I’m working on a wee project combining video and music of the times, and I’d love to collect memories from the time. Wow, that makes it sound like history… Anyway, we all have fond flashbacks to the age of heat reactive tie-dye shirts, and although my middle-of-the-road upbringing is chock full of happy reminiscences, I want more variety!

So, if you have a story, a memory, a moment to share from around 1992 to 2000 – for me, my high school years – and especially if it’s closely related to (or described by) a song popular in the same time period – please share it with me :) You can comment here/on Facebook, or email me (mail at jennielees dot net). Oh, and it doesn’t have to be meaningful or funny – just true.

Hmm, should I start a site to collect these or keep them private?

Here’s mine: our endless school assembly pop culture parodies. I remember dressing up as Mikey from Boyzone (I secretly wanted to be Ronan), doing a Grease ripoff for a departing Latin teacher that was, in retrospect, dangerously suggestive, and for some reason I posed as Renton from Trainspotting in front of our class blackboard in N3. God knows why. Anyway, cue montage! I wonder if kids these days do the same type of thing…

The logical extension, which Twitter will roll out this quarter, is attaching places to tweets. Hmm, sounds somewhat familiar…

Thing is, this leads to an interesting gap. Instead of check-in fatigue, this could reduce the need to check in at venues; send a tweet, and it gets you automatically checked in at that venue, maybe even posting your tweet as a tip for that place.

One issue is the back-channel that occurs when you check in using a service like Foursquare. It’s good to get points and badges and shiny things; if check-ins are automatic, you don’t get any of that.

Still, it’s a nice concept for someone to implement, one day. Someone who isn’t me.

I usually like TechCrunch for the most part, but these two articles have annoyed me: ‘Sentiment is split on the iPad‘ and ‘More iPad Sentiment Analysis‘. Both use poor, crude methods of sentiment analysis to produce posts full of fluff and pretty graphs. Result? Whatever point the blogger wanted to make. (You know what they say about statistics).

A quick rundown of the problems: Spurious classification algorithms, poor data sizes, and non-credible results. An algorithm that analyses every piece of traffic on Twitter and comes up with “51% positive, 49% negative” is Just Plain Wrong. There’s going to be a ton of stuff in the middle, unclassifiable, undecided, even just retweets of blog posts with the word in the title, and any graph should reflect that as well. Stripping out the neutral, a result of 51/49 just seems completely nonsensical to me, and I’ve been working with Twitter sentiment for a long time now.

It’d also be very interesting to know what methods the classifiers use, probably available with some digging, but I fear it’s manual keyword lists that some poor sod had to draw up — “hmm, I think if someone says the iPad is ’stupid’ that’s probably negative, yah?”.

Attensity does better, but what on earth does “not thrilled” mean (weak negative?) and again, where’s the neutral or noise aspect? It’s valuable to know just how many tweets were about the iPad, and how many of those were about sentiment. What if a TechCrunch headline with a negative word got retweeted 2000 times? That’s what we in the trade call “skew”. Plus, classifying on a small sample is just crazy. Why? Surely it can’t be computational limits; were these the only tweets with sentiment information? That’s useful data! Why throw it all away…

It also looks like there are some great leaps in logic in terms of distinguishing between “Like the iPad because it might replace iPhone” and “Don’t like the iPad because it won’t replace my iPhone”. How do you automatically extract the difference between “Can’t replace battery” and praise for the battery life? Sigh.

Plus, there’s the key mistake of not showing error, accuracy bounds, or mistakes. Both posts assume the algorithms are 100% correct. While that makes for some pretty graphs, it just isn’t true, and with no idea of sample size or result size (e.g. for the battery category above) then a result of 5% could just mean one out of a total of twenty tweets with the word battery in was negative. It’s the same for intent to purchase. Not every tweet will have any kind of intent, so if you just took the tweets containing “will” “buy” “iPad” or “won’t” “buy” “iPad”,

Of course, the reason I’m most annoyed at these posts is that I could have helped put together a custom dataset and classifier to provide much more detailed data, and didn’t. But while I can’t go back in time and change things, I can at least point out the flaws in using off the shelf graphs to meet your daily post quota as a pro-blogger.

There have been arguments raging on and offline about paywalls, the commons, old media versus new media, and ‘information should be free’ for — well, it feels like forever now. One of the (many) components of new media under fire is the army of filthy idea-stealin’ bloggers, people who merrily subscribe to paid content and then go and paraphrase it on their free-to-view blogs (or in some cases, just copy it). Paul Carr makes an excellent point about the commoditisation of facts, the human need for information and thus the Internet hivemind’s tendency to trend towards free.

