This document provides an overview of Twitter research, including: - Early Twitter studies characterized most tweets as "daily chatter" but conversations have since evolved with features like @replies and retweets (RTs). - Popular research areas now include brand communication, crises/disasters, elections/politics, popular culture, and technical analyses of information retrieval, sentiment analysis and network structures. - Collecting Twitter data presents challenges like obtaining a representative sample and ensuring reliability. Standards for metrics and analyzing structural elements of tweets are still emerging. - Legal and ethical issues require carefully anonymizing and sharing minimal user-related data. Reproducibility and long-term preservation of Twitter datasets also remain open challenges.

1. TWITTER RESEARCH:
STATE OF THE ART AND
OPEN CHALLENGES
Katrin Weller
@kwelle
katrin.weller@gesis.org
1
Präsentation im DAS Kolloquium
Köln,
21. März 2013

2. Introduction to TwitterPart 1

3. Twitter: A Short Introduction
3
Jack Dorsey (2000): “twttr sketch”. http://www.flickr.com/photos/jackdorsey/182613360/

4. Twitter: A Short Introduction
4

5. Twitter: A Short Introduction
5
Tweet = 140 characters

6. Twitter: A Short Introduction
6
Followers and Followees

7. Twitter: A Short Introduction
7

8. Twitter: A Short Introduction
8

9. Twitter Terminology
9
Retweet
(RT, via)
Kooti, F., Yang, H., Cha, M., Gummadi, K.P. & Mason, W.A. (2012). The Emergence of Conventions in Online Social Networks.
Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2012), Dublin.

10. Twitter Terminology
10
@message
(≠ direct message)

11. Twitter Terminology
11
#
Hashtags

12. Hashtags
12
 Memes
 Conventions
 Trends
 Influence
 #becareful!!!
March 19, 2012

13. Twitter: Numbers
13
 Founded in 2006 (independent Plattform
since 2007)
 March 2012: 140 million active users and
340 million Tweets a day
 December 2012: more than 200 million
users
 USA, 2012: 15% of online adults use Twitter
 Germany, 2012: 4% of population
PEW Internet:
http://www.pewinter
net.org/Reports/201
2/Twitter-Use-
2012.aspx
Twitter Blog:
http://blog.twitt
er.com/2012/0
3/twitter-turns-
six.html
@twitter:
https://twitter.co
m/twitter/status
/28105165223
5087872
ARD/ZDF online Studie
http://www.ard-zdf-
onlinestudie.de/fileadmin/
Online12/0708-
2012_Busemann_Gscheidl
e.pdf

14. Twitter vs. Facebook
14
In Germany
Facebook: 72.1% (of Internet users)
Twitter: 10.5% (of Internet users)
German Social Media
Consumer Report:
http://www.socialmediathinklab
.com/wp-
content/uploads/2013/02/W
WU_Social-Media-Consumer-
Report_0213_Ansicht.pdf

15. Twitter: Trivia
15
 Twitter users with most followers?
 Trends 2012: olympics, US election
 Most retweeted:
https://2012.twitter.com/de/golden-tweets.html

16. Twitter: Popular Users
16
March,2013,http://twittercounter.com/pages/100

17. Twitter Tools
17

18. Twitter Tools
18

19. 19

20. Twitter ResearchPart 2

21. Development of Twitter Research
21
0
200
400
600
800
1000
1200
2007 2008 2009 2010 2011 2012
Scopus WoS SSCI Scopus: Social Science only

22. Twitter vs. Facebook
22
0
200
400
600
800
1000
1200
1400
2007 2008 2009 2010 2011 2012
Scopus: Twitter Scopus: Facebook

23. Scopus: Publications from 70 countries
23
United States; 958
United Kingdom; 174
Japan; 166China; 140Germany; 115
Australia; 95
Canada; 77
Spain; 77
South Korea; 73
India; 58

24. Scopus: Publications by Discipline
24
1621
519
375
291
284
178
115
98
48
41
0 200 400 600 800 1000 1200 1400 1600 1800
Computer Science
Social Sciences
Engineering
Mathematics
Business, Management and Accounting
Medicine
Decision Sciences
Arts and Humanities
Materials Science
Psychology
Top ten subject areas for Twitter research based on Scopus (TITLE-ABS-KEY(Twitter) AND PUBYEAR > 2006)

