Examining a Twitter-Based Discourse Community

of Composition Scholars

Brian N. Larson@Rhetoricked / www.Rhetoricked.com

Department of Writing Studies, University of Minnesota

Finding problems with

Social network graph visualization

Motivation

• Ph.D. Seminar spring 2012– “Emerging Genres on the Internet”– Dr. Carol Berkenkotter

• Do certain Twitter practices constituted genres?– But genres belong to communities– …or to activity systems– Both theoretically bounded

Problem

• What theoretically licensed means can I use to sample a subset of the Twitter population?

• Can I implement that practically?• I smugly proposed what’s in the paper• I now repent, but think the questions are still

worth exploring

Outline: Define your terms, get some data, and see where you’re at!

• Dealing with the Twitter fire hose• “Discourse community lite”• Social network theory basics• Example of data from 2012 CCCCs• Urging caution, suggesting next steps

Challenge: Sampling Twitter

• Genre theory considers communities– Swales 1990; Berkenkotter & Huckin 1994; Russell

1994 (activity systems); Devitt 2004 (communities, collectives, social networks)

• How to sample? Sparsity problems– Twitter has 200 million active users– More than ½ billion tweets per day (5500/second)– Need a “root”

Hashtag as root?

• Potential virtues– Easy to search for– Definitive threshold characteristic

• Potential vices– Unknown relation to theoretical concerns– Over- and under-inclusive

• How about hashtag + follows of users of hashtag?– Assumption: Many communities within hashtag sample

Community? Twitter users span geographies and topics

• (Gruzd et al. 2010)• A common language• “Temporality” or community shares “a consciousness of

a shared temporal dimension in which they co-exist” • The decline in prominence of “high centers”• Interactivity among members• A variety of communicators• “[C]ommon public place where members can meet and interact”• “[S]ustained membership over time”

Community: Sense of community

• (Gruzd et al. 2010)• Members feel that they are members• Members have influence within the

community• Community meets some member needs• Members share an emotional connection

Network analysis theory

• A graph– “Nodes” or “vertices” represent individuals– “Edges” or “arcs” represent relationships between

the individuals. • In visual representations– A node is represented as a point on the graph– An edge is a line between nodes– An arc is a line with an arrow, i.e., a unidirectional

relationship

Figure 2: Friendship diagram

Example of network graph. Source: Wikimedia commons.

Figure 2: Friendship diagram

{(1,2), (1,5), (2,3), (2,5), (3,4), (4,5), (4,6)}

Figure 2: Density

Complete graph = = 15 Density = Edges/15 = 7/15 = 0.467

Figure 2: Degree centrality

①Degree = 2②Degree = 3③Degree = 2④Degree = 3⑤Degree = 3⑥Degree = 1

Higher = more central

Total edges connecting to node

Figure 2: Closeness centrality

①Closeness = 1.8② ③Closeness = 1.6④ ⑤Closeness = 1.4⑥

Lower = more central

Average distance from node to each other node

Figure 2: Betweenness centrality and clustering coefficient

Centrality summarized

• Degree: How many connections does node have?

• Closeness: How close is node to other nodes in graph?

• Betweenness: To what extent does node lie along “critical paths”?

What are the edges in Twitter?

• Not necessarily reciprocal: They are directional or “arcs”

• I follow you, you may or may not follow me• I @-reply to you…• I retweet you…

Community candidate variables

• Start with a Twitter hashtag– Possibily over- and under-inclusive…

• Bound sample in time• Find who each subscriber follows• Interactivity among members: Density of edges

representing @-replies and retweets among candidate group members

• Individual influence: Density of edges representing @-replies and retweets among candidate group members

CCCC 2012: Twitter data set

• Jen Michaels set up the archive• March 9-23, 2012• CCCCs hashtags• 5,000+ tweets, nearly 600 subscribers• Power distribution of tweets– About 115 subscribers tweeted >10 times– Fewer than 60 tweeted >25 times– Six subscribers tweeted 100+ times each

CCCC 2012: Generating a graph

• NodeXL (Hansen et al. 2010)– Open source options exist, e.g., NetworkX

(http://networkx.github.com/; written in Python) • Generate graph• (Example used limited data)

CCCC 2012: First graph visualization attempt

Qualitative research is needed

• Systematic exploration of Tweets is fine, BUT• We need qualitative research regarding Twitter

account holders and their accounts– To what extent do members of candidate communities

feel or believe that the candidate theoretical communities are real communities?

– To what extent to those who retweet and @-reply to each other feel that those actions are constitutive of a community among them?

– To what extent should we include ‘institutional accounts’?

Challenges

• IRB approvals• Copyright concerns• Twitter (and other) terms of use• Participants’ willingness

Next steps

• Different data (problems with 2012 CCCCs data; problems with my involvement)– Go for several smaller-volume hashtags to find baselines

• Collaborators?– Computational linguists & computer scientists– Other writing studies researchers

Brian N. Larson@Rhetoricked / www.Rhetoricked.com

Department of Writing Studies, University of Minnesota