Aug 09

Four types of status update

With regard to audience, I’ve seen people tend to make one of four types of status update on social networks:

Private: I don’t want anyone to be able to see this, just a subset of people.
Examples: photos of the kids, don’t want my boss to see comments on a night out, don’t want people from my past seeing what I’m doing and where I’m going.

Public (persona): I want to tell my network about something because I’m using it to shape my identity and how others perceive me.
Examples: a cool place I visited, a cool activity I did, music I like, a funny joke.

Public (niche): I want people interested in the same niche topic as me to be able to find my content about that topic.
Examples: Twitter communities

Public (diary): Content that people don’t consider much before posting (often a stream of consciousness) and are usually comfortable for anyone to see. This content tends to be low in value to most other people but can be powerful if aggregated and searched across.
Examples: one line business/product review, how they are feeling.

Aug 09

Why Twitter is not 40% “pointless babble”

Pear Analytics just published a whitepaper stating that 40% of tweets are “pointless babble”. This research has been captured by the mainstream media, as well as many blogs.

The main conclusion I took from this research is that I won’t be hiring Pear Analytics anytime soon. The research method is so poor that any conclusions are meaningless. Others have also pointed this out.

Pear Analytics grouped 2000 tweets into 6 pre-defined categories, one of which was “pointless babble”. They defined it as:

These are the “I am eating a sandwich now” tweets.

Without knowing any context behind people’s motivations for publishing content and their perception of their audience, we can never meaningfully group tweets by content. One might tweet about eating a sandwich, and for some followers of the publisher, it may in fact be a highly valuable tweet. Perhaps they previously bantered about eating sandwiches and some followers find this highly amusing, perhaps it’s an inside joke, or perhaps this content helps some followers understand the publisher’s availability for communication.

Also, tweets (and all forms of status updates), are not consumed as one-off independent items. People follow people, people don’t follow content. If we want to understand the types of content in tweets and how they affect their audience, we need to study the flow from tweet to tweet, the sequences of content over time.

Aug 09

Last.fm, intrusive advertising, and good feedback

I love last.fm. Normally it looks like this:

Sometimes, they brand whole pages with big music events like Lollapalooza:

I’m OK with this. It’s very much in the background, I can engage with it if I want, or I can ignore it. It doesn’t take away from my core experience at last.fm – listening to music and checking out new bands.

Recently, when I have ‘loved’ a track, the page has also been taken over by a brand:

I’m not OK with this. This is shouting at me, trying to take over, trying to get my attention. It is taking my attention away from listening and loving music. I just clicked a little heart, I didn’t ask for a huge banner ad to take over my experience. It also isn’t genuine. I don’t trust it. I’m pretty sure that AT&T don’t want me to ‘spread the love’ and ‘learn how to tell the world’ by spreading this music all over the internet. Worst of all for last.fm and for myself, I’ve stopped loving tracks to avoid this ad.

This is bad feedback. It’s rude, it’s in my face. It’s like the shouty sports coach yelling encouragement at his team of 10 year old kids.

Good feedback is subtle. It’s encouraging, but in a soft way. Like your grandmother coaxing you to eat more vegetables.

I wish last.fm was more like my Granny.

Aug 09

Personal Analytics and the social web ‘signal vs. noise’ problem

Last year, my colleague Karen Groenink and I were doing some work around social software and put together a theory we called ‘Personal Analytics’.

The problem we had observed was that in the most popular social software sites, there were very few feedback loops for the people publishing content. On top of that, these sites were encouraging more publishing of content, and more ‘friend’ additions. More is not necessarily better. We were seeing that people were creating more and more content, and sharing it with more and more people. Often this sharing was not explicitly to a set of individuals, but was ambient – for example content shared to 500 people because the publisher happened to have 500 friends on the social network they used.

We also observed that this content can have vastly different value to different people, and in fact, much of this shared content had little value to anyone but the publisher. The problem is that the content that is high in value to people gets lost in the noise. By providing Personal Analytics, we wanted to help people publish better content in the first place. It aimed to give people feedback on what others find valuable, enabling them to filter what they publish in future. We wanted people to think about their audience before hitting the ‘post’ button.

The goals behind this theory were:
- To help users share the content that their friends will value the most.
- To do for personal communication what Google Analytics did for websites.

We believed that people will refine their personal image if there is a feedback loop showing that other
people are consuming their refinements. This behaviour is evident in places where people can personalise how they look to others e.g. MySpace profiles. We also believed that showing audience is one motivating factor, showing how that audience valued your content is a much stronger motivating factor.

There is certainly a downside to providing Personal Analytics. People may not value your content, and the negative feedback may be so demoralising, that people stop publishing content altogether. Clearly from a business perspective, there is a huge disincentive to social network sites to provide Personal Analytics. Currently, Facebook and FriendFeed get around this by only providing positive feedback. But I believe that there is an opportunity in also showing the negative feedback. Maybe it is public for all to see, or maybe it is private for the publisher only via a dashboard (similar to taking a colleague aside discreetly and telling them that they have bad breath, or by taking a friend aside and telling them “he’s just not that into you”).