Social Media Data Analysis

Dates: 16 February 2021, 26 May 2021

Time: 9:30am - 17:00pm

Instructor: Mike Thelwall

Level: Introductory 

Fee: £60

Location: Online


This course describes how to use free Windows software Mozdeh to gather tweets and to download comments on YouTube videos. The course will also describe simple and big data-style text analysis methods to gain insights into the meaning of the downloaded texts and to identify patterns within the data. Analysis methods will cover identifying topic, gender, time and sentiment differences in tweets or comments.


You will learn to use the free Mozdeh Windows software to:

  • Learn how to gather social media texts from Twitter and YouTube.
  • Learn simple techniques for interrogating the downloaded texts to gain insights into topics of discussion.
  • Learn simple data mining techniques to identify trends and patterns in the data.
  • Learn about the limitations of social media data analysis.

Participants will learn to use the free Mozdeh software to:

  • Gather tweets from specified users or matching a set of keyword queries.
  • Gather comments on one or more YouTube videos.
  • Simple quantitative methods in Mozdeh, such as word frequency analysis, gender difference detection, sentiment analysis and time-series graphs.
  • Word frequency comparison methods in Mozdeh to data-mine patterns in the texts.


Participants should have a basic familiarity with YouTube and Twitter, and be prepared to learn to use new software. Familiarity with Microsoft Windows is needed.

Recommended reading

  • Thelwall, M., Makita, M., Mas-Bleda, A. & Stuart, E. (2021). “My ADHD hellbrain”: A Twitter data science perspective on a behavioural disorder. Journal of Data and Information Science, 6(1). 
  • Thelwall, M. (2018). Social media analytics for YouTube comments: Potential and limitations. International Journal of Social Research Methodology, 21(3), 303-316.
  • Thelwall, M. & Mas-Bleda, A. (2018). YouTube science channel video presenters and comments: Female-friendly or vestiges of sexism? Aslib Journal of Information Management, 70(1), 28-46.
  • Thelwall, M. & Cugelman, B. (2017). Monitoring Twitter strategies to discover resonating topics: the case of the UNDP. El Profesional de la Información, 26(4), 649-661.

About the instructor

Mike Thelwall is a professor of Data Science and head of the Statistical Cybermetrics Research Group at the University of Wolverhampton. He researching big data, webometrics, social media metrics, and sentiment analysis; developing quantitative web methods for Twitter, social networks, YouTube, and various types of link and impact metrics; conducting impact assessments for organisations, such as the UNDP.