The document discusses analyzing Twitter data, including challenges and opportunities. It notes that Twitter data allows for time series analysis, network analysis of actors and content, and examining how information spreads through social networks. However, limitations include relying on the reliability of Twitter's API and data. The document provides examples of different types of tweets that could be analyzed and extracted from the Twitter platform.

1. Analyzing Twitter data
Issues
Challenges
and
Opportunities
RC33 Conference, Sydney Australia,
9-13 July 2012
Maurice Vergeer
m.vergeer@maw.ru.nl / www.mauricevergeer.nl / blog.mauricevergeer.nl
Radboud University Nijmegen, the Netherlands

2.  Many platform  Empty platform /
- Facebook infrastructure
- Twitter - Facility
- Linkedin
- Hyves
- RenRen
- Cyworld  User generated content
- Orkut - Text
- Youtube - Audio
- Flickr - Video
- Plurk - Pictures
- Sina Weibo
- Etc
Social media

3. Number of articles on politics, Internet and social media
180
160
140
120
Number of articles
100
80
60
40
20
0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Internet and politics (query 1) Social media and politics (query 2) Internet, social media and politics (query 3)
Source: Vergeer (in press / 2012) in New Media & Society

4. Focus on Twitter

5. The Netherlands
A special case?

7.  Opportunities
◦ Methodological/technical
 Timeseries analysis
 Network analysis
◦ Actors
◦ Content
◦ Diffusion of information through onine social networks
◦ Social media activities
 Limitations
◦ Twitter
 Reliability of Twitter API
Outline

8. • Within Twitter (using the API)
• Username
• Account creation data
• # of followers
• And the actual usernames of these followers
• # of followers
• And the actual usernames of those being followed
• Tweet text
• And many more (see dev.twitter.com)
Data sources

9.  Tweet
◦ Tweet text
◦ Whether or not it was a reply to another tweet
 To whom it was a reply (username/screenname and numerical
userid)
◦ Whether or not it was a retweet (according to Twitter)
 Which tweet was retweeted (nunerical tweetid)

10.  Message of tweet
 Whether or not is was a directed tweet
(sent to someone in particular)
◦ Identified by an @-sign
 Whether or not is was a retweet
◦ Identified by RT
Type of content

11.  Undirected tweet
◦ RCMP Commissioner appearing before Public Safety Cmte now.
What a popular guy - he has his own paparazzi!
 Directed tweet
◦ Fantastic blog by my good friend @GlenPearson -
http://bit.ly/hlAKXp #lpc
 Directed tweet to two usernames
◦ @miken32 @CBCEdmonton probably because that is NOT what I
said--more commercially viable is different than not needed.
 Retweet
◦ RT @liberal_party: Think Durham deserves better than Bev Oda?
Join @BobRaeMP for a rally tomorrow at 1pm http://lpc.ca/durham
#cdnpoli #lpc
Tweet examples

13.  Traditional material
◦ Produced by professional actors
◦ Newspapers
◦ Public administration documents
 Social media
◦ Produced by
 professional actors
 general public
Content analysis of tweets

14.  Large quantities of data
 Word frequencies
◦ Identifying the most important words in the corpus
◦ Code these words into more general categories
 Switch to SPSS (or other type of data management tool)
◦ Search for the words in the actual tweets
◦ Assign tweet to a specific code
 Improvements in SPSS
◦ Compute command facilitates many new text operators
◦ Char.index, Char.substr, etc
 Alternative
◦ Regular expressions
◦ complex
Data extraction

15.  Publicly available data sources on
parliament, election council
 Time series
◦ Identifying relevant societal/political events
relevant for the study at hand
 Ex.1 temporarily shut down of election campaign
due to passenger plane crash of Dutch airliner in
Libia My 2010
 Ex.2 Deregistration of People s Political Power
Party of Canada
External data sources

