Handbook of Computational Social Science, Volume 1: Theory, Case Studies and EthicsUwe Engel, Anabel Quan-Haase, Sunny Liu, Lars E Lyberg Routledge, 10.11.2021 - 416 Seiten 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. This 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 in 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 scientifi c and engineering sectors. |
Inhalt
Analytical sociology amidst a computational social science revolution | |
Computational cognitive modeling in the social sciences | |
lessons from working group sessions with | |
A anging survey landscape | |
modes of data collection applications and errors at a glance | |
a call for pedagogy | |
an agenda to include stakeholder input on municipal big | |
Analysis of the principled AI frameworks constraints in becoming a methodological | |
Sensing closerange proximity for studying facetoface interaction | |
Social media data in affective science | |
using Twier to map the U S 2016 Democratic | |
e social influence of bots and trolls in social media | |
the year in review | |
Open computational social science | |
Causal and predictive modeling in computational social science | |
Datadriven agentbased modeling in computational social science | |
the impact of appearance and aracteristic | |
insights from a Delphi study | |
Public opinion formation on the far right | |