- February
Posted By : Adrian Chan
Transient conversation networks on twitter

This is a re-post of a comment left on a post by Larry Irons, who commented on my recent post about HP labs’ research on twitter’s social networks.

My comment became a post unto itself.


Great post. i think there’s little doubt that in talk tools like twitter, which are time-based and conversational (of a form), the Dunbar number, while constant, probably includes a smaller number of active conversation participants.

Let’s say that some percentage of the Dunbar number is a close set of friends, with whom daily interaction is not necessary to sustain engagement and maintain the relationship — but with whom that conversation might be very grounding, rewarding, and meaningful.

There might be another percentage that is a set of peers — members of one’s network with whom coded and informative exchanges serve to surface, explore, share discoveries and create collaborations.

And there might be some percentage given over to new contacts, or more accurately, twitter partners in talk — transient network members with whom a relationship is latent but not yet enduring. People for whom we are available for talk, but with whom we have no explicit commitment to maintain contact. The conversational activity among members of this subset would be more governed by the etiquette and practices common to the social tool in use: twitter is not blog commenting is not facebook friending is not linkedin answering and so on.

I would like to see some research into twitter networks that is diachronic — which tracks conversation over time and correlates that with follower/following count.

I would expect that the number of transient relationships increases with an increase in followers/following. Does the Dunbar number hold steady? Or is it the wrong metric altogether for conversation monitoring? I suspect it’s the wrong metric. Our ability to sustain engagements would more likely be a matter of our attention spent on the site/service, our interest in it (which goes through phases), our “goals,” our experience to date and historically with the site (rising interest after adoption, plateau, fade out, rediscovery….), and of course the runs of talk themselves (talk increases around cultural news and events).

I would imagine that these conversation engagement metrics would also correlate to user personality types, and to the differences between monological, dialogical, and relational (Self, Other, Relational activity-oriented) “archetypes” of people in general.

To wit, a Self-oriented person might talk more if s/he believes he commands a bigger and more attentive audience. Stats revealing traffic to his site, click throughs on his links, retweets and @replies will embolden his/her engagement and make him/her more enthusiastic about tweeting.

An Other-oriented person might talk more the more @names and Directs s/he receives. Being inclined to respond to people, and to engage in one-to-one conversations, this user’s increasing following count will likely create more conversations — but possibly very passing and transient ones — as many of them are of course greetings and introductions (what we do when we meet people).

A Relational/activity oriented person might @name @name @name people more the more s/he sees group activity on twitter. This being the kind of interaction that is least well supported in twitter (multiple D messaging isn’t possible, for example, cutting out backchannel chat). Chat-style communication, which is necessary to create a sense of communal or group involvement and interaction, isn’t possible in twitter. So the relational/activity oriented user must sustain an awareness of social groups over time — this is a gate to group interactions. [I’m finding that Yammer, which I use with adhocnium members, is a twitter-chat tool for me. There’s no sense that a public reads our posts, and we conduct a slow chat over Yammer that in which, almost paradoxically, the @reply becomes a sidechannel!]

A smart marketing tool would thus not use influence, but would use conversation dynamics and transient properties of social media conversations and their participants, to determine not who to impress, but rather how to distribute by means of user-centric social media communication networks.

I’ll put this in Benjamin’s language: Communication in the age of its technical mediation is contingent no longer on the interaction handling of facework but on the loosely-coupled coordination of asynchronously sustained individual commitments. I nearly called them “commentments.” (reference is The Work of Art in the Age of Mechanical Reproduction – Walter Benjamin)

(This became so long that I’ll blog it on my site, too. Thanks for the inspiration — keep it going!)

Note: This blog post belongs to a series on “status culture.” The posts examine status updates, facebook activity feeds, news feeds, twitter, microblogging, lifestreaming, and other social media applications and features belonging to conversation media. My approach will be user-centric as always, and tackle usability and social experience issues (human factors, interaction design, interface design) at the heart of social interaction design. But we will also use anthropology, sociology, psychology, communication and media theories. Perhaps even some film theory.
The converational trend in social networking sites and applications suggests that web 2.0 is rapidly developing into a social web that embraces talk (post IM, chat, and email) in front of new kinds of publics and peer groups. User generated content supplied to search engines is increasingly produced conversationally. Social media analytics tools provide PR and social media marketing with means to track and monitor conversations. Brands are interested in joining the conversation feeds, through influencers as well as their own twitter presence.
This changing landscape not only raises interesting issues for developers and applications (such as the many twitter third party apps), but for social practices emerging around them. So we will look also at design principles for conversation-based apps, cultural and social trends, marketing trends, and other examples of new forms of talk online.
These blog posts will vary in tenor, from quick reflections on experiences to more in-depth approaches to design methodology for conversational social media.


