This document provides information on analyzing Twitter data for social science and humanities research. It discusses how to gather Twitter data using tools like Mozdeh, and what types of analysis can be done, including time series analysis to identify trends over time, sentiment analysis to determine high sentiment topics, and comparing differences between subsets by gender or keywords. Content analysis of tweets can provide quick insights into research topics. The document also describes the SentiStrength algorithm for detecting sentiment in informal short text.

1. Time series
Word frequencies
Sentiment analysis
Twitter Analysis for the Social
Sciences and Humanities

2. Twitter Data: Quick and Slow
1. Quick: http://topsy.com search, then click
2. Slow: Download Mozdeh
http://mozdeh.wlv.ac.uk,
collect data in advance
then analyse
Tweets per hour
including ‘Nobel’
Tweets per day
including ‘Nobel’

3. Gathering Twitter data
Tweets can be obtained via Mozdeh
 Also: Chorus http://www.chorusanalytics.co.uk/
NodeXL and other software
Must be gathered in near to real time
 Not available after about 2 weeks
Gather using keyword or phrase
searches
If historical tweets needed, buy from a
data provider

4. Twitter Time Series Analysis
with Mozdeh: Step by Step
Download free from http://mozdeh.wlv.ac.uk for Windows

11. Search engine part of Mozdeh – for the saved tweets

12. Sentiment-based searches

13. Gender-based searches

14. Identifies words that are
Relatively frequent for the query

17. Co-word analysis
Am automatic method to compare
different subsets of tweets for key
differences
Identifies words that are relatively
frequent compared to one keyword (or
gender) compared to another

18. The above example will compare the
words in tweets containing book and
read by gender

19. Words more common in male tweets containing “book”
than in female tweets containing “book”

20. Words more common in ? tweets than in ? Tweets
(male or female)

21. Words more common in ? tweets than in ? Tweets
(male or female)
A statistical measure of the significance
of the difference between genders

22. Twitter research ideas
Monitor keyword searches for a topic
Analysis ideas
 Time series – identify trends & causes of
any peaks
 Content analysis of random sample tweets –
why is the topic tweeted?
 Gender/sentiment/high frequency keywords
 Network analysis of tweeters/tweeting &
qualitative analysis of key tweeters – who is
tweeting and who is successful?

23. How and why scholars cite on
Twitter – Priem & Costello
Example of a Twitter study using interviews
and content analysis of tweets
Gathered and analysed a sample of tweets
sent by scholars
Concluded that: “Twitter citations are also
uniquely conversational, reflecting a broader
discussion crossing traditional disciplinary
boundaries.”

24. High frequency word analysis
An analysis of high frequency words for
Nobel prizes found tweets about:
 Alternative winners’ names non-
academic prizes only
 Gender references Female winner only (9%
mentioned her gender).
 Expressions of Sentiment literature
prize only (42% positive)
A quick analysis can give new insights.

25. Sentiment Strength Detection in
the Social Web -SentiStrength
• Detect positive and negative sentiment
strength in short informal text
 Develop workarounds for lack of standard
grammar and spelling
 Harness emotion expression forms unique to
MySpace or CMC (e.g., :-) or haaappppyyy!!!)
 Classify simultaneously as positive 1-5 AND
negative 1-5 sentiment
Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social Web.
Journal of the American Society for Information Science and Technology, 63(1), 163-173.
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text.
Journal of the American Society for Information Science and Technology, 61(12), 2544-2558.

26. SentiStrength Algorithm - Core
List of 2,489 positive and negative
sentiment term stems and strengths (1
to 5), e.g.
 ache = -2, dislike = -3, hate=-4,
excruciating -5
 encourage = 2, coolest = 3, lover = 4
Sentiment strength is highest in
sentence; or highest sentence if multiple
sentences

27. My legs ache.
You are the coolest.
I hate Paul but encourage him.
-2
3
-4 2
1, -2
positive, negative
3, -1
2, -4

28. Extra sentiment methods
spelling correction nicce -> nice
booster words alter strength very happy
negating words flip emotions not nice
repeated letters boost sentiment/+ve niiiice
emoticon list :) =+2
exclamation marks count as +2 unless –ve hi!
repeated punctuation boosts sentiment good!!!
negative emotion ignored in questions u h8 me?
Sentiment idiom list shock horror = -2
Online as http://sentistrength.wlv.ac.uk/

29. Tests against human coders
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
SentiStrength
agrees with
humans
as much as they
agree with each
other 1 is perfect agreement, 0 is random agreement

30. Why the bad results for BBC?
(and Digg)
Irony, sarcasm and expressive language
e.g.,
 David Cameron must be very happy that I
have lost my job.
 It is really interesting that David Cameron
and most of his ministers are millionaires.
 Your argument is a joke.
$

31. Example – sentiment in major
media events
Analysis of a corpus of 1 month of English
Twitter posts (35 Million, from 2.7M accounts)
Automatic detection of spikes (events)
Assessment of whether sentiment changes
during major media events

32. 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.

33. Chile
matchingpostsSentimentstrengthSubj.
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

34. #oscars
%matchingpostsSentimentstrengthSubj.
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 sentime
Just subj.
Av. -ve sentime
Just subj.
Proportion of tweets
mentioning the Oscars

35. Sentiment and spikes
Statistical analysis of top 30 events:
 Strong evidence that higher volume hours
have stronger negative sentiment than
lower volume hours
 No evidence that higher volume hours
have different positive sentiment strength
than lower volume hours
=> Spikes are typified by small increases
in negativity

36. Summary
Tweets gathered free with Mozdeh
 Search tweets and conduct content analysis
 Time series analysis graphs – trends over time
 Identify topics causing high sentiment
 Identify differences by gender
 Identify important topics by high freq. keywords
 Identify important topic differences by co-words
 Pilot test your ideas first
Gather tweets in advance for a period of time
Can give quick insights into your research
goals – or can be a primary research method