2. A first glance at different kinds of social media data 1 2006 2 2011 3 Social Media Data . Texts . Images . Videos . Mixed formats . Connections I (friends, followers) . Connections II (links/URLs) . Connections/Actions (likes, favs, comments, downloads) Images 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. 5 Hashtags 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 6 http://nfgwin.uni-duesseldorf.de/sites/default/files/Bruns.pdf Mentions 7 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. 8 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. 9 Rhythm of a City 10 http://engineering.twitter.com/2012/06/studying-rapidly-evolving-user.html Bruns, A., Weller, K., & Harrington, S. (2014). Twitter and Sports: Football Fandom in Emerging and Established Markets. In: K.Weller, A. Bruns,80000 J. Burgess, M. Mahrt and C. Puschmann (Eds.), Twitter and Society (pp. 263-280). New York et al.: Peter Lang. 70000 Followers BVB Dortmund 09 II (@BVB) 60000 FC Bayern München (@BayMuenchen) 50000 40000 SV Werder Bremen II (@werderbremen) Hamburger SV (@HSV) numberof followers 30000 FC Schalke 04 II (@s04, official) 1. FC Köln (@fckoeln) SV Werder Bremen I (@Werder_Bremen) 20000 Borussia Mönchengladbach (@VfLBorussia) FC Schalke 04 I (@FCSchalke04, inofficial) 10000 0 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 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) 11 Interactions 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. 12 Networks following retweeting mentioning 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. 13 Networks Facebook (Paul Butler) Data from: Facebook https://www.facebook.com/note.php?note_id=469716398919 14 Geo data Twitter Data from Twitter https://blog.twitter.com/2013/geography-tweets-3 Geo data Livehood Project Daten: Foursquare (via Twitter) http://livehoods.org/maps/montreal 16 Geo data http://www.nytimes.com/interactive/2009/11/26/us/20091126-search-graphic.html?_r=0 Data from: Allrecipes.com 17 The Guardian Data from: Twitter http://www.guardian.co.uk/news/datablog/2 012/nov/28/data-shadows-twitter-uk- floods-mapped#zoomed-picture http://www.jeuneafrique.com/Article/ARTJAWEB20130215165826/internet-libreville-accra-addis- 19 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 The Australian Twitter-Sphere (by A. Bruns) http://www.cci.edu.au/node/1362 21 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 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 24 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 “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. 25 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 „manual“ forms APIs Official resellers of collection Re-using published Third party tools (Crowdsourcing) datasets 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 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. 33 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 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: #www2010 Distribution of URLs from #www2010 45 40 #www2010 35 30 25 20 15 Frequency of URL on rank n ofFrequency URL rank on 10 5 0 1 31 61 91 121 241 151 181 211 271 301 331 361 391 421 451 481 511 541 URL on rank n (ranked by frequency) Frequency of URL on rank n 10 15 20 25 30 0 5 1 9 #mla09 from of URLs Distribution Frequency 17 25 33 41 URL on rank n (ranked frequency) by