2. A first glance at different

kinds of social media data

1

2

2006

3

2011

Social Media Data

Texts

Images

Videos

Mixed formats

Connections I (friends, followers)

Connections II (links/URLs)

Connections/Actions (likes, favs, comments, downloads)

Images

5

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

Vis F, Faulkner S, Parry K, Manyukhina Y & Evans L (2013) Twitpic-ing the riots: analysing images shared on Twitter during the 2011 UK riots In Weller K, Bruns A, Burgess J, Mahrt M & Puschmann C (Ed.), Twitter and Society (pp. 385-398). Peter Lang.

6

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

Hashtags

Mentions

7

Timeline

8 Gummer, T., Roßmann, J., & Wolf, C. (2014). Candidates’ Twitter Use in the German Election 2013. Presentation at the General Online Research 2014, Cologne, Germany.

9

Timeline

Gummer, T., Roßmann, J., & Wolf, C. (2014). Candidates’ Twitter Use in the German Election 2013. Presentation at the General Online Research 2014, Cologne, Germany.

Rhythm of a City

10 http://engineering.twitter.com/2012/06/studying-rapidly-evolving-user.html

11

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

nu

mb

er

of

follo

we

rs

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)

Bruns, A., Weller, K., & Harrington, S. (2014). Twitter and Sports: Football Fandom in Emerging and Established Markets. In: K.Weller, A. Bruns, J. Burgess, M. Mahrt and C. Puschmann (Eds.), Twitter and Society (pp. 263-280). New York et al.: Peter Lang.

Followers

Interactions

12

Paßmann, J., Boeschoten, T., & Shäfer, M.T. (2014). The Gift of the Gab: Retweet Cartels and Gift Economies on Twitter. In K. Weller, A. Bruns, J. Burgess, M. Mahrt & C. Puschmann (Eds.), Twitter and Society. New York et al.: Peter Lang.

Networks

13

following

Lietz, H., Wagner, C., Bleier, A., & Strohmaier, M. (2014). When politicians talk: Assessing online conversational practices of political parties on twitter. In International AAAI Conference on Weblogs and Social Media (ICWSM2014), Ann Arbor, MI, USA, June 2-4, 2014.

mentioning retweeting

Networks

14

Facebook (Paul Butler)

Data from: Facebook

https://www.facebook.com/note.php?note_id=469716398919

Twitter

Data from Twitter

https://blog.twitter.com/2013/geography-tweets-3

Geo data

Geo data

Livehood Project

Daten: Foursquare (via Twitter)

http://livehoods.org/maps/montreal 16

17

http://www.nytimes.com/interactive/2009/11/26/us/20091126-search-graphic.html?_r=0 Data from: Allrecipes.com

Geo data

The Guardian

Data from: Twitter

http://www.guardian.co.uk/news/datablog/2

012/nov/28/data-shadows-twitter-uk-

floods-mapped#zoomed-picture

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

Northeastern University and Harvard University

Data from: Twitter. http://www.ccs.neu.edu/home/amislove/twittermood/ 20

21 http://www.cci.edu.au/node/1362

The Australian Twitter-Sphere (by A. Bruns)

Some more about geo information

Overview on new geo-data:

Elwood S., Goodchild M.F., Sui D.Z. (2012). Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers, 102(3), 571-590.

Twitter

Leetaru K., Wang S., Cao G., Padmanabhan A., Shook E. (2013). Mapping the global Twitter heartbeat: The geography of Twitter. First Monday, 18(5).

Research Methods

24

SERIOUSLY? DO THEY NOT REALIZE THAT 99% OF TWEETS ARE WORTHLESS BABBLE THAT READ SOMETHING LIKE ‘JUST WOKE UP. GOING TO STARBUCKS NOW. GETTING LATTE.’ READER’S COMMENT FOUND IN THE COMMENT SECTION FOR GROSS, D. (2010, APRIL 14). LIBRARY OF CONGRESS TO ARCHIVE YOUR TWEETS. CNN. RETRIEVED FROM HTTP://EDITION.CNN.COM/2010/TECH/04/14/LIBRARY.CONGRESS.TWITTER/, RETRIEVED NOVEMBER 19. PHOTOS: HTTPS://WWW.FLICKR.COM/SEARCH/?TEXT=COFFEE&LICENSE=4%2C5%2C6%2C9%2C10

New type of data

Researchers value social media as a new type of data

Previously „ephemeral data“ become visible

Immediate – quick reaction to events

Structured

„natural“ data

25

“What I find really interesting is that structure becomes manifest in internet communication. So it’s the first time in history actually that we can, that social structures between people become manifest within a technology. (...) They become visible, they become crawlable, they become analyzable.”

Kinder-Kurlanda, Katharina E., and Katrin Weller. 2014. "'I always feel it must be great to be a hacker!': The role of interdisciplinary work in social media research." In Proceedings of the 2014 ACM conference on Web Science, 91-98. New York: ACM.