Information being free is good, for obvious reasons, unless you’re someone who wants to get paid to create it. There are plenty of arguments for well-crafted columns, investigative journalism, paid political pundits and so forth. But here’s a thought about the oft-maligned practice of reblogging, rephrasing, and retweeting.

Language is variable.

The more ways an idea or piece of information is expressed linguistically, the easier it is to find — it’ll match far more search queries, as a simple starting point. Although, in an echo of the Sapir-Whorf hypothesis, perhaps expressing an idea in multiple languages, or with different phrasings and words, could change the way people think about the idea. Even if this happens, the idea reaches far more people than it would have if it were confined to one site, in one language, by one author.

From Google’s point of view, if someone takes a New York Times article, paraphrases it, and links back to it, the data miners jump for joy. Beautiful, delicious data. We learn new things about the relationships between words and concepts — maybe one article said climate change but another global warming. The link-back gives us contextual data that can help too. (Linking to a climate change article with the text “This article on global warming”, for example).

Of course, paraphrasing and rewriting has been going on for years, a staple of the essay or lit review. But as with voice recognition, having the power to implement and use a feedback loop at world-scale is a mind-blowing thing. Google has the power to build an entire semantic web out of paraphrased blog posts, and that’s before we even look at contextual links in Wikipedia or Twitter link summaries. If that’s scary, just think of the magic that happens when you search for something and get a result that isn’t the exact terms you entered, but is the exact concept. With a bit of data, intelligence and an army of semantic web PhDs, it just could happen.

I loved watching this talk by Tom Scott from London’s recent Ignite 2 event. (The other talks are also online – cheers, Daniel).

On Tom’s website, he bemusedly answers the FAQ “Is this fictional?” — well, it’s utterly believable, and certainly threw today’s technology, from social media to flashmobs via 4chan, starkly into perspective for me. Excellent job, brilliantly delivered.

I’m trying to catch up on the notable talks of LeWeb — so far I haven’t seen anything revolutionary come out of it, but a few interesting things, so it’s hard to figure out what to watch (especially since my attention span for online video is absolutely terrible; I prefer transcripts 90% of the time). Still, this talk caught my eye as danah’s work is pretty awesome. It’s definitely worth a watch (and it has a transcript/crib, yay!).

There were two main themes as I understand it; firstly the concept that we all see the Internet differently, and secondly the question of whether we are looking or not. The former is something that’s fascinated me for a while. Being able to see someone else’s world through their eyes — through unedited honest life-stream social media updates, through their Facebook photos, etc — can verge on gratuitous voyeurism. It’s taking the idea of a reality celeb to a new level, in a way. Internet superstars don’t have to be A-list or royalty, they can just be the sort of people whose wry observations about daily life (be it a blog, YouTube channel or even Twitter stream) are entertaining and a form of escapism.

As well as e-stalking interesting people, the more useful (and far more voyeuristic) side of this is e-stalking people I vaguely know. Catching up on old friends’ or past acquaintances’ lives via their LinkedIn, Facebook photos, etc; it somehow seems all right to live vicariously through people if you’ve actually met them. Kinda.

The second theme is about the ugly stuff. There’s a lot of nasty things that happen in this world and increasingly, victims are talking about them online. Bullying isn’t more prevalent now than it used to be, but it is more visible, and the problem is that people assume that there’s an automatic equation between stuff being online and stuff being seen. As anyone who’s ever written a blog knows, you can often be writing for an audience of one, and even if you have a hundred Myspace friends, there’s no guarantee anyone’s listening. Or that their reports to the authorities of your accounts of mistreatment at home will ever get taken seriously.

It’s sort of a cross between Neighbourhood Watch and “eyes on the street“. We need to look out for the disturbing stuff, violence, crime, etc., and use that constructively to help people, to open up conversations rather than jump to conclusions. Because appearance is everything, and someone posting about drug use or self-harm on their Livejournal might be doing it to fit in, not as a cry for help. It’s so hard to find where the boundaries are between the projected image you want to create online, and the real self underneath. The move towards real-time stream-of-consciousness updating kind of helps, but even a simple tweet such as “Help me” needs to be taken in context.

I wrote a while ago on the topic of implicit versus explicit voting by users; depending on how hard an action is to take, you can measure and model the data about it differently, and being aware of the distinction is important when trying to create prediction systems, analytics, site improvements, etc.

