This document provides an overview of webometrics and sentiment analysis techniques. It discusses using tools like Webometric Analyst to gather data from sites like YouTube, Twitter, and blogs. Sentiment analysis can study sentiment in YouTube comments and major media events on Twitter. Networks of YouTube video replies can reveal discussion patterns and demographic information. Large-scale YouTube analysis can discover usage patterns and behaviors.

1. Introduction to Webometrics Mike Thelwall @mikethelwall Professor of Information Science, Statistical Cybermetrics Research Gp, University of Wolverhampton

2. Reminder of pre-workshop task Delegates were asked to join YouTube and leave comments and replies to earlier comments on the video: Department of Library and Information Science, Delhi http://www.youtube.com/watch?v=_-OAsF9uRfc These contributions will form part of the discussion at the end of the session, and include reference to the self-declared age and gender information from YouTube.

7. Example: The online impact of research groups (NetReAct)

8. Normalised linking, smallest countries removed Geopolitical connected Sweden Finland Norway UK Germany Austria Switzerland Poland Italy Belgium Spain France NL Example: Links between EU universities

9. International biofuels research network

22. Tests against human coders SentiStrength agrees with humans as much as they agree with each other 1 is perfect agreement, 0 is random agreement Data set Positive scores -correlation with humans Negative scores -correlation with humans YouTube 0.589 0.521 MySpace 0.647 0.599 Twitter 0.541 0.499 Sports forum 0.567 0.541 Digg.com news 0.352 0.552 BBC forums 0.296 0.591 All 6 data sets 0.556 0.565

25. Automatically-identified Twitter spikes 9 Mar 2010 9 Feb 2010 Proportion of tweets mentioning keyword Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events . Journal of the American Society for Information Science and Technology, 62(2), 406-418.

26. Chile matching posts Sentiment strength Subj. Increase in –ve sentiment strength 9 Feb 2010 9 Feb 2010 Date and time Date and time 9 Mar 2010 9 Mar 2010 Av. +ve sentiment Just subj. Av. -ve sentiment Just subj. Proportion of tweets mentioning Chile

27. #oscars % matching posts Sentiment strength Subj. Increase in –ve sentiment strength Date and time Date and time 9 Feb 2010 9 Feb 2010 9 Mar 2010 9 Mar 2010 Av. +ve sentiment Just subj. Av. -ve sentiment Just subj. Proportion of tweets mentioning the Oscars

33. Reply network Extended core interactions 2x2=5 video Nodes (people) blue = male pink = female Arrows (replies) red = happy replies black = angry replies

37. Very sparse Friend network = common

38. Common Friends network – with a densely connected core

40. Comments are mainly positive

42. Controversial and non-controversial ?