Never doubt that a small group of thoughtful, committed citizens - - PowerPoint PPT Presentation

▶

Sep 23, 2022 367 likes •740 views

Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has. --Margaret Mead Thank You R Hackers of NYC Harvesting & Analyzing Interaction Data in R: The

SLIDE 1

“Never doubt that a small group of thoughtful, committed citizens can change the world. Indeed, it is the only thing that ever has.”

-Margaret Mead

SLIDE 2

Thank You R Hackers of NYC

SLIDE 3

Harvesting & Analyzing Interaction Data in R: The Case of MyLyn

Sean P. Goggins, PhD Drexel University

utdoors@acm.org

MyLyn Research Collaborators: Peppo Valetto, PhD (PI) & Kelly Blincoe

SLIDE 4

I Study Small Groups

I use electronic trace data, interviews, field notes, electronic content & surveys for raw data

SLIDE 5

Coolest Open* Data to Me

❖

Group’s Emerging & Evolving

❖

Group Formation & Development

❖

The long tail of social computing, which I describe as everything *except* Wikipedia & Facebook

❖

Groups constructing knowledge, creating information and forming identity.

*Available, but not always easy to get in an analyzable form

SLIDE 6

Points

❖

Harvesting Small, Open Data [MyLyn]

❖

Analyzing

❖

Temporal Changes in the MyLyn Network

❖

Work

❖

Talk

❖

Libraries Used & Source Code

❖

StatNet

❖

iGraph

❖

TNET

❖

R Sourcecode and Data will be available for download at http://www.groupinformatics.org . If you use this data or scripts please cite:

❖

Goggins, S. P., Laffey, J., Amelung, C., and Gallagher, M. 2010. Social Intelligence In Completely Online Groups. IEEE International Conference on Social

Computing. 500-507. DOI=10.1109/SocialCom.2010.79.

❖

Blincoe, K., Valetto, G., and Goggins, S. 2011. Leveraging Task Contexts for Managing Developers’ Coordination. Under Review.

SLIDE 7

Data for R

An Example From the MyLyn Project

SLIDE 8

More About MyLyn: http://tasktop.com/blog/ http://www.eclipse.org/myly n/ Bug Database HTML Parser MySQL Database MyLyn Context Uploads Wor k Tal k .zip file Tal k Tal k

SLIDE 9

Talk Cues

Tal k Wor k

SLIDE 10

Coordination Requirements & Dependencies

MyLyn Data Has 2 Advantages for Analysis compared to source Control systems analysis: 1. You see files *viewed* together 2. Discourse on a Bug is directly connected to the files read and edited 1. Closer connection between analysis of work & talk. Tal k Wor k

SLIDE 11

Harvesting Data for R

An Example From the MyLyn Project

SLIDE 12

MyLyn Interaction Datamart

Interaction Warehouse MyLyn CANS ETC Tal k Wor k Tal k Wor k

SLIDE 13

Analyzing Open Data with R

An Example From the MyLyn Project

SLIDE 14

Analysis Tools

❖

Eight Mylyn Releases (Temporal Analysis)

❖

R Packages Used

❖

TNET

❖

iGraph

❖

Statnet

SLIDE 15

Weighted Network: TNET

SLIDE 16

The Dense Graph (Work)

❖

Developers create a dense graph. Not a complete graph, but dense.

Wor k

SLIDE 17

A Sparser Graph (Talk)

❖

Commenter's create a sparse graph

Tal k

SLIDE 18

Release One (2.0) Analysis

Cod e Discussion Wor k Tal k iGrap h Release 1

SLIDE 19

STATNET for Discussion

❖

StatNet

Red = Bug Commenter Blue = Bug Opener

StatNET Tal k Release 1

SLIDE 20

Release One

Work & Talk

SLIDE 21

Release 1 (2.0) iGraph & Statnet

Tal k Cluster s In Degree & Out Degree

Red = Bug Commenter Blue = Bug Opener

iGrap h StatNET Release 1

SLIDE 22

Google Summer Coder

Release One (2.0): Filtered

Cod e Discussion 304, 373, 399 & 143 form The Strongest Connections In both networks

Red = Bug Commenter Blue = Bug Opener

Tal k Wor k Release 1

SLIDE 23

Release One (2.0): Filtered

Cod e Discussion 304, 373, 399 & 143 form The Strongest Connections In both networks

Red = Bug Commenter Blue = Bug Opener

Google Summer Coder Tal k Wor k 457, 391 & 159 – Comment & Open Release 1

SLIDE 24

Compare Over Time

First & Last Release

SLIDE 25

Release 1 (2.0) Compared to Release 8 (3.3)

Tal k 304, 399, 143, 159, 173, 373 399, 118, 304, 159, 391, 416 StatNET & ordinary plotting Release 1 Release 8

SLIDE 26

Release 1 (2.0) Compared to Release 8 (3.3)

Wor k Two disconnected Graphs in release 8 304, 373, 399 & 143 Release 1 Release 8 143 & 304 disengaged Or missing entirely iGrap h

SLIDE 27

Release Eight

Work & Talk

SLIDE 28

Release 8 (3.3): Filtered

Cod e Discussion

Red = Bug Commenter Blue = Bug Opener

Tal k Wor k Nobody is “Just Blue” Release 8

SLIDE 29

Release 8 (3.3): Filtered

Cod e Discussion

Red = Bug Commenter Blue = Bug Opener

Tal k Wor k Notice 416 in Talk & Second Coder Graph Release 8

SLIDE 30

Tal k Cluster s In Degree & Out Degree

Red = Bug Commenter Blue = Bug Opener

iGrap h StatNET

Release 8 (3.3) iGraph & Statnet

399, 118 & 159 are significant, But play with different clusters of Other people.

Blue Cluste r Release 8

SLIDE 31

Releases One → Eight

High Level Views Over Time

SLIDE 32

Discussion, Releases 1 – 8

Where there is no color, There are multiple, incomplete Graphs.

SLIDE 33

Code, Releases 1 – 8

One Possible explanation: A few central People who slowly but Observably begin to engage Other contributors in An open source software Development project. Structure evolves Key Groups Evolve iGrap h

SLIDE 34

Next Step: The Story

But that’s the research part, not the cool “R Stuff” Part

SLIDE 35

The People

399 304 159 143 373

Our next step is piecing together a narrative about the groups that emerged on this project, and describing each of the individuals. This is all open data. When we finish this part, we will publish one or more papers. For now, Let’s look at the cool “R Stuff”

SLIDE 36

Interaction Traces from Small Groups: The Case of MyLyn

Sean P. Goggins, PhD Drexel University

utdoors@acm.org

Collaborators: Peppo Valetto, PhD & Kelly Blincoe Questions? In the after session.