This topic has come back up in a recent post on Sexy Widget which looks at how the simplicity of foursquare’s check-in concept makes the pyramid of engagement (above) more accessible to users. This is good, because the higher up a user goes, the more they get involved, which leads to useful things like: stickiness, community, evangelism, fans, virality, better and clearer data, better conversations, more customers, etc. (Depending what your site needs and values, of course.)

The interesting thing here is the shift from simple one-click actions carrying limited, passive-voting data to carrying far more implications thanks to the wider context of the action. The more we encourage such low-level activity and plug it into a bigger picture, the more data and benefits we can reap from it. So: think about how your app or service can encourage users to take simple-yet-meaningful actions, to explicitly vote for stuff in actions that previously were mostly implicit, and you’ll get better information about your users as a result.

I like The Big Bang Theory, identify closely with the characters and setting, and have even lived with real physicists (oh my) so can attest to its truth in various ways. So it was remarkably cool to stumble across @sheldoncooper and friends tweeting about their lives, entirely in-character and entirely well done. I also recently saw that the paragon of schoolboy charm Molesworth had found his way on to the platform (chiz chiz).

This tickles all my spidey-senses about ARGs, improv, the blurring of fiction and reality, and how easy it is to be someone else on the Internet. Still, if toasters and plants and ovens can tweet, why not characters? It’s just a new tool in the arsenal of mixed-media marketing, of sucking people into your world, be it via a miniseries or games or interactive fiction. Old-school (ha!) ARGs tended to have a blog as the main point of contact between the protagonist/’eyes’ of the story, and the audience, precisely because blogs were easy to concoct. Taking that on to social networks is an obvious step, and content-experiments like Dinner_Guest are emerging to challenge what we think is real.

Keeping up a Twitter account in-character probably isn’t too hard in and of itself, but when viewed as part of a larger story — such as an ARG or other interactive fiction arc — I imagine it must be pretty exhausting. It’s basically constant improvisation; to truly seem real, you need to tweet like a real person, which means you can’t just tweet from 9 to 9.30 AM and have that done for the day. I think there’s some crossover here, as well, with brand identity and trust, and maintaining a ‘face’ to a corporate, and co-tweeting consistently… Where does the real user begin and the brand stop? Where do the fictional character’s tastes start reflecting the author’s?

I’m unsurprised to see there’s already been attempts at a Twitter ARG, but this platform has such potential for the form that’s as yet unrealised; there’s this wonderful ready, willing, participatory audience who are aware of each other without any need to set up forums or wikis. Oh yes, there’s definitely oodles of possibility here. I can’t wait to see what happens with it.

This change, which apparently happened last Thursday along with the retweeting API and other fancy things, completely passed me by. So that’s why I’m talking about it nearly a week later. The big news? Twitter’s changed its default prompt, the question that every tweet is meant to answer, from “What are you doing?” to “What’s happening?”.

I think it’s interesting. Many tweets bear no resemblance to the ‘old’ question — conference and sporting blow-by-blow commentaries to interesting links, pieces of news and gossip, questions to the twitterverse, and random musings. Some did, of course; the almost canonical ‘eating cereal for breakfast’ and ‘in a queue behind the most annoying woman ever’ type of message, the daily commentary on one’s life that, interspersed with commentary on the wider world, is what makes Twitter so fascinating.

It’s not carefully considered and drafted news tweets or observations on the best MLM strategies that make Twitter fun, it’s the unedited stream of pure human honesty that flows from our hearts via our fingers with nary a look-in from our minds. It’s the things that annoy us, the fact that it’s wet outside, the frustration that Jedward didn’t get the boot (or the disappointment that they did). Certainly from the point of view of data-mining, heartless though it may seem, people being… well, people… is an intriguing fishbowl to glance into.

The fact that most people basically ignored the old ‘question’ means that changing it probably won’t fundamentally change Twitter. It more mirrors, rather than propels, a shift in the way Twitter is being used by citizen journalists and commentators the world over — and an attempt to get away from the dogged old ‘breakfast’ use-case that even I trot out time and again. Maybe it will make people stop and think a little when they’re about to post some banality or other, though, and that saddens me just a little.

Edit: It’s also interesting that Facebook’s question is “What’s on your mind?”, staying me-centric; this reflects the difference between the two services rather well, I think.

