Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Twitter Analytics


Published on

A user’s guide to interpreting, reinterpreting and misinterpreting data and analytics from everyone's favourite social media service - Twitter

Published in: Business
  • @chenry1674 - in the use and uses section, I was examining the historical literature on twitter use - I regard the curated links list as the 'Pass-Along' purpose (slide 41).
    Are you sure you want to  Yes  No
    Your message goes here
  • On your 'Uses and Usage' slide, I think you've forgotten 'Link Dissemination' One of the most important use cases of Twitter is the user curated link dissemination. Many people sign for Twitter to get access to information and links from the brightest minds. Brands have made it a point to establish a presence to not only carry on a conversation, but to share additional information and market about their products. In fact, the efficacy of link sharing is so important, I threw together a tool to measure it, Twitter users who regularly share links can plug in their username to see how effective they are at sharing links.
    Are you sure you want to  Yes  No
    Your message goes here
  • Hello, know my CRM presentation:

    My others presentations in:

    André Luiz Bernardes
    Are you sure you want to  Yes  No
    Your message goes here
  • Great info. ,

    Are you sure you want to  Yes  No
    Your message goes here
  • Great slides and info. cheers!
    Are you sure you want to  Yes  No
    Your message goes here

Twitter Analytics

  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
  2. If you’re on Twitter Questions can be sent to @stephendann
  3. Twitter. <ul><li>Twitter matters because of what it is: at its heart, a platform that offers an exchange of ideas and information on an unprecedented scale. </li></ul><ul><li>Why Twitter Matters : Marketing : Idea Hub :: American Express OPEN Forum Fri Oct 02 2009 21:16:49 GMT+1000 (AUS Eastern Standard Time) </li></ul>Twitter in Plain English
  4. For those who came in late <ul><li> </li></ul><ul><li>140 character message </li></ul><ul><li>Social network </li></ul><ul><li>Web2.0 </li></ul><ul><li>End of the world as we know it </li></ul><ul><li>Best thing since sliced bread </li></ul>
  5. Twitter! (What is it good for?) <ul><li>health community (Berger 2009) </li></ul><ul><li>public libraries (Cahill 2009, Cuddy 2009) </li></ul><ul><li>political campaigns (Cetina 2009, Henneburg et al 2009) </li></ul><ul><li>business (Dudley 2009; Power and Forte 2008) </li></ul><ul><li>journalism (Ettama 2009) </li></ul><ul><li>civil unrest and protests (Fahmi 2009) </li></ul><ul><li>social activism (Galer-Unti 2009) </li></ul><ul><li>live coverage of events (Gay et al 2009) </li></ul><ul><li>eyewitness accounts (Lariscy et al 2009) </li></ul><ul><li>government (Macintosh 2009) </li></ul><ul><li>education (Parslow 2009). </li></ul>
  6. Uses and usage <ul><li>casual listening platform (Crawford 2009), </li></ul><ul><li>creating the illusion of physicality (Hohl 2009) </li></ul><ul><li>sense of connectedness and relationship (Henneburg et al 2009) </li></ul><ul><li>venue for conversation (Steiner 2009) </li></ul>
