Closer To The Ideal

danah boyd writes with great honesty about what it felt like when her audience began mocking her via a live, public Twitter stream:

Well, I started out rough, but I was also totally off-kilter. And then, within the first two minutes, I started hearing rumblings. And then laughter. The sounds were completely irrelevant to what I was saying and I was devastated. I immediately knew that I had lost the audience. Rather than getting into flow and becoming an entertainer, I retreated into myself. I basically decided to read the entire speech instead of deliver it. I counted for the time when I could get off stage. I was reading aloud while thinking all sorts of terrible thoughts about myself and my failures. I wasn’t even interested in my talk. All I wanted was to get it over with. I didn’t know what was going on but I kept hearing sounds that made it very clear that something was happening behind me that was the focus of everyone’s attention. The more people rumbled, the worse my headspace got and the worse my talk became. I fed on the response I got from the audience in the worst possible way. Rather than the audience pushing me to become a better speaker, it was pushing me to get worse. I hated the audience. I hated myself. I hated the situation. I wanted off. And so I talked through my talk, finishing greater than 2 minutes ahead of schedule because all I wanted was to be finished. And then I felt guilty so I made shit up for a whole minute and left the stage with 1 minute to spare.

I walked off stage and immediately went to Brady and asked what on earth was happening. And he gave me a brief rundown. The Twitter stream was initially upset that I was talking too fast. My first response to this was: OMG, seriously? That was it? Cuz that’s not how I read the situation on stage. So rather than getting through to me that I should slow down, I was hearing the audience as saying that I sucked. And responding the exact opposite way the audience wanted me to. This pushed the audience to actually start critiquing me in the way that I was imagining it was. And as Brady went on, he said that it started to get really rude so they pulled it to figure out what to do. But this distracted the audience and explains one set of outbursts that I didn’t understand from the stage. And then they put it back up and people immediately started swearing. More outbursts and laughter. The Twitter stream had become the center of attention, not the speaker. Not me.

Yes, I cried. Yes, I left Web2.0 Expo devastated. I hate giving a bad talk but I also felt like I was being laughed at. People tried to smooth it over, to tell me that I was OK, that it wouldn’t matter, that they liked the talk. But no amount of niceness from friends or strangers could make up for the 20 minutes in which I was misinterpreting the audience and berating myself. Nothing the audience could say could make up for what I was thinking about myself while on stage. So I went for a massage. And I spent 90 minutes trying to tell myself that I am a lovable creature. And when that wasn’t working, I told myself to suck it up and deal. I knew that if I could convince myself to look like everything was OK that eventually I would believe it. Or at least that it would all go away.

There are currently 206 comments on that post. Most people seem to feel that forcing the audience to read the Twitter stream (by projecting it onto a screen) served as a terrible distraction which disrupted boyd’s presentation.

I’m hoping this use of technology gets re-thought.

Clay Shirky is writing about algorithmic authority.

Algorithmic authority is the decision to regard as authoritative an unmanaged process of extracting value from diverse, untrustworthy sources, without any human standing beside the result saying “Trust this because you trust me.” This model of authority differs from personal or institutional authority, and has, I think, three critical characteristics.

First, it takes in material from multiple sources, which sources themselves are not universally vetted for their trustworthiness, and it combines those sources in a way that doesn’t rely on any human manager to sign off on the results before they are published. This is how Google’s PageRank algorithm works, it’s how Twitscoop’s zeitgeist measurement works, it’s how Wikipedia’s post hoc peer review works. At this point, its just an information tool.

Second, it produces good results, and as a consequence people come to trust it. At this point, it’s become a valuable information tool, but not yet anything more.

The third characteristic is when people become aware not just of their own trust but of the trust of others: “I use Wikipedia all the time, and other members of my group do as well.” Once everyone in the group has this realization, checking Wikipedia is tantamount to answering the kinds of questions Wikipedia purports to answer, for that group. This is the transition to algorithmic authority.

As the philosopher John Searle describes social facts, they rely on the formulation X counts as Y in C — in this case, Wikipedia comes to count as an acceptable source of answers for a particular group.

There’s a spectrum of authority from “Good enough to settle a bar bet” to “Evidence to include in a dissertation defense”, and most uses of algorithmic authority right now cluster around the inebriated end of that spectrum, but the important thing is that it is a spectrum, that algorithmic authority is on it, and that current forces seem set to push it further up the spectrum to an increasing number and variety of groups that regard these kinds of sources as authoritative.

There are people horrified by this prospect, but the criticism that Wikipedia, say, is not an “authoritative source” is an attempt to end the debate by hiding the fact that authority is a social agreement, not a culturally independent fact. Authority is as a authority does.

A very interesting conversation about the failures of crowd-sourcing (they are discussing the way images from the Library Of Congress (LOC) were tagged by users of Flickr):

This blog post caught my eye (hattip to george!):
“Lick This”: LOC, Flickr, and the Limits of Crowd Sourcing

The author argues that finding good content in the user-generated comments, tags, and notes is like finding a needle in a haystack. Using an LOC photo, the author explains,

There are 20-30 notes on the photograph and not one contains useful historical information to give context or help us understand the photograph. Most are throw-away jokes or comments, “I love this fabric!” and “Lick this” (referring to the woman’s forehead!). Most of the rest of the notes refer to the woman’s appearance or the composition of the picture. Almost useful is a little nested debate about the authenticity of the photograph–how staged was it?–but the discussion is hard to floow, involving hovering the mouse over each box to see the comment.

The author, Larry Cebula. a Public Historian at Eastern Washington University and Assistant Digital Archivist at the Washington State Digital Archives, argues,

The notes are mostly smart-ass remarks, the comments are empty, the tags are idiosyncratic. The frustrating thing is that there really is some crowdsourced gold withing the flood of junk, such as the transcriptions of hand-lettered signs in the windows of the Brockton Enterprise newspaper office in this photo.

There follows a good discussion in the comments, so go check it out if this subject interests you.

I looked on the comments on some of the images, and I agree that there is a lot of waste. Some photos have 20 or more people who simply posted (on the photo itself) “Cool pix!”

For my part, I am a big believer in the potential of crowd sourcing, but I don’t see many sites capturing that potential. I think crowd sourcing works best when the crowd can be offered some kind of incentive, perhaps with a raffle, or a prize, or something free, or an outright offer of money. Some kind of incentive would, I think, raise the average quality of the responses.