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Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation

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Cover of 'Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation'

Table of Contents

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    Book Overview
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    Chapter 1 Crowd Behavior Modeling in Emergency Evacuation Scenarios Using Belief-Desire-Intention Model
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    Chapter 2 Entering Their World: Using Social Media to Support Students in Modern Times
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    Chapter 3 Utilizing Multilingual Social Media Analysis for Food Venue Recommendation
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    Chapter 4 Simplifying E-Commerce Analytics by Discovering Hidden Knowledge in Big Data Clickstreams
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    Chapter 5 Event Detection on Communities: Tracking the Change in Community Structure within Temporal Communication Networks
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    Chapter 6 Chasing Undetected Fraud Processes with Deep Probabilistic Networks
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    Chapter 7 User’s Research Interests Based Paper Recommendation System: A Deep Learning Approach
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    Chapter 8 Characterizing Behavioral Trends in a Community Driven Discussion Platform
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    Chapter 9 Mining Habitual User Choices from Google Maps History Logs
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    Chapter 10 Semi-Automatic Training Set Construction for Supervised Sentiment Analysis in Polarized Contexts
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    Chapter 11 Detecting Clickbait on Online News Sites
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    Chapter 12 A Model-Based Approach for Mining Anomalous Nodes in Networks
Attention for Chapter 6: Chasing Undetected Fraud Processes with Deep Probabilistic Networks
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Chapter title
Chasing Undetected Fraud Processes with Deep Probabilistic Networks
Chapter number 6
Book title
Putting Social Media and Networking Data in Practice for Education, Planning, Prediction and Recommendation
Published by
Springer, Cham, December 2019
DOI 10.1007/978-3-030-33698-1_6
Book ISBNs
978-3-03-033697-4, 978-3-03-033698-1
Authors

Christophe Thovex, Thovex, Christophe

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