So, yesterday’s Monitoring Social Media conference is over, and all I have to show for it is a heightened case of RSI (ok, ok, I jest). My live notes from the talk are here – Alice, you were an inspiration, I just had to call up mental images of your GDC typing-at-the-speed-of-light – how could I not publish the notes I was already taking? All that training at videogame events has certainly paid off.

But now it’s time to reflect and put together some marginally more coherent thoughts on social media and the lessons of the day.

Lesson One. Social media is people.

We’re finally starting to get it. Social media isn’t about numbers, or spreadsheets, or models, or calculating ROI to the last tenth of a decimal point. It’s about people, and you can’t (always) chain people down in tidy little tickyboxes and assign numbers to them.

We are not numbers.

This causes conflict in organisations that are used to the ‘old’ ways of doing things and don’t really understand the ‘new’. The case for the new was presented again and again and again yesterday. Look. We get it. Social media  matters. People matter. It’s just difficult convincing higher-ups that it’ll impact the bottom line.

There were a few attempts to get some slightly more detailed answers on this subject. What exactly is the investment, when we talk ROI? Is it the cost of a tool? The cost of an agency? The cost of people? What will make the higher-ups listen? In the case of STA Travel, it was pointing out the properties of existing customers (that the STA relationship stopped once customers had booked a trip) and making a clear, coherent case for engagement to extend that relationship. But this brings me on to…

Lesson Two. Everyone is different.

We’re all human, and so naturally we want easy answers. But there are none. It seems that currently the range of social media monitoring tools (in terms of software offerings) is very much an off-the-shelf jobbie – obviously customisable to some extent within that, but still, off-the-shelf. Indeed, some companies with freemium/SaaS products seem to be encouraging this approach.

But if I learned nothing yesterday, it’s that everyone’s totally different, and that works for one client won’t work at all for another. Enter agencies, and humans (see point 3), and customisation, and tailoring. Hell, the agency behind Skype built a dashboard because nothing out there fit their needs! Weren’t all the SMM providers in the audience cringing at that? Speakers repeatedly said that today’s tools aren’t really that great – but some speakers praised them! What a load of mixed messages.

There is method to this madness, though, and it’s all about the human. People praising the tools probably used them well for their specific needs – people dissin’ them probably found that they were looking for something that the tools didn’t do. One thing seems sure though, the tools should work for the clients, rather than the 37signals-etc approach of ‘fit your thinking into the way the tool does it’.

Lesson 3. Automatic isn’t good enough.

This is obviously something I’m interested in, but it was almost disheartening to hear it repeated so much.

Basically, we need humans. We’ll always need humans. Tools help us cut down the humans’ time involvement, but there seems this fundamental mistrust – sentiment is wrong too much and too often, and even humans disagree 15% of the time (bang in line with the kappas I’ve seen in academia).

So even if there were a brilliant, perfect, 100% reliable sentiment detection system, it would be wrong 15% of the time, and so humans would want to check every message just in case. And if all you want is a ‘temperature’ type analysis, well, free tools already do that, and even allowing for error they’re just about good enough.

Lovely.

Lesson 4. We’re too close to the curve to see what’s around the corner.

The whole social media landscape is changing, and the monitoring stuff is just starting to catch up. Two years ago it was rubbish, nowadays it’s OK, and in two years it’ll be great. But the future’s not about technology, it’s about business intelligence, business process, and getting companies to embrace social media and its feedback loops at every level.

Because this is going to become such a fundamental part of how we do business, major players are already getting in the act. Search engines are integrating realtime search, so ’social’ SEO – building social capital – will become as important as keyword-based SEO. But you can’t just add in ’social keywords’ – that concept simply does not transfer.

As well as that, Google and Twitter could well be (hell, let’s just say it, they are already) developing their own social media monitoring systems. Google Analytics is powerful, but not in a social way – but it could be. Twitter could launch their own monitoring product and charge us for API use, creating an.. interesting, albeit unlikely, situation. Sure, cross-platform will still be a need, but we’ve already seen that that need varies so much even by department within a company!

One of the more interesting concepts to come up yesterday was that of an open source framework for monitoring social media, a plug and play approach that everyone could be using in two years – with a company making money where the hard stuff is, consulting and the human factor. I do wonder if this is perhaps viable, especially adding in outsourced human validation (MTurk) and cross-classification to reduce error.

Anyway, this is certainly all food for thought, and <shameless plug>should give me plenty to talk about at the RealTime ChristmasCrunch, at least!</plug>