Banner
Home      Log In      Contacts      FAQs      INSTICC Portal
 

Keynote Lectures

Word Association Thematic Analysis: Insight Discovery from the Social Web
Mike Thelwall, University of Wolverhampton, United Kingdom

Available Soon
Wolfgang Nejdl, L3S and University of Hannover, Germany

Available Soon
Riccardo Rosati, Sapienza Università di Roma, Italy

 

Word Association Thematic Analysis: Insight Discovery from the Social Web

Mike Thelwall
University of Wolverhampton
United Kingdom
 

Brief Bio
Mike Thelwall is Professor of Data Science and leads the Statistical Cybermetrics and Research Evaluation Group at the University of Wolverhampton in the UK. He researches sentiment analysis, science of science, and social web analysis methods. He has created software and methods for analysing Twitter, YouTube, and general web pages from a quantitative social science perspective. He has published 425 refereed journal articles and four books, including Word association thematic analysis: A social media text exploration strategy. He is an associate editor of the Journal of the Association for Information Science and Technology and sits on five other editorial boards.


Abstract
Billions of short messages are posted daily to the public social web. This gives opportunities for researchers to gain insights into the issues discussed, but extracting useful information is challenging. On the one hand, the simplifying quantitative approaches for large scale analysis risk misinterpreting the patterns found because of the many different uses of the social web. On the other hand, small scale qualitative investigations may miss the big picture and ignore most of the data. This talk describes a mixed methods approach, word association thematic analysis, that attempts to gain the face validity of small-scale qualitative investigations with the power of large-scale pattern detection. The method leverages comparisons to identify sets of characteristic words, then applies thematic analysis to group these words into patterns according to the context in which they are used. The comparisons can be temporal (e.g., early vs. late tweets), topic-based (e.g., vaxxers vs. antivaxxers), or tweeter-based (e.g., gender, location). The outcome of word association thematic analysis is a set of themes that characterise an issue in a social web site, supported by qualitative evaluations of the context of the words analysed and statistical tests for the validity of the differences identified. The method is supported by the free software mozdeh.wlv.ac.uk and the talk will give a range of examples from YouTube and Twitter.



 

 

Keynote Lecture

Wolfgang Nejdl
L3S and University of Hannover
Germany
 

Brief Bio
Available Soon


Abstract
Available Soon



 

 

Keynote Lecture

Riccardo Rosati
Sapienza Università di Roma
Italy
 

Brief Bio
Available Soon


Abstract
Available Soon



footer