Approaches Surveys Experiments Interviews Web ethnography

Content analysis

Network analysis Linguistic analyses (eg. sentiment analysis)

Rather rarely used in combination Many case studies, little methodological standards

Multi-disciplinary environment

Freedom to explore new approaches

Multi-method

Exchange with other disciplines

27

How to study social media?

„information disclosure and

privacy on Facebook“

„Election prediction with Twitter data“

Challenge vs. Chance

lots of room for exploration and innovation

but

few or no standards

29

Outlook: Data collection options

APIs Official resellers „manual“ forms

of collection

Re-using published datasets

Third party tools (Crowdsourcing)

30

Big Data?

31

Big Data?

Examples from Twitter research 309,740 Twitter users (with followers and tweets) 17,803 tweets from 8,616 users + 1st degree network (3,048,360 directed

edges, 631,416 unique followers, and 715,198 unique friends) 1.3 million Twitter conversations, with each conversation containing between

2 and 243 posts 20,000 tweets 21,623,947 geo-tagged tweets 99,832 tweets But also: One person’s Twitter network (652 followers, 114 followings). Experiment with 125 students. 1,827 annotated tweets Experiment with 1677 participants Survey with 505 young American adults none

33

Different methods – in social science based Twitter research

Weller, K. (2014). What do we get from Twitter – and what not? A close look at Twitter research in the social sciences. Knowledge Organization. 41(3), 238-248

Big data? Twitter and elections

No. of Tweets No. of publications (2013)

0-500 3

501-1.000 4

1.001-5.000 1

5.001-10.000 1

10.001-50.000 7

50.001-100.000 4

100.001-500.000 5

500.001-1.000.000. 3

1.000.001-5.000.000 3

mehr als 5.000.000 3

More than 100.000.000 1

More than 1.000.000.000 1

no/insufficient details 13

Weller, K. (2014). Twitter und Wahlen: Zwischen 140 Zeichen und Milliarden von Tweets. In: R. Reichert (Ed.), Big Data: Analysen zum digitalen Wandel von Wissen, Macht und Ökonomie (pp. 239-257). Bielefeld: transcript.

Example: Twitter

35

Example: Twitter Data

Example: Twitter Data

Some small example with Twitter data

38

Testdata

Go to http://tiny.cc/testdata (link will be deactivated after the course)

Save the file to the desktop.

Open the file.

39

Who is discussing?

Identify all users, who have written at least one tweet.

What is the distribution of tweets per user?

How many users have written exactly one tweet?

Who are the five most active users? – What can you find out about them?

40

Other information

• How many tweets are geocoded?

How many RTs?

YourTwapperkeeper

41

What is going on?

Read approx. 30 tweets.

How would you approach studying what the tweets are about?

Look up approx. 10 links from tweets.

How would you approach studying what the tweets are about?

42

Frequency of URLs: #www2010

0

5

10

15

20

25

30

35

40

45

1

31

61

91

12

1

15

1

18

1

21

1

24

1

27

1

30

1

33

1

36

1

39

1

42

1

45

1

48

1

51

1

54

1

Fre

qu

en

cy o

f U

RL

on

ran

k n

URL on rank n (ranked by frequency)

Distribution of URLs from #www2010

#www2010

Weller, K., Dröge, E., & Puschmann, C. (2011). Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences. In M. Rowe, M. Stankovic, A.-S. Dadzie, & M. Hardey (Eds.), Making Sense of Microposts (#MSM2011), Workshop at Extended Semantic Web Conference (ESWC 2011), Crete, Greece (pp. 1–12). CEUR Workshop Proceedings Vol. 718.

Frequency of URLs: #mla09

0

5

10

15

20

25

30

1 91

72

53

34

14

95

76

57

38

18

99

71

05

11

31

21

12

91

37

14

51

53

16

11

69

17

71

85

Fre

qu

en

cy o

f U

RL

on

ran

k n

URL on rank n (ranked by frequency)

Distribution of URLs from #mla09

#mla09

URL Categorization

Blog Conference Error Media

Press Project Publication Slides

Twitter Other

Frequent URLs and their categories: #www2010 URL Frequency Category

http://blog.marcua.net/post/566480920/twitter-papers-at-the-www-2010-conference

41 Blog

http://www.danah.org/papers/talks/2010/WWW2010.html 35 Publication

http://kmi.tugraz.at/staff/markus/www2010/www2010_roomstream.html

29 Twitter

http://xquery.pbworks.com/rtp-meetup 22 Error

http://www.elon.edu/e-web/predictions/futureweb2010/carl_mala mud_www_keynote.xhtml

22 Conference

http://www.elon.edu/e-web/predictions/futureweb2010/default .xhtml

18 Conference

http://futureweb2010.wordpress.com/schedule/ 16 Conference

http://www.slideshare.net/haewoon/what-is-twitter-a-social-network-or-a-news-media-3922095

