trendpreneur

Sometimes life just moves too quickly, y’know?

This post over at RWW is surprisingly thought-provoking for all it’s sponsored. (Aside: What a strange grammatical construction.) I’m not really sure I trust or even believe their random numbers, but the concept of implicit vs explicit voting for sites and the interaction of realtime vs old-school search are both interesting.

Implicit voting

So, implicit voting is where you give a site a silent thumbs-up. The most common way of implicitly voting for a site is just to visit it; this actually works in two ways, the action of clicking to get to the site, and what you do once you’re there. Explicit voting, on the other hand, is where you actively promote the site — for example, by tweeting/retweeting a link to it, or linking to it yourself.

Where does submission to a social news site such as Digg or Hacker News fit in? Well, my first thought is that submitting is explicit voting, but simply voting up (I agree with the submitter that this site is interesting) is implicit. By this matter you could say that retweeting links falls somewhere between implicit and explicit: if you model Twitter as a kind of Digg, with retweets as ‘votes’, you can see the parallels. Is del.icio.us’ing a link implicit or explicit? Bookmarking locally? Linking in IRC?

Anyway, that’s a case of detail.

Tracking explicit voting is fairly easy: look around for mentions of the URL. OK, there’s some magic involved in de-obfuscating and unifying references, but that’s just techie icing. Once you know who’s mentioned the URL and when you can do all sorts of computations to work out some kind of search ranking system. PageRank is just one approach, but there are modifications and things you can borrow from other search algorithms, especially HITS (one of my favourites!), that exploit the social graph as well. If you have more information — perhaps the entire tweet, or blog post, or whatever — you can even do language analysis and add that extra dimension of understanding on to the link. But fundamentally, you’re just looking at links.

It gets a lot more interesting when you try to work out an implicit measurement system. For votes that are click-throughs, there are ways to measure those, although not perfectly: bit.ly statistics, toolbar trackers, etc. For votes that are based within a site, you’re kind of stuck unless you’re a) the site owner or b) embedded in the user’s browser somewhere. The browser is the best place: there, you can measure if the user has it open in a tab for hours untouched, or if they keep flicking to and from it, etc, etc. But by the very nature of such things, you’re going to get a selective set of data. And what about the aforementioned pasting into IRC/IM/email, what about linking the fact I spent thirty minutes on a site with the fact I tweeted it and then I wrote a blog post about it?

It all comes back to user lifestreams, and the fact that today’s communication is far too disjointed for these types of measurements. Which is a shame, I think. Somehow we must be able to combine the wisdom of the crowd with an individual’s self-knowledge: I know that all these sites belong to me, so I know it’s just me voting for the site with a fairly loud mouth. (Unifying the voter isn’t even a necessary step, but I feel it’s important, especially when you consider fun things such as recommendation algorithms and shill detections).

Real-time information

Let’s assume we have some kind of implicit data about links as well as explicit. A key measurement axis we have is time – so we can spot voting spikes, clusters, etc. The long tail is an interesting quandary, though. Do people searching for a term want the most recent/trendy items, or the ones voted most authoritative over time? It depends on the user, and on the search. Even for a trending topic, a user might be searching for the background, not the latest happenings — so you have to offer both, surely, to satisfy user needs. At what point does a short term voting spike become part of a long-term vote? Would a smoothing function of time work?

There’s also the option to embed implicit voting within the search system itself, something like Google’s SearchWiki. If a site provided the information you wanted, you somehow give it a thumbs up. (Of course, users do this explicitly at the moment by tweeting links, though — at least with my own behaviour — that’s not that frequently linked to searching. I’m far more likely to tweet something I’ve browsed to or been sent). This would provide a trackable form of implicit voting, but still nothing near perfect.

User behaviour could be a problem, of course; what would cause a user to vote up a site? Interestingness? Relevance? I vote things up on Hacker News because they’re interesting, but I’d vote things up on Google if they were relevant. In a way, the real-time, sporadic flurry of retweets is a measure of interestingness and timeliness; the time spent on a site is a measure of interestingness and usefulness; the bounce rate and whether it shows up in search results at all is a measure of relevance. What are we measuring? Until we know that, we can’t rank!

The peer-to-peer system proposed by the RWW article’s author, Faroo, is one way of doing things, but I’m somewhat sceptical. I don’t think it’s going to be possible to get quality implicit voting data in sufficient representative quantities to do anything particularly accurate just yet, but as our habits and the way we search and browse change, it may become so.

Update: This TechCrunch post about the star rating distribution on YouTube — and, as a side link, this post about web reputation systems — are both interesting and vaguely related. Especially when you consider the proposed measure of implicit voting for YouTube videos: how many times you rewatch it, or whether you even finished watching it at all. (Is that accurate? If I watch a video for a few seconds, long enough to identify it as a decent version of the Black Knight scene so I can link it to a friend, does that mean I dislike it? Or are situations like that mere noise?)