The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. The second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital-trace and textual data, as well as probability-, non-probability-, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. The first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. The second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital-trace and textual data, as well as probability-, non-probability-, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

The SAGE Handbook of Social Media Research Methods spans the entire research process, from data collection to analysis and interpretation. This second edition has been comprehensively updated and expanded, from 39 to 49 chapters. In addition to a new section of chapters focussing on ethics, privacy and the politics of social media data, the new edition provides broader coverage of topics such as: Data sources Scraping and spidering data Locative data, video data and linked data Platform-specific analysis Analytical tools Critical social media analysis Written by leading scholars from across the globe, the chapters provide a mix of theoretical and applied assessments of topics, and include a range of new case studies and data sets that exemplify the methodological approaches. This Handbook is an essential resource for any researcher or postgraduate student embarking on a social media research project. PART 1: Conceptualising and Designing Social Media Research PART 2: Collecting Data PART 3: Qualitative Approaches to Social Media Data PART 4: Quantitative Approaches to Social Media Data PART 5: Diverse Approaches to Social Media Data PART 6: Research & Analytical Tools PART 7: Social Media Platforms PART 8: Privacy, Ethics and Inequalities

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks.

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks. 4. Covers important and emergent topics in the field like the relationship between CSS, AI and machine learning.

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks. 4. Covers important and emergent topics in the field like the relationship between CSS, AI and machine learning.

1. Very comprehensive and extensive coverage (stresses the relevance of the entire research cycle, from design to data collection to analysis to interpretation). 2. Highlights the multidisciplinary nature of CSS, drawing from research in computer science, statistics, and the social and behavioural sciences. 3. Takes a holistic approach to CSS methods. Instead of focusing on simply harvesting data, the editors emphasise the importance of a carefully crafted research design containing key milestone checks.