  7. How to dissect a living medium?
  8. Raw Counts Tweetstats –
  9. Text Analysis Tweetstats – Wordle –
  10. Leximancer Leximancer –
  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 <ul><li>Three factors </li></ul><ul><li>the public (or personal-public) nature of the communication and conversation </li></ul><ul><li>the brevity of posted content </li></ul><ul><li>highly connected social space / articulated online contact networks. </li></ul><ul><li>Naaman, Boase, and Lai (2010), </li></ul>
  15. Prior Analysis
  16. Analysis 1: Take the people out <ul><li>Krishnamurthy et al (2008) </li></ul><ul><li>users were classified by </li></ul><ul><ul><li>follower/following counts, </li></ul></ul><ul><ul><ul><li>Numbers and ratios </li></ul></ul></ul><ul><ul><li>means and mechanisms of their engagement </li></ul></ul><ul><ul><ul><li>Web (61.7%), mobile/text (7.5%), software (22.4%) </li></ul></ul></ul><ul><ul><li>volume of use </li></ul></ul><ul><ul><ul><li>Tweets per time period </li></ul></ul></ul>
  17. Analysis 2: Content Category <ul><li>Java et al 2007 </li></ul><ul><li>1,348,543 tweets </li></ul><ul><li>76,177 users </li></ul><ul><li>April 01, to May 30, 2007 </li></ul><ul><li>Four meta-categories </li></ul><ul><li>daily chatter </li></ul><ul><li>conversations </li></ul><ul><li>information / URL sharing </li></ul><ul><li>news reporting </li></ul>
  18. Analysis 3: Insider Coding <ul><li>Jansen et al (2009) </li></ul><ul><li>tweets with brand name </li></ul><ul><li>expression of brand sentiment </li></ul><ul><li>13-week period </li></ul><ul><ul><li>April 4, 2008 to July 3, 2008. </li></ul></ul><ul><li>650 reporting episodes </li></ul><ul><ul><li>13 x 50 brands </li></ul></ul><ul><li>149,472 tweets </li></ul><ul><li>Sentiment Scale </li></ul><ul><li>No Sentiment </li></ul><ul><li>Wretched </li></ul><ul><li>Bad </li></ul><ul><li>So-so </li></ul><ul><li>Swell </li></ul><ul><li>Great </li></ul><ul><li>Content Schema </li></ul><ul><li>Sentiment </li></ul><ul><li>Information seeking </li></ul><ul><li>Information providing </li></ul><ul><li>Comment </li></ul>
  19. Analysis 4: Pear’s Babble <ul><li>Pear Analytics (2009) </li></ul><ul><li>2000 tweets </li></ul><ul><li>11am to 5pm </li></ul><ul><li>10 working days </li></ul><ul><li>Six part classification </li></ul><ul><li>news (3.6%), </li></ul><ul><li>spam (3.75%), </li></ul><ul><li>self-promotion (5.85%), </li></ul><ul><li>pointless babble (40.55%) </li></ul><ul><li>conversational (37.55%) </li></ul><ul><li>pass-along value (8.70%). </li></ul>
  20. Analysis 5: Where’s the party @? <ul><li>Honeycutt and Herring (2009) </li></ul><ul><li>four one-hour samples </li></ul><ul><li>four-hour intervals </li></ul><ul><li>6 a.m. to 6 p.m. Eastern Standard Time, on January 11, 2008 </li></ul><ul><li>Sample of 200 tweets coded with grounded methodology </li></ul><ul><li>1) Addressivity: Directs a message to another person </li></ul><ul><li>2) Reference: Makes reference to another person, but </li></ul><ul><li>does not direct a message to him or her. </li></ul><ul><li>3) Emoticon: Used as part of an emoticon. </li></ul><ul><li>4) Email: Used as part of an email address. </li></ul><ul><li>5) Locational 'at': Signals where an entity is located. </li></ul><ul><li>6) Non-locational 'at': Used to represent the preposition 'at' other than in the sense of location. </li></ul><ul><li>7) Other: Uses not fitting into any other category, </li></ul>
  21. Analysis 6: Rigor and Bass <ul><li>Naaman, Boase and Lai (2010) </li></ul><ul><li>Sample of 400 tweets </li></ul><ul><ul><li>more than one category was assigned to a single message. </li></ul></ul><ul><li>Sampling frame </li></ul><ul><ul><li>125,593 unique user IDs </li></ul></ul><ul><ul><li>‘ personal’ Twitter users </li></ul></ul><ul><ul><li>10 friends, 10 followers, 10 messages </li></ul></ul><ul><ul><li>911 users </li></ul></ul><ul><li>N = 350 users </li></ul><ul><li>The Categories </li></ul><ul><li>Information Sharing </li></ul><ul><li>Self Promotion </li></ul><ul><li>Opinions/Complaints </li></ul><ul><li>Statements and Random Thoughts </li></ul><ul><li>Me now </li></ul><ul><li>Question to followers </li></ul><ul><li>Presence Maintenance </li></ul><ul><li>Anecdote (me) </li></ul><ul><li>Anecdote (others) </li></ul>
