Agents of Diffusion – Insights from a Survey of Facebook Users, Rebecca Ermecke, Philip Mayrhofer, Stefan Wagner

On viral adoption on Facebook

Beyond Microblogging: Conversation and Collaboration via Twitter, Courtenay Honeycutt, Susan C. Herring

How people use the @ reply mechanism in Twitter. Did you know that 30% of messages get replies?

A Conceptual and Operational Definition of ‘Social Role’ in Online Community, Eric Gleave, Howard T. Welser, Thomas M. Lento, Marc A. Smith

A theoretical paper on determing social roles in an online community. Best paper award for the Track.

Hello Stranger! A Study of Introductory Communication Structure and Social Match Success, Daphne R. Raban, Stephen T. Ricken, Sukeshini A., Grandhi, Nathaniel Laws, and Quentin Jones

Social introductions.

Mycrocosm: Visual Microblogging, Yannick Assogba, Judith Donath

Overview of the mycrocosm service.

Cyber Migration: An Empirical Investigation on Factors that Affect Users’ Switch Intentions in Social Networking Sites, Cheng Zengyan, Yang Yinping, John Lim

What triggers migration between different social network sites?

A Life Cycle Model of Virtual Communities, Elham Mousavidin, Lakshmi Goel

The lifecycle and stages of an online community

Knowledge Workers and the Realm of Social Tagging, Ralph Boeije, Gwendolyn L. Kolfschoten, Pieter de Vries, Wim Veen

Social tagging by workers.

Groupware for Design: an Interactive System to Facilitate Creative Processes in Team Design Work, Arjun Venkataswamy, Rajinder Sodhi, Yerkin Abdildin, Brian P. Bailey

How do you design groupware that is specifically supposed to support the creative process of team design work?

Cultural Diversity, Perception of Work Atmosphere, and Task Conflict in Collaboration Technology Supported Global Virtual Teams: Findings from a Laboratory Experiment, Souren Paul, Sumati Ray

I already blogged about this one and how it is an interesting finding about conflict and cultural differences in distributed teams.

Blogs Are Echo Chambers: Blogs Are Echo Chambers, Eric Gilbert, Tony Bergstrom and Karrie Karahalios

Are bloggers talking to like-minded bloggers?

Employee Adoption of Corporate Blogs: A Quantitative Analysis, Sunil Wattal, Pradeep Racherla, Munir Mandviwalla

Model of when/why employees start blogging.

Monetizing the Internet: Surely There Must be Something other than Advertising, Eric K. Clemons

Great title and interesting discussion of some other possibilities for making money on the internet, besides through advertising.

“Cultural Diversity, Perception of Work Atmosphere, and Task Conflict in Collaboration Technology
Supported Global Virtual Teams: Findings from a Laboratory Experiment”
by Souren Paul and Sumati Ray

The main findings:

• perceived work atmosphere is positively related to participation in work groups
• participation increases task conflict which is required for high quality decisions (see pre-existing literature on the value of conflict in teams!)
• the findings highlight the importance of developing favorable perception of work atmosphere inculturally diverse virtual teams.

Conclusion: perceived work atmosphere is critical in shaping the performance of virtual teams

Here is the paper’s official abstract:

In this paper, we focus on work atmosphere and
conflict in global virtual teams and report the
findings of a laboratory experiment that involved
twenty-seven cross-cultural virtual teams. The
members of the teams used IBM’s Lotus Sametime to
work on decision-making tasks. The findings of the
study reveal that in collaboration technology
supported virtual teams, the cultural heterogeneity of
the team members influences their perceptions of the
work atmosphere, which in its turn influences
members’ participation in group work. We also find
that the number of occurrences of task conflict
related discussion among the group members is
positively related to their participation in group
work. The findings of the study are interesting and
provide motivation for future research on work
atmosphere and conflict in virtual teams.

Babble is similar to a multi-channel textual chat system except that its conversation persists over sessions, allowing both synchronous and asynchronous talk. Its aim is to support everyday, opportunistic interaction among members of a workgroup. [link]

I never used Babble (it was built in 1997 and used internally at IBM before I joined), but it is one of the projects that inspired my thesis research. Its key concept is social translucence:

Social translucence is the idea that we should make some (but not all) cues about the presence and activity of users of digital systems available to one another. [link]

Twitter fits this criteria and has a lot of the same features as Babble:

• you can communicate either synchronously or asynchronously (txt, mobile, browser, etc… )
• you can see who is present (Twitter’s following and followers pictures)
• you can see who is active (Twitter’s time-sorted list of who said what)
• you can selectively determine who sees your posts (direct messages, @ messages, broadcast)

The main differences between Twitter and Babble are:

• Babble has a graphical visualization showing who is currently engaged in the conversation
• Twitter’s “groups” are not bounded. Even though you and I might be following each other, my group is probably different than your group. It is possible there could be 0% overlap, but we could still communicate.
• The cultural norms of Twitter are pretty distinctive in that people use “tweets” to give casual updates on their latest thoughts, ideas, opinions and experiences. The updates are not solely focused on workgroup interaction, like Babble was conceptualized to be for. But I see this as Twitter’s unusual strength.

