Anthropology of user-generated connections and mobilization
I have been thinking about online mobilization by social movements for quite some time now. It is clear by now that social movements (environmental, women, labor, religious, and so on) try to recruit and mobilize their constituency online by building and disseminating collective action frames and while participation in social movements back in the old days seem to have been limited to activists, today a broader group maybe involved in online mobilization. How to research this? We can look at connections between people that are generated online and collective action online. A lot of research has been done about that by now but what is missing then is the social context. By only looking at those online connections and online collective actions we have some information about these individuals, but we do not exactly know what that means within the context of social reality online and offline. A good example research that goes beyond mere content analysis is Michael Wesch’s research on ‘user-generated organization’ (instead of only user-generated content) and ‘user-generated distribution’ for example in his Anthropological Introduction to Youtube. Wesch traces the spread of music by including re-posts and re-mixtures, visuals, stories and relationships by looking a computer meta-data and offline ethnography. This user-generated content is not about the actual content of the videos but the organization and distribution: user-generated connections.
This is important for research on social movements online as well. Social movements of whatever kind use the internet to convery their messages. In these messages a social movement identifies particular shared grievances, an outgroup against which claims for compensation are raised and particular actions to accomplish their goals for example by instilling a sense of purpose, effectiveness and identity among (potential) followers. They frame their ideas and proposed actions:
Talk To Action | Imagining Satan (Part Two)
Klandermans explains that the “social construction of collective action frames” involves “public discourse” where “media discourse and interpersonal interaction” interface with “persuasive communication during mobilization campaigns by movement organizations, their opponents and countermovement organizations;” and that the process of “consciousness raising” occurs “during episodes of collective action.”~33
Our analysis should not only focus on the content of these frames but we should also look into how such messages are distributed, re-interpreted and re-worked online and offline. Discussionplatforms, weblogs, Youtube, Twitter, Facebook and so on, they all provide means to forward messages (in the case of Twitter, re-tweet) messages to other people in ways that cannot be fully controlled by the people disseminating the original message. They can define the content (in this case the particular possibilities to connect) but users, within those parameters, can establish their identity, raise awareness among others, mock the messages and even gain fame. Key in this are reference groups or the question: who is the (intended) public. People at Twitter send tweets to their followers and for themselves (lets not forget the latter), on Facebook and MySpace we are sending to our friends and on Youtube to those who are friends and/or have subscribed to our channel.
Missing the social in on-line social life « meaningfulconnections
It is a community in the sense of displaying a set of institutions not reducible to individual motives or intentions but, instead, shaping those.
Twitter for example has been used make the recent protests in Iran available to a wider audience in unforeseen ways as well as in Canada. Also websites have been used to sparkle protest online sometimes turning a crowd of users into a lynch mob. A recent article in the Guardian gives an example from research to show how a crowd can turn into a lynch mob:
When the net’s wisdom of crowds turns into an online lynch mob | Technology | The Observer
The paradox of the “wisdom of online crowds” is that it only works in clubbable, relatively small groups of like-thinking minds. The reason why the richest and most productive audiences online are for the most arcane subjects – on the relationship between economics and law, for example, or how to care for cats – is because everyone involved feels part of an exclusive club dedicated to finding out more about the same thing.
However, it’s for exactly the same reason that many of these clubs can become breeding grounds for vicious tribalism. The brevity required for communication on Twitter does not lend itself to decorous etiquette, but neither is it the soul of wit to circulate snide, snarky tweets to an enthusiastic group of followers.
Too often the online audience separates into a series of rival gangs, each of them patting each other on the back and throwing stink-bombs at the other side. In this environment civility can disappear, with the result that those who do not take an extreme approach in offering their views decide that online forums are not for them.
When everyone is reinforcing everyone else’s opinion in an online echo-chamber, there’s little need to state a case or debate one’s opponent. It’s easier – like the schoolyard bully – just to abuse them. The other problem with online “communities” is that decisions about quality often become snagged in a highly conservative and self-reinforcing feedback loop in which everyone queues up to follow the leader.
In an intriguing experiment, three social network theorists at Columbia University used the web to invite more than 14,000 young people to rate songs by relatively unknown bands and download the ones they liked. The researchers began by dividing their subjects into two groups. The first group was asked to make their decisions independently of each other, while the second was allowed to see a rolling chart of how many times, in descending order, each song had been downloaded by others – telling them, in effect, which songs were the most popular among their peers.
When they came in, the results were as clear as day. Those who could see the download charts, the researchers discovered, tended to give higher ratings to the songs at the top of the chart and were more likely to download those songs. People tended to like songs more, in other words, if other people liked them. The result was to make the choices of those in the second group highly unpredictable, with a great deal depending on who rolled up to make their choices first. Identical songs were judged to be hits or flops depending on whether other people had been seen to download them earlier.
In their haste to rustle up an audience, mainstream institutions have not quite grasped the implications of all this, which is why they keep trying to flatter the vast, anonymous masses by inviting them in. The results can be ruinous.
This to a certain extent this could also explain why particular inciting messages online can be so dangerous. Maybe not always intended by those sending the message initially, it particular frame can encourage particlar interpretations and obscure alternatives in particular when frame-alignment occurs: when frames held by individuals are congruent with and complentary to those of the movement. Sometimes politicians, movements and infotainment websites use such knowledge to disturb other people’s website and online actions. Not by saying: go their and wreck it, but by merely quoting out of context making it in line with a particular frame and provide a link of someone’s profile or weblog. They know nasty comments (to say the least) will be made but they will not explicitly call for it. The original message then functions more or less the same way as the chart with songs in the example above. Other websites will take up the same message and re-interpret them but with the same links and thereby contributing to the online attacks. In order to understand the major impact of this, merely looking at content is not enough, the content must be related to larger frames ‘out there’ and user-generated connections; together they can explain user-generated mobilization.