13 Slides

http://events.linkeddata.org/ldow2010/ 12 Conference

http://opengraphprotocol.org/ 12 Project

http://www.websci10.org/program.html 12 Conference

Frequent URLs and their categories: #mla09 URL Frequency Category

http://amandafrench.net/2009/12/30/make-10-louder/ 27 Blog

http://www.briancroxall.net/2009/12/28/the-absent-presence-todays-faculty/

23 Blog

http://nowviskie.org/2009/monopolies-of-invention/ 22 Blog

http://chronicle.com/article/missing-in-action-at/63276/ 20 Error

http://www.profhacker.com/?p=4448 18 Press

http://www.samplereality.com/2009/11/15/digital-humanities-sessions-at-the-2009-mla/

18 Blog

http://chronicle.com/blogpost/the-mlathe-digital/19468/ 16 Press

http://www.profhacker.com/2010/01/09/academics-and-social-media-mla09-and-twitter/

15 Press

http://academhack.outsidethetext.com/home/2010/the-mla-briancroxall-and-the-non-rise-of-the-digital-humanities/

15 Blog

http://www.samplereality.com/2010/01/02/the-mla-in-tweets/ 15 Blog

URL Categories: #mla09 and #www2010

Blog; 229

Conference; 23

Error; 69

Media; 34

Press; 123

Project; 11

Publication; 4

Slides; 0

Twitter; 22 Other; 36

Categories of URLs from #mla09 (counting all URLs, n=551)

Blog; 54

Conference; 16

Error; 28

Media; 25

Press; 34 Project; 5

Publication; 3

Slides; 0

Twitter; 14 Other; 20

Categories of URLs from #mla09 (counting unique URLs only, n=199)

Blog; 68

Conference; 37

Error; 92

Media; 71

Press; 33 Project; 51

Publication; 52

Slides; 45

Twitter; 31

Other; 94

Categories of URLs from #www2010 (counting unique URLs only, n=574)

Blog; 222

Conference; 206

Error; 201

Media; 137 Press; 92

Project; 116

Publication; 135

Slides; 106

Twitter; 76

Other; 169

Categories of URLs from #www2010 (counting all URLs, n=1460)

Internal Citations: Retweets

#www2010 #mla09

Automatically detected RTs: Number and percentage of RTs in entire conference dataset

1,121 (33.38% of

3,358)

414 (21.46% of 1,929)

∅ RTs per twitterer (automatically detected RTs, entire conference dataset)

1.24 1.12

Manually detected RTs: Number and percentage of RTs in entire conference dataset

1,318 (39.25% of 3,358)

514 (26.65% of 1,929)

Different ways to count retweets

Weller, K., Dröge, E., & Puschmann, C. (2011). Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences. In M. Rowe, M. Stankovic, A.-S. Dadzie, & M. Hardey (Eds.), Making Sense of Microposts (#MSM2011), Workshop at Extended Semantic Web Conference (ESWC 2011), Crete, Greece (pp. 1–12). CEUR Workshop Proceedings Vol. 718.

Testdata 2 and 3

Go to http://tiny.cc/testdata2 and/or http://tiny.cc/testdata3 (links will be deactivated after the course)

Save file to the desktop.

No. 2: Import csv to Excel.

No. 3: Open in Excel.

Explore!

50

YourTwapperkeeper

YourTwapperkeeper

53

http://www.tweetarchivist.com/

54 http://www.tagsleuth.com/

„Homework“

55

Voluntary Homework

56

Think about a case study you would be interested in (in the context of social media research). Prepare a research question you would like to answer.

Alternatively, you can usa an example topic tomorrow.

Activate TagSleuth Account

Activate a free 3 day trial account for TagSleuth.

Set up a collection that matches your selected topic.

57

Conclusions 2

58

Lessons learned In the context of social science research, it is not all

about „big“ data, but about new data which can enable new types of insights.

New types of data also come with several challenges, e.g. concerning new methods.

Get to know „your“ platforms and their data – as early as possible in the research process. Familiarizing with platforms may take some time.

Identify what is feasible or not in your domain of interest.

If your ideal dataset is not accessible, think about proxies.

59

If you have time to read 3 papers…

Almuhimedi, H., Wilson, S., Liu, B., Sadeh, N., & Acquisti, A. (2013). Tweets are forever: a large-scale quantitative analysis of deleted tweets (p. 897). ACM Press. http://doi.org/10.1145/2441776.2441878

Fabio Giglietto, Luca Rossi, Davide Bennato (2012) The Open Laboratory: Limits and Possibilities of Using Facebook, Twitter, and YouTube as a Research Data Source, 145-159. In: Journal of Technology in Human Services 30 (3-4).

Mahrt, M., & Scharkow, M. (2013). The value of big data in digital media research. In: Journal of Broadcasting & Electronic Media, 57(1), 20-33.

60