It would be great if Twitter had some social visualization capability. (Maybe it does? Anyone know?) Then it really would be Babble, adapted for the flexible, ad-hoc type of collaboration and communication we do in this post-2000 world.

If you haven’t read them already, I highly recommend these papers on Babble and social translucence:

Socially Translucent Systems: Social Proxies, Persistent Conversation, and the Design of “Babble”, by Thomas Erickson, David N. Smith, Wendy A. Kellogg, Mark Laff, John T. Richards, Erin Bradner. In Human Factors in Computing Systems: The Proceedings of CHI ‘99. ACM Press, 1999.

Social Translucence: An Approach to Designing Systems that Support Social Processes by Thomas Erickson and Wendy A. Kellogg. In Transactions on Computer-Human Interaction. Vol. 7, No. 1, pp 59-83. New York: ACM Press, 2000.

As you watch the debates, even though CNN has told you that these uncommitted Ohioans are not a statistically representative sample, you are influenced by their knob turning. Just like you can be influenced by your friends’ laughter and comments in a face-to-face group setting, as you watch those lines moving up and down, you are convinced that McCain is angry and Obama is presidential. There is lots of experimental evidence of this from previous elections (read the OTM transcript).

My PhD thesis focused on this exact issue: real-time visualizations of social behavior and how this information changes the dynamics and your perceptions of an interaction. For my project, Second Messenger, I focused on small, face-to-face groups receiving feedback about their group dynamics, but the phenomenon is universal and extends beyond social visualization. Behavioral economists refer to it as framing.

Besides being interesting to think about, it is important to consider the influence of the squiggly lines. First, CNN should be aware of its power to influence viewers. Second, viewers should be aware that their attitudes can be manipulated by this relatively simple and unrepresentative data point. They discussed this a bit on OTM:

BOB GARFIELD (interviewer): We have established that the squiggly lines do not represent any statistically significant data. And … we’ve established that people are influenced by the squiggly lines. Does that not mean that it is journalistically irresponsible to put them on the TV? I mean, is CNN doing the right thing in adding this feature to its coverage?

SAM SCHECHNER: We’re social animals. We process information in the context of the people around us. We do watch our debates with friends or at a bar or in some sort of group environment, frequently, in part because we want to hear the reactions of other people. That’s in part why … you and I can’t stop looking at that CNN line on the bottom of the screen.

I think it’s hard to argue, necessarily, that it’s a bad thing. In fact, the people who do it – the professors at SMU – see it as a way of shifting some of the balance of influence from pundits, who have been shown to be able to have a pretty strong effect with their pre- and post-debate spin and how they set expectations and frame the debates to actual voters.

I’m not sure I agree that uncommitted Ohio voters are the antidote to polarizing pundits, but something to think about.

More information on the report:

WSJ, 10/10/08: “When Your Political Opinion Isn’t Yours Alone” Broadcasts of Political Debates That Include Live Audience Feedback Can Influence What You’re Thinking

More on my research:

Joan Morris DiMicco, Kate J Hollenbach, Anna Pandolfo, Walter Bender. (2007) “The Impact of Increased Awareness while Face-to-Face.” Special Issue on Awareness Systems Design, Human-Computer Interaction. Volume 22 (2007), Number 1.

Edison was one dedicated inventor! Not only did he work every day of his long life, including while vacationing in Florida, but he also never slept more than two hours at a time, taking continuous cat naps throughout the day.

Edison’s career began with a number of great successes (e.g. the lighbulb :), which brought him fame and wealth, and this led to him forming a large R&D lab in Menlo Park, NJ. Later in his career, he had very few successes and a great number of expensive failures, such as trying to grow rubber domestically during WWII.

The tour I went on attributed some of his failures to his management style. When he began his career, he worked very closely with a small dedicated team, where each person was responsible for one part of the puzzle, but everyone knew what everyone else was doing at all times. When he built his lab at Menlo Park, the idea was that the lab would be 10 times as big as his prior lab, and would produce 10 times the number of inventions. No such luck. Because Edison insisted on being involved in all parts of the invention process, the work was no longer done in small, focused, close-knit teams, but was rather run from the top-down with him involved in every project. And innovation suffered. I thought it was really interesting to hear that one of America’s greatest inventors had difficulty letting innovation flourish in others. It may be that innovation only happens in small, independent teams. Can you think of some counter examples?

These are pictures I took of his lab in Florida, which kind of reminded me of my high school chemistry lab. (I did not go to high school in the 1940’s — my school was just that out-of-date! )