In: SpringerBriefs in Computer Science Series
Intro -- 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.
In: SpringerBriefs in computer science
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