25. Why Study Twitter?
25
pointless babble?

26. Early Twitter Research
26
 Java and colleagues (2007) characterised most
tweets as “daily chatter”.
 Pear Analytics study: 40% of tweets are pointless
babble (Kelly, 2009).
Java, A., Song, X., Finin, T., & Tseng, B. (2007). Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th
WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (WebKDD/SNA-KDD ’07). ACM, New York, NY, USA,
56-65. DOI=10.1145/1348549.1348556 http://doi.acm.org/10.1145/1348549.1348556
Kelly, R. (2009). Twitter Study. Pear Analytics, retrieved from http://www.pearanalytics.com/wp-content/uploads/2012/12/Twitter-Study-
August-2009.pdf

27. Twitter Evolution
27
Conversations evolved on Twitter
 @ symbol for replies (Honeycutt & Herring, 2009)
 RTs
 New studies focusing on communication structures
and networks
Honeycutt, C., and Herring, Susan C. (2009). Beyond microblogging: Conversation and collaboration via Twitter. Proceedings of the
Forty-Second Hawaii International Conference on System Sciences.Los Alamitos, CA IEEE Press.

28. Twitter Evolution
28
Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a Social Network or a News Media? In Proceedings of the 19th
International World Wide Web (WWW) Conference, April 26-30, 2010, Raleigh NC, USA.
Follow me?

29. Selected Research Areas
29
de-banalising Twitter!

30. Selected Research Areas
30
Brand Communication
& Marketing
Crises & Natural
Disasters Elections & Politics
Popular Culture Education & Scholarly
Communication
Health Care &
Diseases

31. Technical Research
31
Named Entity Extraction Information Retrieval &
Ranking Sentiment Analysis
Network Analyses Trend Detection

32. How to Study Twitter?
32

33. Twitter Data?
33

34. How to Collect Twitter Data?
34
Challenges:
Real-time archiving
obligatory
Limited to portions of
traffic

35. How to Collect Twitter Data?
35
Twitter APIs
 API = Application Programming Interface
 Interface to collect data from web applications
 Streaming API, Rest APIs, Search API

36. How to Collect Twitter Data?
36
STREAMING API
- push-based, live stream of data
- Researchers need tools that maintain a connection to
this stream.

37. How to Collect Twitter Data?
37
 Sample (1% or 10% of all tweets, probably
random)
 Track (tweets including specific keywords)
 Follow (tweets from selected users)
 Locations (for geotagged tweets)

38. How to Collect Twitter Data?
38
REST API
 Limited requests per hour
 Social graph data (who is following who)
 Access trending topics
 And many more

39. How to Collect Twitter Data?
39
 Your Twapperkeeper
 Via RSS feed (e.g. google reader, thunderbird)
 Third parties ($)
 Your own programmes
 Gnip / DataSift

40. Your Twapperkeeper
40

41. Your Twapperkeeper
41

42. Tweet Archivist
42
http://www.tweetarchivist.com/

43. GNIP
43

44. Examples
44

45. Twitter Visualisations
45
http://www.cci.edu.au/node/1362
The Australian Twitter-Sphere

46. Rhythm of a City
46
http://engineering.twitter.com/2012/06/studying-rapidly-evolving-user.html

47. Sentiments
47
http://www.ccs.neu.edu/home/amislove/twittermood/

48. Twitter in African Capitals
48
http://www.jeuneafrique.com/Article/ARTJAWEB20130215165826/internet-
libreville-accra-addis-abebareseaux-sociaux-les-capitales-africaines-de-
twitter-quartier-par-quartier.html#Tunis

