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.

Autorentext

Mike Thelwall is Professor of Information Science and leader of the Statistical Cybermetrics Research Group at the University of Wolverhampton, which he joined in 1989. He is also Docent at the Department of Information Studies at Abo Akademi University, and a research associate at the Oxford Internet Institute. His Ph.D. was in Pure Mathematics from the University of Lancaster but he is now a social scientist focusing on quantitative methods. Mike has developed a wide range of methods for gathering and analysing web indicator data, most of which are available in the free software Webometric Analyst. His 550 publications include 280 refereed journal articles, 23 book chapters and two books, including Introduction to Webometrics. He is an associate editor of the Journal of the Association for Information Science and Technology and sits on three other editorial boards. He led the Wolverhampton contribution to the EU-funded projects Acumen, CyberEmotions RESCAR, CREEN, NetReAct, Rindicateand Wiser, and has been funded for research by JISC and non-profit organisations in the UK and Italy. He has also conducted evaluation contracts for the EC (several times), the UNDP (several times), the UNFAO and a UN university and was a member of the UKs independent review of the role of metrics in research assessment.

Inhalt
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.