  • Hi Adrian,

    Your post/comment, as always, is both interesting and stimulating in its attention to what is social about social media. Not enough people who use social media to inform strategy ask that question. I don’t disagree with your point that the Dunbar Number is limited as a way to explain the range of conversational dynamics over time. My point, following that of Ross Mayfield, was that social software such as Twitter allows us to manage those dynamics. I take that to be the point of your quote from Benjamin whose work I’ve read and appreciated. Am I wrong?

    As an aside, my personal favorite from Benjamin is his work on Baudelaire.

    The HP study essentially makes a point about reciprocity in directed social networks. My take on their findings, aside from the question of the Dunbar Number’s relevance, is that influence does not depend on the number of followers or friends of an individual twitter user. Connections, as you rightly note, are not engagement. Distinguishing the influence of people by the number of followers, or the number of people they follow, seems like the equivalent of traditional mass media’s concern with audience to me. Although this is probably a trivial, taken-for-granted assumption by informed users of social media, I can’t tell you how many ads I’ve seen lately for social media consultants in which the number of followers of a candidate is treated as an indication of how much social media savvy they possess.

    If we look beyond Twitter to the larger category of using social software in marketing, I agree with your comment that,

    “A smart marketing tool would thus not use influence, but would use conversation dynamics and transient properties of social media conversations and their participants, to determine not who to impress, but rather how to distribute by means of user-centric social media communication networks.”

    As far as your archetype distinction goes, I think talking about archetypes or personalities is treating the medium as psychological rather than social. People come to social media as individuals and, as you rightly note, the responses they experience shape their overall pattern of engagement. However, I’m more inclined to think their patterns of engagement don’t result from personality as much as their overall grasp of the communication afforded by the application architecture and the responses they experience over time as they participate in it by communicating with others.

    Your recent tweets indicate an appreciation for the dynamics of gift giving in social networks. Gouldner provided one of the best updates I know of to the classic literature of Durkheim, Mauss, and Simmel on the topic. He distinguished between the norm of reciprocity and the norm of beneficence to explain the dynamics of how gift giving kicks off relationships of exchange that reciprocity maintains. Unlike traditional, or industrial, society, gifting on social networks is almost entirely symbolic though that doesn’t mean the reciprocal connections made remain symbolic.

  • Larry,

    I’m with you on all points. And yes on the purpose of social software.

    On the matter of psych and social, however, it gets interesting. I’ve been developing a user-centric framework for social interaction that does use psychology, because i think we’re different, and have different interests and competencies w/ social media.

    That said, there are social practices in social media. However, I think they’re a direct result of the dynamics and interactions of people — thus they must reflect our interactions with each other.

    After drafting personality types, one would then map dynamics, and following that, relate them to tools used…

    A self-focused person may tweet for sake of ego, and relate strongly and positively to mirroring by his/her audience.

    An other-focused person may respond to tweets, be more conversational, and relate strongly and positively to validation/recognition provided by communication.

    A relational/activity focused person may use twitter, but also more active tools/sites, to find and engage in online activities, and relate strongly and positively to a sense of time spent, rules, games, structure, points, and so on.

    Tools support or mitigate the user experience in each case, and also produce or confound emergent social practices resulting from those kinds of dynamics.

    e.g. a lot of us are tweeting more, and are less on fbook. we dont need to maintain profiles, and we’re either monological or dialogical — self-talkers, or conversationalists. but twitter is not good for rounds and activities — cases where fbook apps, online games, etc, are better….

    it just strikes me that because each user has to sustain his/her own involvement and have an experience of online interaction that makes sense to him/her, s/he will choose fulfilling practices online. These will reflect/manifest psych makeup.

    Normative constraints, and activity structure, are thinner online and f2f — so the psychological is more important. We each make sense, and make sense of others, in greater isolation than f2f.

    Would you agree?

  • Adrian,

    The main concern I have with using personality as a way to analyze the patterns of engagement for individuals in social media stems from my belief that the same person uses different social media apps in different ways. I know I do. The same individual may use twitter to essentially talk to themselves, yet blog or write on fb in different styles. A focus on personality doesn’t provide a consistent way of explaining those differences. It would make an interesting research project, though I’m not sure how you could work the methodology.

  • Larry,

    Stylistic differences, moods, and changing use practices (as users “get it” over time, and as practices emerge on a tool) complicate matters, no doubt.

    Furthermore, to get good data one would have to obtain a holistic set of user accounts and uses of all his/her tools. Simply not feasible.

    That said, I believe a forensic psychologist of sorts could spot the personality even through stylistic differences.

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