The document summarizes different ways that Twitter data and usage has been analyzed in prior studies. It discusses 6 prior analyses that took different approaches such as removing user information, categorizing tweet content, analyzing brand sentiment, and classifying messages. The document then describes its own coding approach to analyze a sample of the author's Twitter history based on major categories of conversational, status, pass along, news, and spam messages. Results of this analysis on percentages of tweets in each category and subcategory are also presented.

1. Twitter Analytics A user’s guide to interpreting, reinterpreting and misinterpreting the social media service. Dr Stephen Dann School of Management Marketing & International Business, Australian National University @stephendann or stephen.dann@anu.edu.au Siêu thị điện máy Việt Long www.vietlongplaza.com.vn

2. If you’re on Twitter Questions can be sent to @stephendann Siêu thị điện máy Việt Long www.vietlongplaza.com.vn

3. Twitter. Twitter matters because of what it is: at its heart, a platform that offers an exchange of ideas and information on an unprecedented scale. Why Twitter Matters : Marketing : Idea Hub :: American Express OPEN Forum http://www.openforum.com/idea-hub/topics/marketing/article/why-twitter-matters-ann-handley Fri Oct 02 2009 21:16:49 GMT+1000 (AUS Eastern Standard Time) Twitter in Plain English Siêu thị điện máy Việt Long www.vietlongplaza.com.vn

4. For those who came in late Twitter.com 140 character message Social network Web2.0 End of the world as we know it Best thing since sliced bread www.digiworldhanoi.vn

5. Twitter! (What is it good for?) health community (Berger 2009) public libraries (Cahill 2009, Cuddy 2009) political campaigns (Cetina 2009, Henneburg et al 2009) business (Dudley 2009; Power and Forte 2008) journalism (Ettama 2009) civil unrest and protests (Fahmi 2009) social activism (Galer-Unti 2009) live coverage of events (Gay et al 2009) eyewitness accounts (Lariscy et al 2009) government (Macintosh 2009) education (Parslow 2009).

6. Uses and usage casual listening platform (Crawford 2009), creating the illusion of physicality (Hohl 2009) sense of connectedness and relationship (Henneburg et al 2009) venue for conversation (Steiner 2009)

7. How to dissect a living medium? www.digiworldhanoi.vn

8. Raw Counts Tweetstats – www.tweetstats.com

9. Text Analysis Tweetstats – www.tweetstats.com Wordle – wordle.com

10. Leximancer Leximancer – www.leximancer.com

11. Coded Content Analysis Made up for this set of slides.

12. Same Data set… So many different ways to present the results

13. Coded Content Analysis

14. Social awareness streams Three factors the public (or personal-public) nature of the communication and conversation the brevity of posted content highly connected social space / articulated online contact networks. Naaman, Boase, and Lai (2010), infolab.stanford.edu/~mor/research/naamanCSCW10.pdf

15. Prior Analysis

16. Analysis 1: Take the people out Krishnamurthy et al (2008) users were classified by follower/following counts, Numbers and ratios means and mechanisms of their engagement Web (61.7%), mobile/text (7.5%), software (22.4%) volume of use Tweets per time period

17. Analysis 2: Content Category Java et al 2007 1,348,543 tweets 76,177 users April 01, to May 30, 2007 Four meta-categories daily chatter conversations information / URL sharing news reporting

18. Analysis 3: Insider Coding Jansen et al (2009) tweets with brand name expression of brand sentiment 13-week period April 4, 2008 to July 3, 2008. 650 reporting episodes 13 x 50 brands 149,472 tweets Sentiment Scale No Sentiment Wretched Bad So-so Swell Great Content Schema Sentiment Information seeking Information providing Comment

19. Analysis 4: Pear’s Babble Pear Analytics (2009) 2000 tweets 11am to 5pm 10 working days Six part classification news (3.6%), spam (3.75%), self-promotion (5.85%), pointless babble (40.55%) conversational (37.55%) pass-along value (8.70%).

