informer-meformer cscw presentation

Is It Really About Me? Message Content in Social Awareness Streams

Mor Naaman, Jeffrey Boase, Chih-Hui Lai*

02/09/2010 CSCW Conference

What is Twitter?

Popular social media service launched in 2007, with 19.1 millions user in Dec. 2009

Users posts short messages -- 140 characters, maximum

the “follow-ship” relationshipsAsymmetric

Social Awareness Streams (SAS)

Characteristics of SASPublic or semi-public nature of messages (not one-on-one)Messages are short, consumed in streamsOnline personal networks are articulated; networks

structure communication

Twitter as a SAS model By identifying Twitter as a type of communication system,

results of this research can potentially be generalized to other similar platforms

Research Questions

RQ1: What types of messages are commonly posted and how does message type relate to other variables?

RQ2: What are the differences between users in terms of the types and diversity of messages that they usually post?

RQ3: How are these differences between users’ content practices related to other user characteristics?

Analysis: Qualitative + Quantitative Approaches

Why qualitative coding?– No existing studies have created a definitive classification

of Twitter messages– Twitter is a new mode of communication

A grounded approach to derive message categories– See paper for details

Coding Scheme

Code Example(s)

Information Sharing (IS) “15 Impressive and Beautiful Uses of WordPress <URL REMOVED>”

Opinions/Complaints (OC) “Go Aussie $ go!”“Illmatic= greatest rap album ever”

Statements and Random Thoughts (RT)

“The sky is blue in the winter here””I miss New York but I love LA...”

Me now (ME) “tired and upset”“just enjoyed speeding around my lawn on my John Deere. Hehe :)”

Other categories See paper for details

Method

Data collection– Using Twitter’s (API) , randomly selected ‘active,

personal users’, N = 350– Code 10 messages from each user

RQ1: Message Categories on Twitter

me now (ME, 41% of all messages coded), random thoughts (RT, 25%), opinions/complaints (OC, 24%), information sharing (IS, 22%)

CodeInformation Sharing (IS)

Self Promotion (SP)Opinions/Complaints (OC)

Statements and Random Thoughts (RT)Me now (ME)Question to followers (QF)

Presence Maintenance (PM)

Anecdote (me) (AM)Anecdote (others) (AO)

RQ1: Message Type and Other Variables

Females are more likely to post “me now” messages (M=45% of a user’s messages) than males (M=37%) t(344)=3.12, p<0.005

51% of mobile-posted messages are “me now” messages, compared to the 37% of “me now” messages posted from non-mobile applications

chi square=49.7, p<.0001

RQ2: User Clusters by Message Type

Cluster analysis was used to identify two clusters of users:– Informers (20% of users), Meformers (80% of users)

Both informers and meformers engage in different types of message activity

RQ3: How Users are Different?Informers are better connected to friends and

followers than meformers

• Informers are more conversational: have a higher proportion of mentions of other users in their messages

Informers Meformers

Friends (medians)

131 61

Followers (medians)

112 42

Summary of Key Findings

Four major categories of messages on Twitter:– me now (41% of all messages coded) , information sharing (22%) , opinions

(24%), random statements (25%)

Gender and device use– Females are more likely to post “me now” messages– “Me now” messages are likely to be sent by mobile devices

Users are of two kinds: – Informers (20% of users), Meformers (80% of users)

Informers are better connected to friends and followers than meformers

Discussion and Implications

Meformers engage in self-focused messages People may use Twitter to engage in different ways of self-

expression, and through which initiate social interaction (phatic communion---see ICA paper)

Informers engage in use of mentions and have more social contactsPeople may use Twitter to send/receive potentially

valuable information directly, and through which maintain and build relationships

Thank you!Any questions?

Mor Naaman, Jeffrey Boase, Chih-Hui Lai*{ mor, jboase, chihhui}@rutgers.edu