  22. The consistent theme <ul><li>People keep using Twitter for personal use. </li></ul><ul><li>Discussions of “self” </li></ul><ul><li>Pointless babble </li></ul><ul><li>Conversational </li></ul><ul><li>All criticisms of the use of twitter for pleasure and personal consumption </li></ul>
  23. What Twitter looks like… <ul><li>… and how are people using Twitter? </li></ul>Twitter –
  24. Recoding the Platform Let’s do it my way
  25. Theory and Ideology <ul><li>Useful versus Enjoyable </li></ul><ul><li>Bohme (2006) outlines a propensity of society to classify technology of all forms into </li></ul><ul><ul><li>“ useful and therefore valuable” </li></ul></ul><ul><ul><li>“ enjoyable, therefore irrelevant”. </li></ul></ul>Böhme, G (2006) Technical Gadgetry: Technological Development in the Aesthetic Economy, Thesis Eleven, 86 (1): 54-66
  26. Method <ul><li>Grounded Theory </li></ul><ul><li>Broad categories based on / supported by six prior studies </li></ul><ul><li>Sub categories developed from theory and data </li></ul>
  27. Sample <ul><li>Personal Twitter History </li></ul><ul><li>@stephendann (274 Following / 355 Followers) </li></ul><ul><li>2841 messages </li></ul><ul><li>Mar 13 2007 to Aug 18 2009 </li></ul><ul><li>@darthvader (5,513 Following / 113,624 Followers) </li></ul><ul><li>484 messages </li></ul><ul><li>Jan 09 2007 to Sep 27 2009 </li></ul>
  28. Sample <ul><li>@stephendann </li></ul><ul><li>274 Following / 355 Followers </li></ul><ul><ul><li>Supports Krishnamurthy et al (2008) 250 follower rule </li></ul></ul><ul><li>2841 tweets </li></ul><ul><ul><li>Start: Tue Mar 13 2007 11:53:01 </li></ul></ul><ul><ul><li>End: Tue Aug 18 2009 07:29:30 </li></ul></ul><ul><li>Data was captured from the timeline using the Sujathan (2009) “ Twitter to pdf ” software. </li></ul>
  29. Categories and Results
  30. Major Categories <ul><li>Conversational </li></ul><ul><ul><li>Uses an @statement to address another user </li></ul></ul><ul><li>Status </li></ul><ul><ul><li>An answer to “What are you doing now?”. </li></ul></ul><ul><li>Pass along </li></ul><ul><ul><li>Tweets of endorsement of content </li></ul></ul><ul><li>News </li></ul><ul><ul><li>Identifiable news content which is not UGC </li></ul></ul><ul><li>Phatic </li></ul><ul><ul><li>Content independent connected presence </li></ul></ul><ul><li>Spam </li></ul><ul><ul><li>Junk traffic, unsolicited automated posts, and other automated tweets generated without user consent </li></ul></ul>
  31. Results- @stephendann phatic
  32. Minor Categories <ul><li>Conversational </li></ul><ul><li>1. Query </li></ul><ul><li>2. Referral </li></ul><ul><li>3. Action </li></ul><ul><li>4. Response </li></ul><ul><li>Status </li></ul><ul><li>1. Personal </li></ul><ul><li>2. Temporal </li></ul><ul><li>3. Location </li></ul><ul><li>4. Mechanical </li></ul><ul><li>5. Physical </li></ul><ul><li>6. Work </li></ul><ul><li>7. Activity </li></ul><ul><li>Pass along </li></ul><ul><li>1. RT </li></ul><ul><li>2. UGC </li></ul><ul><li>3. Endorsement </li></ul><ul><li>News </li></ul><ul><li>1. Headlines </li></ul><ul><li>2. Sport </li></ul><ul><li>3. Event </li></ul><ul><li>4. Weather </li></ul><ul><li>Phatic </li></ul><ul><li>1. Greeting </li></ul><ul><li>2. Fourth wall </li></ul><ul><li>3. Broadcast </li></ul><ul><li>4. Unclassifiable </li></ul><ul><li>Spam </li></ul>
  33. Results - @stephendann
  34. Conversational <ul><li>Query </li></ul><ul><ul><li>Questions, question marks or polls </li></ul></ul><ul><li>Referral </li></ul><ul><ul><li>An @response which contains URLs or recommendation of other Twitter users. </li></ul></ul><ul><li>Action </li></ul><ul><ul><li>Activities involving other Twitter users </li></ul></ul><ul><li>Response </li></ul><ul><ul><li>Catch-all classification for conversation @tweets </li></ul></ul>
  35. Conversational @USERNAME Beware the polar bears. 30% 850 Response Invading Germany from France. Who's with me? 17% 480 Query @USERNAME Items under $1000 are exempt. 2% 66 Pass-along *waves at @USERNAME* 3% 77 Action Exemplar % N Category 3rd pers plural 73.84 21.97 17.05 13637 30% 850 Response Impersonal pronouns, Auxiliary verbs, Tentative, Discrepancy, QMark 75.33 21.22 10.13 7032 17% 480 Query OtherP, Period 54.31 21.86 18.89 1020 2% 66 Pass-along Conjunctions, Inhibition, Inclusive Biological processes 67.75 23.80 14.74 958 3% 77 Action Linguistic Inquiry Results Dictionary Words >6 letters Words/ Sentence Word Count % N Category
