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Big data and social media analyticst...
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Cakirtas, Mehmet.
Big data and social media analyticstrending applications /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Big data and social media analyticsedited by Mehmet Cakirtas, Mehmet Kemal Ozdemir.
其他題名:
trending applications /
其他作者:
Cakirtas, Mehmet.
出版者:
Cham :Springer International Publishing :2021.
面頁冊數:
vi, 245 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
標題:
Big data.
電子資源:
https://doi.org/10.1007/978-3-030-67044-3
ISBN:
9783030670443$q(electronic bk.)
Big data and social media analyticstrending applications /
Big data and social media analytics
trending applications /[electronic resource] :edited by Mehmet Cakirtas, Mehmet Kemal Ozdemir. - Cham :Springer International Publishing :2021. - vi, 245 p. :ill., digital ;24 cm. - Lecture notes in social networks,2190-5428. - Lecture notes in social networks..
Chapter 1. Twenty Years of Network Science: A Bibliographic and Co-Authorship Network Analysis -- Chapter 2. Impact of Locational Factors on Business Ratings/Reviews: A Yelp and TripAdvisor Study -- Chapter 3. Identifying Reliable Recommenders in Users' Collaborating Filtering and Social Neighbourhoods -- Chapter 4. Safe Travelling Period Recommendation to High Attack Risk European Destinations based on Past Attack Information -- Chapter 5. Analyzing Cyber Influence Campaigns on YouTube using YouTubeTracker -- Chapter 6. Blog Data Analytics Using Blogtrackers -- Chapter 7. Using Social Media Surveillance in order to Enhance the Effectiveness of Crew Members in Search and Rescue Missions -- Chapter 8. Visual Exploration and Debugging of Machine Learning Classification over Social Media Data -- Chapter 9. Efficient and Flexible Compression of Very Sparse Networks of Big Data -- Chapter 10. Weather Big Data Analytics: Seeking Motifs in Multivariate Weather Data -- Chapter 11. Analysis of Link Prediction Algorithms in Hashtag Graphs.
This edited book provides techniques which address various aspects of big data collection and analysis from social media platforms and beyond. It covers efficient compression of large networks, link prediction in hashtag graphs, visual exploration of social media data, identifying motifs in multivariate data, social media surveillance to enhance search and rescue missions, recommenders for collaborative filtering and safe travel plans to high risk destinations, analysis of cyber influence campaigns on YouTube, impact of location on business rating, bibliographical and co-authorship network analysis, and blog data analytics. All these trending topics form a major part of the state of the art in social media and big data analytics. Thus, this edited book may be considered as a valuable source for readers interested in grasping some of the most recent advancements in this high trending domain.
ISBN: 9783030670443$q(electronic bk.)
Standard No.: 10.1007/978-3-030-67044-3doiSubjects--Topical Terms:
609582
Big data.
LC Class. No.: QA76.9.B45
Dewey Class. No.: 005.7
Big data and social media analyticstrending applications /
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Chapter 1. Twenty Years of Network Science: A Bibliographic and Co-Authorship Network Analysis -- Chapter 2. Impact of Locational Factors on Business Ratings/Reviews: A Yelp and TripAdvisor Study -- Chapter 3. Identifying Reliable Recommenders in Users' Collaborating Filtering and Social Neighbourhoods -- Chapter 4. Safe Travelling Period Recommendation to High Attack Risk European Destinations based on Past Attack Information -- Chapter 5. Analyzing Cyber Influence Campaigns on YouTube using YouTubeTracker -- Chapter 6. Blog Data Analytics Using Blogtrackers -- Chapter 7. Using Social Media Surveillance in order to Enhance the Effectiveness of Crew Members in Search and Rescue Missions -- Chapter 8. Visual Exploration and Debugging of Machine Learning Classification over Social Media Data -- Chapter 9. Efficient and Flexible Compression of Very Sparse Networks of Big Data -- Chapter 10. Weather Big Data Analytics: Seeking Motifs in Multivariate Weather Data -- Chapter 11. Analysis of Link Prediction Algorithms in Hashtag Graphs.
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