49. 49
1. FC Köln (@fckoeln)
Borussia Mönchengladbach (@VfLBorussia)
BVB Dortmund 09 II (@BVB)
FC Bayern München (@BayMuenchen)
FC Schalke 04 II (@s04, official)
FC Schalke 04 I (@FCSchalke04, inofficial)
Hamburger SV (@HSV)
SV Werder Bremen I (@Werder_Bremen)
SV Werder Bremen II (@werderbremen)
0
10000
20000
30000
40000
50000
60000
70000
80000
Jun 11 Jul 11 Aug 11 Sep 11 Oct 11 Nov 11 Dec 11 Jan 12 Feb 12 Mar 12 Apr 12 May 12 Jun 12
numberoffollowers
month
1. FC Augsburg (@FCAugsburg) 1. FC Kaiserslautern (@Rote_Teufel)* 1. FC Köln (@fckoeln)
1. FC Nürnberg (@1_fc_nuernberg) 1. FSV Mainz 05 (1FSVMainz05) 1899 Hoffenheim (achtzehn99)
Bayer 04 Leverkusen (@bayer04fussball) Borussia Mönchengladbach (@VfLBorussia) BVB Dortmund 09 I (@BVBDortmund09)
BVB Dortmund 09 II (@BVB) FC Bayern München (@BayMuenchen) FC Schalke 04 II (@s04, official)
FC Schalke 04 I (@FCSchalke04, inofficial) Hamburger SV (@HSV) Hannover 96 I (@ichbin96)
Hannover 96 II (@hannover96) Hertha BSC Berlin (@HerthaBSC)* SC Freiburg (@sc_freiburg)
SV Werder Bremen I (@Werder_Bremen) SV Werder Bremen II (@werderbremen) VfB Stuttgart (@VfB)
VfL Wolfsburg (@VfL_Wolfsburg)
Twitterleague: German Bundesliga Clubs during Season 2011/12
See also: Weller, K., & Bruns, A. (2013). Das Spiel dauert 140 Zeichen. Wie deutsche Fußballvereine Twitter für Marketing und
Fan-Kommunikation entdecken. In: HiER 2013. Proceedings des 8. Hildesheimer Evaluierungs- und Retrievalworkshop, April 2013: https://www.uni-
hildesheim.de/media/fb3/informationswissenschaft/HIER/hier2013_proceedings_vorab.pdf

50. London Riots
50
http://www.guardian.co.uk/uk/2011/dec/07/twitter-riots-how-news-spread

51. 51
Bruns, A., & Burgess, J. (2012). Notes towards the scientific study of Twitter. In Tokar, A., Beurskens, M., Keuneke, S.,
Mahrt, M., Peters, I., Puschmann, C., van Treeck, T., & Weller, K. (Eds.). (2012). Science and the Internet (pp. 159-169).
Düsseldorf: Düsseldorf University Press http://nfgwin.uni-duesseldorf.de/sites/default/files/Bruns.pdf

52. ChallengesPart 3

53. It‘s not all about the numbers…
53
Big Data vs.
meaningful research
questions

54. Representativeness
54
Influenced by, e.g.:
 User statistics
 Time of data collection

55. Reliability
55
 Verified accounts
 Verified storys?

56. What should we measure?
56
What is a link, a follower, a
friend, a tweet worth?
 How to interpret users‘
activities?

57. What should we measure?
57
Typically:
- Tweets from a certain user / a group of users
- Tweets mentioning a certain users
- Tweets that contain a specific word or hashtag
- Random tweets
Also:
- Follower numbers
- Tweets containg (specific) URLs

58. Standard Metrics?
58
 Number of tweets (per period, per user)
 Number of users with at least one tweet
 Structural Analysis of Tweets:
 Original tweets, RTs, (modified RTs), @message
 Tweets containing URLs

59. Standards? Time Series
hours
Numberoftweetsperhour
By Cornelius Puschmann (@coffee001)

60. Standards? Networks (@, RT, follower)
By Cornelius Puschmann (@coffee001)

61. Standards? Active Users
Users and their tweets during #www2010
0
20
40
60
80
100
120
140
160
180
200
Numberoftweets

62. Standards? Active Users
0
5
10
15
20
25
30
35
40
Numberof@-messaes
@-Nachrichten gesendet @-Nachrichten empfangen@messages sent @messages received

63. Legal vs. Ethical?
63

64. Legal Questions
64
 Grey area in law
Basics:
 Twitter Terms of Services
 Twitter Rules of the Road
 Twitter Privacy Policy

65. Ethical Questions
65
 Not all that‘s legal is ethical
 How to deal with user data?
 How to anonomyse the data?

66. Legal and Ethical Questions
66
Currently: common sense
 Share as little individual-related data as possible
 Don‘t make tweet collections publicly available

67. Data Access & Long Term Accessibility
67
today vs. tomorrow

68. Reproducibility
68
 Collecting the same data twice?
 Working with existing datasets?
 Cleaning the data?

69. Preservation
69
 Library of Congress
 ARCOMEM project

70. Thank you for your attention!
70

71. Upcoming
71
Edited Collection: Twitter and Society
Editors: Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt
&Cornelius Puschmann. To be published 2013, with Peter Lang.