  36. Conversational
  37. Status (1 of 2) <ul><li>Personal </li></ul><ul><ul><li>Positive or negative sentiment in the form of personal opinion or emotional status </li></ul></ul><ul><li>Temporal </li></ul><ul><ul><li>References to specific dates, times, statements of temporal nature (waiting) and temporal action (“Time to” ) </li></ul></ul><ul><li>Location </li></ul><ul><ul><li>Geographic references and location statements, including statements of traveling, location change </li></ul></ul>
  38. Status (2 of 2) <ul><li>Mechanical </li></ul><ul><ul><li>Technology or mechanical systems </li></ul></ul><ul><li>Physical </li></ul><ul><ul><li>Sensory experiences of a physical nature </li></ul></ul><ul><li>Work </li></ul><ul><ul><li>Reference to work related activity </li></ul></ul><ul><li>Activity </li></ul><ul><ul><li>Direct statements that answer “What are you doing now?” </li></ul></ul>
  39. Status 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) 7% 196 work Waiting for my 2pm performance review to start. 6% 170 temporal It's freezing out there this morning 1% 37 physical I liked Modest Mouse after they became famous. 8% 221 personal Well... I'm in trouble. Used 3829.060MB (62.322%) of your 6GB. You have 22 days remaining 4% 106 mechanical Standing in a lecture theatre talking about Marketing Management. 2% 69 location Diplomacy is the art of saying &quot;Nice doggy&quot; until you find a big enough rock. Captaincy is the timely provision of large enough rocks. 5% 140 broadcast Playing with the internet in the name of science 1% 35 activity Work 80.19 23.96 16.59 3881 7% 196 work Prepositions 79.05 19.84 14.11 3160 6% 170 temporal Perceptual processes, feel, body, health 81.91 23.10 16.87 658 1% 37 physical Total function words, Common verbs, Past / Present tense, Adverbs, Cognitive processes, 80.71 19.05 19.35 4121 8% 221 personal Sadness 70.43 22.02 13.88 1985 4% 106 mechanical Articles, space 78.12 22.96 12.67 1115 2% 69 location Friends, Quote 71.21 22.84 11.21 2119 5% 140 broadcast see, Ingestion,Achievement 76.17 23.26 14.41 533 1% 35 activity Linguistic Inquiry Results Dictionary Words >6 letters Words/ Sentence Word Count % N Category
  40. Status
  41. Pass along <ul><li>RT </li></ul><ul><ul><li>Any statement reproducing another Twitter status using the via @ or RT protocol </li></ul></ul><ul><li>UGC </li></ul><ul><ul><li>Links to content created by the user </li></ul></ul><ul><li>Endorsement </li></ul><ul><ul><li>Links to web content not created by the sender </li></ul></ul>
  42. Pass Along - Bus Slogan Generator Time - 4% 122 Ugc L4D Survivors in Rockband2 singing L7 Pretend We're Dead. (HT to @LesbianGamers ). It's seriously amazing. 2% 48 RT I'm looking myself up on Publish or Perish ( to find a reference to a paper that cited me because I want to cite them 4% 108 endorsement Pass along Exemplar % N Category Numbers, leisure 52.89 20.61 39.98 1679 4% 122 Ugc SemiC 54.31 24.75 16.85 893 2% 48 RT Parenth, Dash 55.71 21.05 30.12 1777 4% 108 endorsement Linguistic Inquiry Results Dictionary Words >6 letters Words/ Sentence Word Count % N Category
  43. Pass Along
  44. News <ul><li>Headlines </li></ul><ul><ul><li>Coverage of breaking news and personal eye-witness accounts of news events </li></ul></ul><ul><li>Sport </li></ul><ul><ul><li>Identifiable results of sporting events </li></ul></ul><ul><li>Event </li></ul><ul><ul><li>Any tweet which represents the live discussion of an identified or identifiable event </li></ul></ul><ul><li>Weather </li></ul><ul><ul><li>Report of weather conditions without commentary </li></ul></ul>
  45. News - - - Headlines - - - Sport Between NASA's satellite and autoanalysis of imagery, and Google Map data, scientific proof where there's smoke, there's fires #bcc2 0% 13 Event News Exemplar % N Category Negative emotion, Anger Certainty Perceptual processes 65.62 22.92 10.67 192 0% 13 Event News Linguistic Inquiry Results Dictionary Words >6 letters Words/ Sentence Word Count % N Category
