Generative Methods for Social Media Analysis

Description

This book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks. The book is divided into five chapters and provides an extensive bibliography consisting of more than 250 papers. After a quick introduction and survey of the book in the first chapter, chapter 2 is devoted to the discussion of data models and ontologies for social network analysis. Next, chapter 3 deals with text generation and generative text models and the dangers they pose to social media and society at large. Chapter 4 then focuses on topic modelling and sentiment analysis in the context of social networks. Finally, Chapter 5 presents graph theory tools and approaches to mine and model social networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified. The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications. Acknowledgments Contents 1 Introduction 2 Ontologies and Data Models for Cross-platform Social Media Data 2.1 Data Models for Social Media Data Analysis Homophily Analysis Social Identity Linkage Personality Analysis 2.2 Ontologies for Social Media Data Ontologies for Sentiment Analysis Ontologies for Situational Awareness 2.3 Potential Future Research Topics Metadata Federated Learning 3 Methods for Text Generation in NLP 3.1 Introduction 3.2 Past Approaches 3.3 GANs in NLP Reinforcement learning strategies Operating on continuous representations instead of discrete symbols Gumbel-softmax 3.4 Large Neural Language Models (LNLMs or LLMs) The Transformer and BERT BERT variants Introduction to GPT-3 3.5 Dangers of E ective Generative LLMs Marginalized Group and Gender Bias Generation of Hateful Content De-biasing Approaches Environmental and Financial Impacts Identifying Information Extraction Attacks Simpler Approaches Potential Research Direction # 1 (Large Neural Language Models) 3.6 Detecting Generated Text Overview Detection of Machine-Generated Text The Issue with Simple Detection Detection of Fake News Content Issues of Comparison and Dataset Standardization Content-based Approaches Social-response-based Approaches Hybrid Approaches Graph-based Approaches Multimodal Approaches: Incorporating Visual Information Potential Research Direction # 2 (Fake News Detection) 4 Topic and Sentiment Modelling for Social Media 4.1 Introduction 4.2 Introduction to Topic Modelling 4.3 Overview of Classical Approaches to Topic Modelling LDA 4.4 Neural Topic Modelling Variational Topic Modelling LDA2Vec Top2Vec Use of Pre-trained Embeddings for Neural Topic Modelling Neural Topic Modelling for Social Media Potential Research Direction # 3 (Extending NTMs) 4.5 Sentiment Analysis Sentiment Analysis and Stance Detection Standardized Datasets Traditional Supervised Sentiment Analysis Multimodal Sentiment Analysis Potential Research Direction # 4 (Textual Sentiment Analysis over Time) Aspect-based Sentiment Analysis ASBA in a Uni ed Framework Potential Research Direction # 5 (Aspect-based Multimodal Sentiment Analysis) 5 Mining and Modelling Complex Networks 5.1 Node Embeddings Hyperbolic Spaces Signed Networks Potential Research Direction # 6 (Embedding Sequences of Graphs) Potential Research Direction # 7 (Multi-Layered Graphs) 5.2 Evaluating Node Embeddings Potential Research Direction # 8 (Selecting an Appropriate Embedding for a Given Task at Hand—Supervised vs. Unsupervised Approach) 5.3 Community Detection Potential Research Direction # 9 (More General Community Detection and Using Several Sources of Information) 5.4 Hypergraphs Potential Research Direction # 10 (Hypergraph Modularity Function) 5.5 Understanding the Dynamics of Networks Human-bot Interaction and Spread of Misinformation Social Bursts in Collective Attention Social Learning (Segregation, Polarization) Potential Research Direction # 11 (Tools Based on the Null-models) 5.6 Generating Synthetic Networks Potential Research Direction # 12 (Generating Synthetic Higher-order Structures) 6 Conclusions References