Word Association Thematic Analysis (Record no. 85090)

000 -LEADER
fixed length control field 03629nam a22004695i 4500
001 - CONTROL NUMBER
control field 978-3-031-02324-8
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730163901.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2021 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031023248
-- 978-3-031-02324-8
082 04 - CLASSIFICATION NUMBER
Call Number 004.6
100 1# - AUTHOR NAME
Author Thelwall, Michael.
245 10 - TITLE STATEMENT
Title Word Association Thematic Analysis
Sub Title A Social Media Text Exploration Strategy /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVII, 111 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Information Concepts, Retrieval, and Services,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Acknowledgments -- Introduction -- Data Collection with Mozdeh -- Word Association Detection: Term Identification -- Word Association Contextualization: Term Meaning and Context -- Word Association Thematic Analysis: Theme Detection -- Word Association Thematic Analysis Examples -- Comparison Between WATA and Other Methods -- Ethics -- Project Planning -- Summary -- References -- Author Biography.
520 ## - SUMMARY, ETC.
Summary, etc Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection. The method starts by automatically finding words that are statistically significantly more common in one subset than another, thenidentifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages. This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-02324-8
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Koha item type eBooks
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-- Springer International Publishing :
-- Imprint: Springer,
-- 2021.
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-- computer
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-- online resource
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer networks .
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Communication Networks.
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-- 1947-9468
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