Classifying Twitter Content

Dr Stephen DannAustralian National University

@stephendann

Presented at Marketing Science, Houston, June 11, 2011

If you’re on Twitter

Questions can be sent to @stephendannor

Hashtag #mktsci2011

Why here, why now?

Why this presentation?– MSI interest in role of social media in branding– Attitudinal metrics from web can predict

transactions

Why this method?– Try to further avoid the criteriaflation issue

• Hence announcing a coding structure exists

What outcome?– I could use a good set of equations

Series of Projects

Blog Post reacting to Pear Analytics 2009

First Monday Paper (Dann, 2010)

Marketing Science, Method <-You are here.

USF Social Marketing, Social Media in Social Marketing (next week)

AMSRS Conference, Crisis Communication Analysis (September)

ANZMAC, Categories in Detail (December)

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

How to analyze a living medium?

Hawthorn Effect*Uncertainty PrincipleSample Size / Twitter Volume[ ]

Why do any coding?

• Twitter is not about the aggregate firehose– There are those who disagree, and I have cited

many of them. However, few, if any actually read the impossibly fast updating full timeline

• Twitter is about how you use it.– Twitter becomes something in co-creation– Twitter timeline as documented history– Tracking Near-Past Behaviour

Raw Counts

Tweetstats – www.tweetstats.com

Text Analysis

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

Prior Analysis

Boyd et al 2010Crawford 2009DiMicco, et al 2008Fahmi 2009Gay et al 2009Heany and McClurg 2009Hohl 2009Honeycutt and Herring 2009

Jansen et al 2009Java et al 2007Lariscy et al 2009Makice, 2009Miller, 2008Naaman et al 2010Pear Analytics 2009Steiner 2009Zhao and Rosson 2009

Dann (2010) based on:

Schema

Developed from ground theory approach60+ Twitter articles

Use behaviours, content analysis, sentiment analysis

10,000+ tweetsManual coding

Supporting analysisLinguistic Analysis (LIEC)

Automated analysis

Leximancer Analysis

Framework

Six categories.1. Conversational2 . News Events3 . Pass along4 . Phatic5 . Status6 . Spam

Conversational

• core of the interpersonal exchange on Twitter, and the binding activity that links different users together into a sense of community, companionship and conversation – Cahill 2009, Cranefield and Yoong 2009,

Honeycutt and Herring 2009, Java et al 2009, Perlmutter 2009, Steiner 2009, Ratkiewicz 2010).

• four identifiable sub components– action, query, referral and response

News Events

• broad selection of media releases, citizen journalism, professional journalism, PR and publicity – Mäkinen and Wangu Kuira 2008, Power and

Forte 2008, Java et al 2009, Phelan et al 2009, Chu et al 2010, Petrovic et al 2010, Zhou et al 2010, Phuvipadawat and Murata 2011, Cheong and Lee 2011).

– Seven categories:• announcements, hashtagged events, headlines,

sport, natural disasters, transport and weather.

Pass along

• where Twitter is used as a short form publishing outlet for recommended links, other Twitter remarks, or links to the author’s own content – Java et al 2007, Mischaud 2007, Heany and

McClurg 2009, Java et al 2009, Pear Analytics, 2009, Naaman et al 2010, Zhang et al 2010, Bakshy et al 2011).

• Five categories– automated endorsement, endorsements,

retweet, secondary social media and user generated content,

Phatic

• Use of Twitter as a meanings to maintain a presence within a community, and connections to other users of the service without direct conversation – Java et al 2007, Miller, 2008, Henneburg et al

2009, Keenan and Shiri 2009, Makice 2009, Pear Analytics, 2009, Fernando 2010, Marwick and boyd 2010, Zhang et al 2010

• Four categories– undirected broadcast statements, fourth wall

breaking meta commentary, greetings and the unclassifiable content

Status

• Use of the service to answer the original Twitter question of “What are you doing?” in terms of reporting the user’s sense of “Me-Now”, or statements of immediately transpired activity – Gaonkar et al 2008, Bollen et al 2009, Java et al

2009, Chu et al 2010, Dodds et al 2011, Naaman et al 2010, Zhang et al 2010

• eight categories – activity, automated status, location, mechanical,

personal statements, physical, temporal and work

Sub categories

• Conversational– Response– Referral– Query– Action

• News Events• Pass along• Phatic• Status

Sub categories

• Conversational• News Events

– Headlines– Hashtagged Event– Natural disasters– Transport– Weather– Sport– Announcement

• Pass along• Phatic

Sub categories

• Conversational• News Events• Pass along

– Retweet– Endorsement– Secondary Social Media– User generated content– Automated Endorsement

• Phatic• Status

Marketing Science Style

• N = 11672

– Three public sector organisation timelines• Local government, police force, energy company

– Two hashtags • natural disaster• conference

– One personal timeline data set

 

Data n Dann #Dis. #Conf Police Counc. Ener.

Convers-ational

29% 3415 1473 30 427 585 785 115

News Events

8% 884 13 17 29 784 31 10

PassAlong

50% 5787 278 533 351 2780 949 896

Phatic 3% 398 213 12 60 69 24 20

Status 10% 1188 834 10 153 126 34 31

Total   11672 2811 602 1020 4344 1823 1072

Uses of the Data

0%

10%

20%

30%

40%

50%

60%

70%

80%

Pre-crisis Flood Inter-crisis Cyclone Post Crisis

Conversational

News Events

Pass along

Phatic

Status

Here’s where you come in…

The Challenge140 characters of text

[C] [S] [PA] [N] [P] [X]*

[C1]

[C2]

[C3]

[C4]

[S1]

[S2]

[S3]

[S4]

[S5]

[S6]

[S7]

[S7]

[PA1]

[PA2]

[PA3]

[PA4]

[PA5]

[N1]

[N2]

[N3]

[N4]

[N5]

[N6]

[N7]

[P1]

[P2]

[P3]

[P4]

[X1]

[X2]

[X3]

[X4]

Time

Day

Month

Year

* Spam gets a category indicated as “Delete”

Future plans

Segments and Use-Case Scenarios

Forward facing strategic guidelines

Predictive Models

Certain level of automationBut not autonomous coding.

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Questions

Stephen.dann@anu.edu.auOr

@stephendann

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