Twitter analytics -digiworldhanoi.vn

Twitter Analytics

A user’s guide to interpreting, reinterpreting and misinterpreting the social media service.

Dr Stephen DannSchool of Management

Marketing & International Business, Australian National University

@stephendann or [email protected]

www.digiworldhanoi.vn

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 Forumhttp://www.openforum.com/idea-hub/topics/marketing/article/why-twitter-matters-ann-handleyFri 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 1. the public (or personal-public) nature of

the communication and conversation2. the brevity of posted content3. 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 person2) Reference: Makes reference to another person, butdoes 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

Conversational (1473) 52%

news (13) 0%

Informal (103) 4%

status (974) 34%pass_along (278) 10%

phatic

Minor CategoriesConversational1. Query2. Referral3. Action4. Response

Status1. Personal2. Temporal3. Location4. Mechanical5. Physical6. Work7. Activity

Pass along1. RT2. UGC3. Endorsement

News1. Headlines2. Sport3. Event4. Weather

Phatic1. Greeting2. Fourth wall3. Broadcast4. 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

ConversationalCategory 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 lettersDictionary

WordsLinguistic 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

Conversational

action5% pass-along

4%

query33%

response58%

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

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)


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

Status

activty4%

broadcast14%

location7%

mechanical11%

personal23%

physical4%

temporal17%

work20%

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 AlongCategory 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


Words/ Sentence



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

Pass Along

endorsement39%

RT17%

ugc44%

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

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 - - -


Words/ Sentence



News

Event 13 0% 192 10.67 22.92 65.62 Negative emotion, Anger Certainty Perceptual processes

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

PhaticCategory 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


Words/ Sentence



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

Phatic

action

fourthwall

phatic

unclassifiable

Findings

Non commercial Twitter classification

Replicable across multiple accounts

Heavy duty liftingManual codingQualitative research

Implications

Twitter• Consumption analysis• Consumer framework • Not always a business framework

Future research

The Public Timeline versus the Classification Scheme

Questions

[email protected] Or

@stephendann


ReferencesBöhme, 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

ReferencesKrishnamurthy, 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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