Social Media Data Analysis
Date:
10/04/2015
Organised by:
University of Manchester
Presenter:
Professor Mike Thelwall
Level:
Intermediate (some prior knowledge)
Contact:
Short Courses Administrator; cmist-courses@manchester.ac.uk
Map:
View in Google Maps (M13 9PL)
Venue:
The University of Manchester, Manchester M13 9PL
Humanities Bridgeford Building.
Description:
Outline
Participants should have a basic familiarity with YouTube and Twitter, and be prepared to learn to use new software.
Objectives
The course will use the free Webometric Analyst software for the following purposes:
- Gathering tweets matching a geographic or keyword query
- Gathering comments on one or more YouTube videos
- Constructing network diagrams from users or comments
- The analysis methods discussed will include:
- Simple quantitative methods, such as social network analysis, to describe the results
- Content analysis to provide insights into the YouTube or Twitter topic studied
Prerequisites
None
Recommended Reading
Wilkinson, D. & Thelwall, M. (in press). Trending Twitter topics in English: An international comparison. Journal of the American Society for Information Science and Technology. Trending Twitter Topics in English: An International Comparison
Thelwall, M., Sud, P., & Vis, F. (2012). Commenting on YouTube videos: From Guatemalan rock to El Big Bang. Journal of the American Society for Information Science and Technology, 63(3), 616–629.
Thelwall, M. (2009). Introduction to webometrics: Quantitative web research for the social sciences. San Rafael, CA: Morgan & Claypool (Synthesis Lectures on Information Concepts, Retrieval, and Services, 2009, Vol. 1, No. 1).
Cost:
£195 (£140 for those from educational and charitable institutions)
Website and registration:
http://www.cmist.manchester.ac.uk/study/courses/short/intermediate/social-media-data-analysis/
Region:
North West
Keywords:
Data Collection, Sampling , Data Quality and Data Management , social media, data, data analysis
Related publications and presentations:
Data Collection
Sampling
Data Quality and Data Management