/20220915
001449394 24500 $$aDisease control through social network surveillance /$$cThirimachos Bourlai, Panagiotis Karampelas, Reda Alhajj, editors.
001449394 260__ $$aCham :$$bSpringer,$$c2022.
001449394 300__ $$a1 online resource (237 pages)
001449394 336__ $$atext$$btxt$$2rdacontent
001449394 337__ $$acomputer$$bc$$2rdamedia
001449394 338__ $$aonline resource$$bcr$$2rdacarrier
001449394 4901_ $$aLecture notes in social networks
001449394 504__ $$aIncludes bibliographical references.
001449394 5050_ $$aIntro -- Preface -- Disease Control Through Social Network Surveillance -- Chapter Contributions -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Analysis of Public Perceptions Towards the COVID-19 Vaccination Drive: A Case Study of Tweets with Machine Learning Classifiers -- 1 Introduction -- 2 A Brief Literature Survey -- 3 Data Collection, Pre-processing and Methodology -- 3.1 Raw-Data Acquisition -- 3.2 Data Pre-processing -- 3.3 Data Visualization -- 3.3.1 Geographical Analytics of Tweets -- 3.3.2 Users with Maximum Tweets -- 3.3.3 Most Frequent Hashtags
001449394 5058_ $$a3.3.4 Monthly Statistics of Tweets -- 3.3.5 Textual Analysis of Tweets -- 3.3.6 Word and Phrase Associations -- 4 Experimental Design, Results and Discussions -- 4.1 Feature Selection -- 4.2 Platform Employed and Performance Evaluation Parameters -- 5 Conclusions -- References -- Spreader-Centric Fake News Mitigation Framework Based on Epidemiology -- 1 Introduction -- 2 Related Work -- 3 Epidemiology Inspired Framework -- 4 Preliminaries -- 4.1 Trustingness and Trustworthiness -- 4.2 Believability -- 4.3 Community Health Assessment Model -- 5 Vulnerability Assessment
001449394 5058_ $$a6 Identification of Infected Population -- 7 Risk Assessment of Population -- 8 Infection Control and Prevention -- 9 Conclusion -- References -- Understanding How Readers Determine the Legitimacy of Online Medical News Articles in the Era of Fake News -- 1 Introduction -- 2 Background and Related Work -- 2.1 Presentation and Content in True and Fake News Articles -- 2.2 Detecting Fake News Articles: The Reader's Side -- 3 Methodology -- 3.1 Survey 1 -- 3.2 Survey 2 -- 3.3 Survey 3 -- 3.4 Clustering Analysis -- 4 Results -- 4.1 Survey 1 -- 4.2 Survey 2 -- 4.3 Survey 3 -- 5 Discussion
001449394 5058_ $$a6 Conclusion -- References -- Trends, Politics, Sentiments, and Misinformation: Understanding People's Reactions to COVID-19 During Its Early Stages -- 1 Introduction -- 1.1 Contributions -- 1.2 Organization -- 2 Related Work -- 3 Reactions to COVID-19 During its Early Stages: Social Media Analytics -- 3.1 Dataset and Implementation Environment -- 3.2 Analysis Results -- 3.2.1 Number of Posts Related to COVID-19 Over Time -- 3.2.2 Number of Published News Per Web Site, Per Month -- 3.2.3 Geographic Distribution of Shared News -- 3.2.4 Geographic and Temporal Trends in Fake News
001449394 5058_ $$a3.2.5 Opinions About Public Figures -- 4 Conclusion -- References -- Citation Graph Analysis and Alignment Between Citation Adjacency and Themes or Topics of Publications in the Area of Disease Control Through Social Network Surveillance -- 1 Introduction -- 2 Literature Review -- 3 The Citation Graph Methodology -- 4 Data -- 4.1 Data Collection -- 4.2 Derived Networks -- 5 Discussion of Nodal Attributes of the DCSNS Citation Graph -- 5.1 Degrees -- 5.2 Types -- 5.3 Themes -- 5.4 Topics -- 5.5 Relationships Between Nodal Attributes -- 5.6 Degree and Attribute Assortativities -- 6 Conclusions
001449394 506__ $$aAccess limited to authorized users.
001449394 520__ $$aThis book examines modern paradigms of disease control based on social network surveillance applications, including electronic sentinel surveillance and wireless application-based surveillance science. It also highlights topics that integrate statistical and epidemiological sciences with surveillance practice and, in order to reflect the evolution of social networking practices, discusses topics concerning the challenges for surveillance theory and practice. In turn, the book goes a step further by providing insights on how we need to analyse epidemiological trends by following best practices on distinguishing useful information from noise, namely fake news, false reporting of disease incidents and events, etc. At the same time, we need to be able to protect health-focused applications and communication tools via cybersecurity technologies and to ensure that anonymity of reporting and privacy are preserved. In closing, the book discusses the role and impact of social media on disease surveillance, as well as the current role of communities in infectious disease surveillance and control.
001449394 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 15, 2022).
001449394 650_0 $$aPublic health surveillance$$xData processing.
001449394 650_0 $$aOnline social networks.
001449394 655_0 $$aElectronic books.
001449394 7001_ $$aBourlai, Thirimachos.
001449394 7001_ $$aKarampelas, Panagiotis.
001449394 7001_ $$aAlhajj, Reda.
001449394 77608 $$iPrint version:$$aBourlai, Thirimachos.$$tDisease Control Through Social Network Surveillance.$$dCham : Springer International Publishing AG, ©2022$$z9783031078682
001449394 830_0 $$aLecture notes in social networks.
001449394 852__ $$bebk
001449394 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-07869-9$$zOnline Access$$91397441.1
001449394 909CO $$ooai:library.usi.edu:1449394$$pGLOBAL_SET
001449394 980__ $$aBIB
001449394 980__ $$aEBOOK
001449394 982__ $$aEbook
001449394 983__ $$aOnline
001449394 994__ $$a92$$bISE