Twitter Analytics

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

If you’re on Twitter Questions can be sent to @stephendann

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

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

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).

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)

How to dissect a living medium?

Raw Counts Tweetstats – www.tweetstats.com

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

Leximancer Leximancer – www.leximancer.com

Coded Content Analysis Made up for this set of slides.

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

Coded Content Analysis

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

Prior Analysis

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

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

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

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%).

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,

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)

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

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

Recoding the Platform Let’s do it my way

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

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

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

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.

Categories and Results

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

Results- @stephendann phatic

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

Results - @stephendann

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

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. http://is.gd/AV7K 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

Conversational

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

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?”

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 "Nice doggy" 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

Status

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

Pass Along http://twitpic.com/2o1c1 - Bus Slogan Generator Time - http://is.gd/hU2Q 4% 122 Ugc L4D Survivors in Rockband2 singing L7 Pretend We're Dead. http://is.gd/BsVE (HT to @LesbianGamers ). It's seriously amazing. 2% 48 RT 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 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

Pass Along

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

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

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

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

Phatic

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

Implications
Twitter
Consumption analysis
Consumer framework
Not always a business framework

Future research The Public Timeline versus the Classification Scheme

Questions [email_address] Or @stephendann

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

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

This work is licensed under the Creative Commons Attribution-Share Alike 2.5 Australia License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/2.5/au/

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Twitter Analytics

by Stephen Dann on Oct 10, 2009

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