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?)

While I attempt to scrounge up time to work on a longer post about journalism, these galleries of abandoned places caught my eye.

Firstly, I now want to go and visit every one of these places (except perhaps the ones that will kill me).

Secondly, I couldn’t help but think of the parallels to the Internet. How many corners of the Internet are there that are equally forlorn, unvisited, unloved; existing only as a memory in someone’s mind (and impossible to trace once you remember it)? This is even more apparent in proper-like virtual worlds, such as Second Life, flawed though it may be; you can fly around for hours without seeing a soul, just prims and textures and buildings and imaginations unleashed, that people have wandered away from and forgotten.

Games, too; for hours on end, even months and months, you inhabit this other place only to wander off the moment you finish the game / unlock the secrets / get a girlfriend. All it takes to go back is to execute some software, and you’re right back where you started – load a savegame and everything’s as it was. MMOs change with more volatility than this, of course – while I may have considered World of Warcraft practically a second home at one point, I log in these days and am frankly bewildered by the place. It’s changed, and so have I. Not only is the ’space’ I inhabited in the world abandoned, so’s my identity.

So: How can we find fantastic abandoned virtual spaces, and how can we record them? How do you record an experience? Video and images are the best we can do with real spaces, but when the entire experience is digital — but it isn’t, is it? There are more factors than just pixels at stake, especially with these other worlds. And you definitely need to account for the observer changing as well as the observed.

Today I met a personal goal of mine that I started last summer. It hasn’t been an easy ride, but by gradually setting smaller goals while focusing on the big picture, altering fundamental behaviour and habits, keeping things interesting with experimentation and adding a social element, it’s seemed a lot easier than it really was. I’ve learned a lot about goal-setting and achieving as a result, but many blogs cover these topics — what I haven’t seen written about as much is what happens next.

Depending on the goal, meeting it is everything. Once that deadline’s over, that mountain scaled, you’re done. For more generic, ongoing goals – things like “I want to manage my money”, “I want to get fit”, you’re putting changes in place while achieving the goal that will help you maintain the end state once you get there. However, without anything to motivate you to continue, it’s easy to slip from the mark.

What I’ve done is extend my goal. I’m happy with what I’ve achieved, but having something slightly further away – and a lot harder – to work towards is going to keep me motivated. I’ve also built in other life goals into this one, combining fitness goals with personal finance and development ones, so I’m motivated to work on other areas as well as focus on the one I know I can achieve.

I will briefly reiterate the most common piece of life-hack advice when it comes to goal setting: be specific and set a deadline (yes, yes, be SMART). It makes the moment when you reach that goal very tangible, and also extremely awesome.

It’s a nice dream, that of the aspiring problogger. Address a fascinated Internet audience daily about a topic you – and they – love, while earning money? Too good to be true!

If you work as part of a blogging team for a large site, chances are you will be tasked to come up with multiple daily posts on the blog’s topic. While inspiration and introspection can get you so far, the job of keeping content fresh and covering breaking news means you need to establish good work habits, particularly if you’ve only blogged as a hobby before.

Blogging breaks down into handy steps:

• Find something interesting to write about
• Write about it
• Add appropriate images
• Add metadata: internal and external links, tags, etc
• Schedule
• (Optional) Promote
• Babysit – Edit, update, monitor comments

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Due to the WiFi seriously dying on me today (though it was mostly fine yesterday – go figure) I tended to tweet today’s sessions in bursts — the bursts corresponding to me being online and actually in the session, rather than offline but in the session, online but not in the session, or offline talking to someone interesting. Anyway.

In a reverse kind of order, here are a few Day 2 goodies. The day ended on a Gillmor Gang live session which consisted mostly of overweight loud Americans shouting at each other — with a surprising detour into a quiet and all-too-brief discussion on news filtering algorithms. One of the things about this panel that appeared on the Twitterstream was the fact it lacked women. I’ve also seen a few comments here and there about the number of women at LeWeb. Plus, a brief exchange that sparked at least one tweet — when asked what the audience liked about Google’s presentation at LeWeb, it’s apparently perfectly fine to note that the (female) speaker was “hot”.
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