20. Analysis 5: Where’s the party @? Honeycutt and Herring (2009) four one-hour samples four-hour intervals 6 a.m. to 6 p.m. Eastern Standard Time, on January 11, 2008 Sample of 200 tweets coded with grounded methodology 1) Addressivity: Directs a message to another person 2) Reference: Makes reference to another person, but does not direct a message to him or her. 3) Emoticon: Used as part of an emoticon. 4) Email: Used as part of an email address. 5) Locational 'at': Signals where an entity is located. 6) Non-locational 'at': Used to represent the preposition 'at' other than in the sense of location. 7) Other: Uses not fitting into any other category,

21. Analysis 6: Rigor and Bass Naaman, Boase and Lai (2010) Sample of 400 tweets more than one category was assigned to a single message. Sampling frame 125,593 unique user IDs ‘ personal’ Twitter users 10 friends, 10 followers, 10 messages 911 users N = 350 users The Categories Information Sharing Self Promotion Opinions/Complaints Statements and Random Thoughts Me now Question to followers Presence Maintenance Anecdote (me) Anecdote (others)

22. The consistent theme People keep using Twitter for personal use. Discussions of “self” Pointless babble Conversational All criticisms of the use of twitter for pleasure and personal consumption

23. What Twitter looks like… … and how are people using Twitter? Twitter – www.twitter.com

24. Recoding the Platform Let’s do it my way

25. Theory and Ideology Useful versus Enjoyable Bohme (2006) outlines a propensity of society to classify technology of all forms into “ useful and therefore valuable” “ enjoyable, therefore irrelevant”. Böhme, G (2006) Technical Gadgetry: Technological Development in the Aesthetic Economy, Thesis Eleven, 86 (1): 54-66

26. Method Grounded Theory Broad categories based on / supported by six prior studies Sub categories developed from theory and data

27. Sample Personal Twitter History @stephendann (274 Following / 355 Followers) 2841 messages Mar 13 2007 to Aug 18 2009 @darthvader (5,513 Following / 113,624 Followers) 484 messages Jan 09 2007 to Sep 27 2009

28. Sample @stephendann 274 Following / 355 Followers Supports Krishnamurthy et al (2008) 250 follower rule 2841 tweets Start: Tue Mar 13 2007 11:53:01 End: Tue Aug 18 2009 07:29:30 Data was captured from the timeline using the Sujathan (2009) “ Twitter to pdf ” software.

29. Categories and Results

30. Major Categories Conversational Uses an @statement to address another user Status An answer to “What are you doing now?”. Pass along Tweets of endorsement of content News Identifiable news content which is not UGC Phatic Content independent connected presence Spam Junk traffic, unsolicited automated posts, and other automated tweets generated without user consent

31. Results- @stephendann phatic

32. Minor Categories Conversational 1. Query 2. Referral 3. Action 4. Response Status 1. Personal 2. Temporal 3. Location 4. Mechanical 5. Physical 6. Work 7. Activity Pass along 1. RT 2. UGC 3. Endorsement News 1. Headlines 2. Sport 3. Event 4. Weather Phatic 1. Greeting 2. Fourth wall 3. Broadcast 4. Unclassifiable Spam

33. Results - @stephendann

34. Conversational Query Questions, question marks or polls Referral An @response which contains URLs or recommendation of other Twitter users. Action Activities involving other Twitter users Response Catch-all classification for conversation @tweets

35. Conversational Category N % Exemplar Action 77 3% *waves at @USERNAME* Pass-along 66 2% @USERNAME Items under $1000 are exempt. http://is.gd/AV7K Query 480 17% Invading Germany from France. Who's with me? Response 850 30% @USERNAME Beware the polar bears. Category N % Word Count Words/ Sentence >6 letters Dictionary Words Linguistic Inquiry Results Action 77 3% 958 14.74 23.80 67.75 Conjunctions, Inhibition, Inclusive Biological processes Pass-along 66 2% 1020 18.89 21.86 54.31 OtherP, Period Query 480 17% 7032 10.13 21.22 75.33 Impersonal pronouns, Auxiliary verbs, Tentative, Discrepancy, QMark Response 850 30% 13637 17.05 21.97 73.84 3rd pers plural

36. Conversational

37. Status (1 of 2) Personal Positive or negative sentiment in the form of personal opinion or emotional status Temporal References to specific dates, times, statements of temporal nature (waiting) and temporal action (“Time to” ) Location Geographic references and location statements, including statements of traveling, location change

38. Status (2 of 2) Mechanical Technology or mechanical systems Physical Sensory experiences of a physical nature Work Reference to work related activity Activity Direct statements that answer “What are you doing now?”