  46. Phatic <ul><li>Greeting </li></ul><ul><ul><li>Statements of greetings to the broader Twitter community </li></ul></ul><ul><li>Fourth wall </li></ul><ul><ul><li>Textual equivalent of comments made directly to camera in television or cinema </li></ul></ul><ul><li>Broadcast </li></ul><ul><ul><li>Textual soliloquy, monologue and undirected statements of opinion </li></ul></ul><ul><li>Unclassifiable </li></ul><ul><ul><li>Unclassifiable strings of text </li></ul></ul>
  47. Phatic AAAAAAAAAAAAAAARGH 0% 7 Unclassifiable Good morning Twitterverse. How's the world outside? 1% 17 Greeting Note to self: Just because you're carrying tiny vials of hypercaffeine is no reason to start calculating remote delivery systems for them. 2% 49 Fourth wall *wanders through his twitter follower list, blocking all of the automated/spam follower accounts* 1% 30 Action Phatic Exemplar % N Category NIL 33.33 33.33 3.00 6 0% 7 Unclassifiable Future tense, Positive emotion Relativity, time 78.37 29.33 7.70 208 1% 17 Greeting Negations, Quantifiers, Social processes, Humans, Affective processes, Causation, Exclusive 76.44 20.69 12.29 836 2% 49 Fourthwall Anxiety, hear, motion 75.22 26.75 22.80 456 1% 30 Action Linguistic Inquiry Results Dictionary Words >6 letters Words/ Sentence Word Count % N Category
  48. Phatic
  49. Findings <ul><li>Non commercial Twitter classification </li></ul><ul><li>Replicable across multiple accounts </li></ul><ul><li>Heavy duty lifting </li></ul><ul><li>Manual coding </li></ul><ul><li>Qualitative research </li></ul>
  50. Implications <ul><li>Twitter </li></ul><ul><li>Consumption analysis </li></ul><ul><li>Consumer framework </li></ul><ul><li>Not always a business framework </li></ul>
  51. Future research The Public Timeline versus the Classification Scheme
  52. Questions [email_address] Or @stephendann
  53. References <ul><li>Böhme, G (2006) Technical Gadgetry: Technological Development in the Aesthetic Economy, Thesis Eleven, 86 (1): 54-66 </li></ul><ul><li>Cetina, K K 2009, What is a Pipe? bama and the Sociological Imagination, Theory, Culture & Society 2009 26(5): 129–140 </li></ul><ul><li>Crawford, K (2009)'Following you: Disciplines of listening in social media',Continuum,23:4,525 — 535 </li></ul><ul><li>Dudley, E 2009, Editorial: Lines of Communication, Journal of Librarianship and Information Science 2009; 41; 131-134 </li></ul><ul><li>Ettama, J 2009 New media and new mechanisms of public accountability, Journalism 2009; 10; 319-321 </li></ul><ul><li>Fahmi, W S 2009, Bloggers' street movement and the right to the city. (Re)claiming Cairo's real and virtual &quot;spaces of freedom&quot;, Environment and Urbanization 2009; 21; 89-107 </li></ul><ul><li>Galer-Unti, R 2009, Guerilla Advocacy: Using Aggressive Marketing Techniques for Health Policy Change, Health Promotion Practice, 10; 325-327 </li></ul><ul><li>Gay, P Plait, P, Raddick, J, Cain, F and Lakdawalla, E (2009) &quot;Live Casting: Bringing Astronomy to the Masses in Real Time&quot;, CAP Journal, June 26-29 </li></ul><ul><li>Henneburg, S. Scammell, M and O'Shaughnessy, N (2009) Political marketing management and theories of democracy, Marketing Theory 2009; 9; 165-188 </li></ul><ul><li>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, </li></ul><ul><li>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 </li></ul><ul><li>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 </li></ul>
  54. References <ul><li>Krishnamurthy, B, Gill, P and Arlitt, M (2008) A Few Chirps About Twitter, WOSN'08, August 18, 2008, 19-24 </li></ul><ul><li>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 </li></ul><ul><li>Macintosh, A 2009, The emergence of digital governance, Significance, December, 176-178 </li></ul><ul><li>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 </li></ul><ul><li>Parslow, G, 2009, Commentary: Twitter for Educational Networking, BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION Vol. 37, No. 4, pp. 255–256, 2009 </li></ul><ul><li>Pear Analytics (2009) Twitter Study – August 2009, </li></ul><ul><li>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 </li></ul><ul><li>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 </li></ul>
  55. <ul><li>This work is licensed under the Creative Commons Attribution-Share Alike 2.5 Australia License. To view a copy of this license, visit </li></ul>