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This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics
Network Structure Analysis
Node Classification Using Deep Learning in Social Networks
NN-LP-CF: Neural Network Based Link Prediction on Social Networks Using Centrality-Based Features
Social Media Text Analysis
Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review
Convolutional and Recurrent Neural Networks for Opinion Mining on Drug Reviews
Text-Based Sentiment Analysis Using Deep Learning Techniques
Social Sentiment Analysis Using Features Based Intelligent Learning Techniques
User Behaviour Analysis
Modified-PIP with Deep Neural Network (DNN) Architecture: A Coherent Recommendation Framework for Capturing User Behaviour
A Survey on Graph Neural Network Based Video Recommendation System
Characterisation of Mental Health Conditions in Social Media Using Deep Learning Techniques
Predicting Mental Health and Nutritional Status from Social Media Profile Using Deep Learning
Impact of Artificial Intelligence-Based Chatbots on Customer Engagement and Business Growth
Social Media Security Analysis
Do Not ‘Fake It Till You Make It’! Synopsis of Trending Fake News Detection Methodologies Using Deep Learning
Towards Detecting Fake Spammers Groups in Social Media: An Unsupervised Deep Learning Approach
A Deep Learning Approach for Anomalous User-Intrusion Detection in Social Media Network System
Deep Digging of Anomalous Transactions in Financial Networks with Imbalanced Data