39. Status activity 35 1% Playing with the internet in the name of science broadcast 140 5% Diplomacy is the art of saying "Nice doggy" until you find a big enough rock. Captaincy is the timely provision of large enough rocks. location 69 2% Standing in a lecture theatre talking about Marketing Management. mechanical 106 4% Well... I'm in trouble. Used 3829.060MB (62.322%) of your 6GB. You have 22 days remaining personal 221 8% I liked Modest Mouse after they became famous. physical 37 1% It's freezing out there this morning temporal 170 6% Waiting for my 2pm performance review to start. work 196 7% Firing off e-mail after e-mail to clear my to do list (knowing that's a great way to regenerate to do list items doesn't stop me or help me) Category N % Word Count Words/ Sentence >6 letters Dictionary Words Linguistic Inquiry Results activity 35 1% 533 14.41 23.26 76.17 see, Ingestion,Achievement broadcast 140 5% 2119 11.21 22.84 71.21 Friends, Quote location 69 2% 1115 12.67 22.96 78.12 Articles, space mechanical 106 4% 1985 13.88 22.02 70.43 Sadness personal 221 8% 4121 19.35 19.05 80.71 Total function words, Common verbs, Past / Present tense, Adverbs, Cognitive processes, physical 37 1% 658 16.87 23.10 81.91 Perceptual processes, feel, body, health temporal 170 6% 3160 14.11 19.84 79.05 Prepositions work 196 7% 3881 16.59 23.96 80.19 Work

40. Status

41. Pass along RT Any statement reproducing another Twitter status using the via @ or RT protocol UGC Links to content created by the user Endorsement Links to web content not created by the sender

42. Pass Along Category N % Exemplar Pass along endorsement 108 4% I'm looking myself up on Publish or Perish (http://rurl.org/iw4) to find a reference to a paper that cited me because I want to cite them RT 48 2% L4D Survivors in Rockband2 singing L7 Pretend We're Dead. http://is.gd/BsVE (HT to @LesbianGamers ). It's seriously amazing. Ugc 122 4% http://twitpic.com/2o1c1 - Bus Slogan Generator Time - http://is.gd/hU2Q Category N % Word Count Words/ Sentence >6 letters Dictionary Words Linguistic Inquiry Results endorsement 108 4% 1777 30.12 21.05 55.71 Parenth, Dash RT 48 2% 893 16.85 24.75 54.31 SemiC Ugc 122 4% 1679 39.98 20.61 52.89 Numbers, leisure

43. Pass Along

44. News Headlines Coverage of breaking news and personal eye-witness accounts of news events Sport Identifiable results of sporting events Event Any tweet which represents the live discussion of an identified or identifiable event Weather Report of weather conditions without commentary

45. News Category N % Exemplar News Event 13 0% Between NASA's satellite and autoanalysis of imagery, and Google Map data, scientific proof where there's smoke, there's fires #bcc2 Sport - - - Headlines - - - Category N % Word Count Words/ Sentence >6 letters Dictionary Words Linguistic Inquiry Results News Event 13 0% 192 10.67 22.92 65.62 Negative emotion, Anger Certainty Perceptual processes

46. Phatic Greeting Statements of greetings to the broader Twitter community Fourth wall Textual equivalent of comments made directly to camera in television or cinema Broadcast Textual soliloquy, monologue and undirected statements of opinion Unclassifiable Unclassifiable strings of text

47. Phatic Category N % Exemplar Phatic Action 30 1% *wanders through his twitter follower list, blocking all of the automated/spam follower accounts* Fourth wall 49 2% Note to self: Just because you're carrying tiny vials of hypercaffeine is no reason to start calculating remote delivery systems for them. Greeting 17 1% Good morning Twitterverse. How's the world outside? Unclassifiable 7 0% AAAAAAAAAAAAAAARGH Category N % Word Count Words/ Sentence >6 letters Dictionary Words Linguistic Inquiry Results Action 30 1% 456 22.80 26.75 75.22 Anxiety, hear, motion Fourthwall 49 2% 836 12.29 20.69 76.44 Negations, Quantifiers, Social processes, Humans, Affective processes, Causation, Exclusive Greeting 17 1% 208 7.70 29.33 78.37 Future tense, Positive emotion Relativity, time Unclassifiable 7 0% 6 3.00 33.33 33.33 NIL