16. 900
800
700
600
500
400
300
200
100
0
newspaper broadcasting radio news agency magazine online only local
institutional Twitter account Personal Twitter account 9

17. Source: Vergeer & Hermans (forthcoming / 2013)
in Journal of Computer-Mediated Communication

19. 1000
0
100
200
500
800
900
300
400
600
700
01-mei-2010
02-mei-2010
03-mei-2010
04-mei-2010
05-mei-2010
06-mei-2010
07-mei-2010
08-mei-2010
09-mei-2010
CDA
PvdD
10-mei-2010
11-mei-2010
12-mei-2010
SGP
PvdA
13-mei-2010
14-mei-2010
15-mei-2010
SP
NN
16-mei-2010
17-mei-2010
18-mei-2010
VVD
TON
19-mei-2010
20-mei-2010
21-mei-2010
PVV
22-mei-2010
MenS
23-mei-2010
24-mei-2010
GL
HNL
25-mei-2010
26-mei-2010
27-mei-2010
CU
28-mei-2010
Partij1
29-mei-2010
30-mei-2010
31-mei-2010
D66
Piraten
01-jun-2010
02-jun-2010
03-jun-2010
04-jun-2010
05-jun-2010
06-jun-2010
07-jun-2010
08-jun-2010
09-jun-2010

20.  Date and time
 For longitudinal analysis and cross-national comparisons
◦ take note of the time differences and correct if necessary.
 Time zones
 Daytime saving
 What to do with countries having multiple time zones?
◦ Depends on RQs
 Communication patterns: keep a single time zone
 Focus on individual daily patterns: adjust for time zones

21.  Total tweets by candidates, followers and followed:
◦ 4,536,854 tweets
 Breakdown
◦ Tweets among candidates: appr 2%
◦ Tweets to inner circles (followers or being followed)
appr 18%
◦ Tweets to outer circle: appr
33%
◦ Tweets not directed to anyone in particular appr
49%
◦ Extracting users from tweets (@adresses)
Communication network analysis

22.  Communication network based on
candidates identified in tweets
 Excluding the general public
Communication network analysis

24.  See http://tinyurl.com/blzajsl for
animated version.

25.  Retrospective
◦ 3200 tweets back in time
 Cost technical
◦ Access to firehose for real time data
Limitations in data collection

26.  Date of tweet
◦ Minute fraction is time stamped with the wrong date
 Solution
◦ Estimate date and time using the tweetid
 Status of tweet as retweet
◦ RT
 Solution:
 Use text search operators to identify real retweets (“RT ”, “rt “)
Also see http://tinyurl.com/bohhjzn
 Reply to tweets
◦ Only the first address is identified
 Solution
◦ Search for multiple @-addresses using text extraction methods
Reliability of data as provided by
the API

27. BIG DATA
The buzz word of these days

28.  Not gigabyte, ot terabytes,
 But petabytes and exabytes of data

29.  Only for the few
 Specific hardware requirements
◦ Computing power
◦ Data storage
 The data presented in this presentation
◦ Appr 4.5 million records equals appr 1
gigabyte, not that Big

30. There is still so much to be done
with…

31. • Focus on specific cases
 -political communication:
 politicians – candidates in elections
 -fan studies
 celebrities
 cast of popular soap opera’s
◦ -journalism studies
 journalists and newspapers

Focus on specific cases

32.  actor information
 information on societal events
 accumulate data over time using the
same data structure
◦ Proonged analysis
◦ Multuple case studies, cross-national
comparative analysis
Enrich existing Twitter data with
external data

33.  Traditional process (textbook approach)
◦ RQ -> research design
 Practice, particularly with secondaire (i.e. third party) data
◦ Data  RQ  research design
◦ Data  research design  RQ
Twitter
 Content analysis
 Longitudinal analysis
 Network analysis
 Different research designs requires different techniques
 Collaborate
Look at the data from different
angles, i.e. research designs

34. Thank you for your attention