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”).



  1. I have to say, I love the idea.

    I wish that a Google Reader / Analytics combo could tell me, for example, what posts are most popular on the Contrast blog, and what does the current audience not like as much. Obviously I’d like it to go further, e.g. “Posts like this attract/appeal to a programming audience” etc.

    Currently my feedback work flow consists of throwing an eye over re-tweets, inbound links (which Google Analytics is too slow to catch, so we use Mint), backtweets, comments, trackbacks, etc.

    It’s a mess, but it’s something we have to do in my opinion, as for a lot of companies creating an audience is very important.


  2. There is also a UX issue here, which is the act of rendering the data itself, inline, can add a lot of *visual* noise to the page.

    When I was at Viacom, I changed the headlines on to include views, ratings and comments on top of every headline. The Design Director pushed back, because, in his view, it made the page very noisy.

    Since I have left there, they have reduced the information to views, and moved it below the headline. These aren’t even personal views, but material in the aggregate.

    I would add that the meta-problem can be articulated as “how do we render social gestures interestingness in a compelling fashion.”

    I would also suggest that you include shares (number of times a piece of information is re-tweeted or emailed) into the formula.

    Kind regards, Ty

  3. @Des Interesting to see the business use case around Personal Analytics. I have always thought of it from a consumer perspective but it is equally applicable to business as you point out.

    @Ty I agree with your premise – it’s a debate I’ve had with plenty of people. Ultimately this is about a trade-off:
    How much do you need and value personal analytics over readability of content and information density?

    Personally I believe that we can create very dense yet readable content. If people argue that things are “very noisy”, I would question what they mean by that as in my experience it often comes down to subjective measures of what is visually attractive.

    All of this comes back to what metrics are most important to the business. Perhaps it is subjective measures of beauty, but I’d hope that it is level of engagement with the content.

  4. I had a similar situation when recently designing a social network around cancer survivors and caregiver networks.

    I can’t go into the real details of how I solved it as the network isnt online yet, but I will give some of the key aspects of it.

    I removed the entire ‘negative’ aspect, because it is needless to the general population in my eyes when talking about someone else’s personal views about their own situation. But, on the same token, it can be detracted by content quality or the like. I’m not sure that model works for all items, but in this case it was particularly true.

    The metrics were quite simple… things were either “inspiring” “shared …” “voted up” “read full page”.

    By being creative with the algorithm I got some very interesting results from our ongoing beta users. Instead of anything having a negative impact, everything had some sort of positive impact of varying degrees. The varying degrees part is really the key to it all in my eyes. Its easy to just remove the ‘vote down’, but it requires more thought about putting different values based each action.

    Now lets take the rabbit hole a bit further.

    What if the rating system was actually based on each individual commenter. That their habits were tracked and appropriately the scoring system was altered on the fly. If I hit ‘inspiring’ on every tweet I see, it should mean less than someone else who is inspired once a week.

    To sum up I think that we … on the design and HCI side…. need to put our thinking caps on to come up with rich interactions and intelligent environments instead of trying to get users to do that for us.

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