48. Phatic

49. Findings Non commercial Twitter classification Replicable across multiple accounts Heavy duty lifting Manual coding Qualitative research

50. Implications Twitter Consumption analysis Consumer framework Not always a business framework

51. Future research The Public Timeline versus the Classification Scheme

52. Questions [email_address] Or @stephendann www.digiworldhanoi.vn

53. References Böhme, G (2006) Technical Gadgetry: Technological Development in the Aesthetic Economy, Thesis Eleven, 86 (1): 54-66 Cetina, K K 2009, What is a Pipe? bama and the Sociological Imagination, Theory, Culture & Society 2009 26(5): 129–140 Crawford, K (2009)'Following you: Disciplines of listening in social media',Continuum,23:4,525 — 535 Dudley, E 2009, Editorial: Lines of Communication, Journal of Librarianship and Information Science 2009; 41; 131-134 Ettama, J 2009 New media and new mechanisms of public accountability, Journalism 2009; 10; 319-321 Fahmi, W S 2009, Bloggers' street movement and the right to the city. (Re)claiming Cairo's real and virtual "spaces of freedom", Environment and Urbanization 2009; 21; 89-107 Galer-Unti, R 2009, Guerilla Advocacy: Using Aggressive Marketing Techniques for Health Policy Change, Health Promotion Practice, 10; 325-327 Gay, P Plait, P, Raddick, J, Cain, F and Lakdawalla, E (2009) "Live Casting: Bringing Astronomy to the Masses in Real Time", CAP Journal, June 26-29 Henneburg, S. Scammell, M and O'Shaughnessy, N (2009) Political marketing management and theories of democracy, Marketing Theory 2009; 9; 165-188 Honeycutt, C and Herring, S C (2009) Beyond Microblogging: Conversation and Collaboration via Twitter, (2009). Proceedings of the Forty-Second Hawai’i International Conference on System Sciences (HICSS-42). Los Alamitos, CA: IEEE Press. 1-10, http://ella.slis.indiana.edu/~herring/honeycutt.herring.2009.pdf Jansen, B, Zhang, M, Sobel, K and Chowdury, A (2009) Twitter power: Tweets as electronic word of mouth, Journal of the American Society for Information Science and Technology, 60(11):2169–2188, 2009 http://ist.psu.edu/faculty_pages/jjansen/academic/jansen_twitter_electronic_word_of_mouth.pdf Java, A, Song, X, Finin, T and Tseng, B (2007) Why We Twitter: Understanding Microblogging Usage and Communities, Joint 9th WEBKDD and 1st SNA-KDD Workshop ’07 , August 12, 2007, p 56-65

54. References Krishnamurthy, B, Gill, P and Arlitt, M (2008) A Few Chirps About Twitter, WOSN'08, August 18, 2008, 19-24 Lariscy, R Avery, E J, Sweetser, K and Howes, P 2009 An examination of the role of online social media in journalists’ source mix, Public Relations Review 35 (2009) 314–316 Macintosh, A 2009, The emergence of digital governance, Significance, December, 176-178 Naaman, M, Boase, J and Lai, C-H (2010) Is it Really About Me? Message Content in Social Awareness Streams, CSCW 2010, February 6–10 Parslow, G, 2009, Commentary: Twitter for Educational Networking, BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION Vol. 37, No. 4, pp. 255–256, 2009 Pear Analytics (2009) Twitter Study – August 2009, http://www.pearanalytics.com/wp-content/uploads/2009/08/Twitter-Study-August-2009.pdf Power, R and Forte, D 2008, War & Peace in Cyberspace: Don’t twitter away your organisation’s secrets, Computer Fraud and Security, August, 18-20 Zhao, D and Rosson, M B, How and Why People Twitter: The Role that Micro-blogging Plays in Informal Communication at Work, GROUP’04, May 10–13, 2009, 243-252

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