Early on in the NoTube project we asked how we address the challenge that people want personalisation without any compromise to their privacy. We posed this question in the context of the NoTube Beancounter, a service for aggregating and analysing activity data around programme watching from various sources around the Web as an input to recommender services. I wondered how willing people might be to share this kind of data, and it seems to me now, fifteen months later, that our initial hunch that TV-related activity data is quite private for most people was probably correct.
Despite the fact that, in theory, more data about you means better personalisation (both for you and for the people you are connected to) because recommendations can be based on what your friends are watching, Beancounter-like data aggregation services don’t seem to be taking off in a big way in relation to Social TV. [However, Dale Lane has been doing some very interesting work sharing and visualising his television-watching habits, which is very similar to our initial ideas for the NoTube Beancounter.]
The findings of a user survey released last month by Sidereel.com (a service that “helps users find, track and watch shows online”) support the notion that, when it comes to TV, the promise of better personalisation in return for a potential compromise to one’s privacy is not enough of an incentive for most people. The survey found that whilst 25% of participants want to see their friends’ TV watching history, only 10% are actually prepared to share their own data:
However, this doesn’t mean that people don’t want to talk about TV online, and we are observing that people are continuing to choose to discuss specific programmes they’re watching using existing tools such as Facebook and Twitter. In fact, a high proportion of the conversations in social media are around what people are watching. For example: a YouGov/Deloitte report published in August 2010 found that 42% of those UK adults who use the Internet while watching television do so to discuss or comment on the programmes they are watching at the time. Such explicit, conscious sharing allows you to take part in Social TV whilst avoiding the potential pitfalls of unwittingly disclosing (possibly incorrect) information that you would prefer to keep private.
One of the reasons why people might be put off using automatic activity data aggregation systems might be the fact that designing a good user experience to control the sharing of personal data at a very granular level (e.g. “share this type of data with this group of people, but not that type of data with that group of people”) is just so difficult to do well. For example, witness how users of Facebook are still struggling with its privacy settings. Further, perhaps this is especially the case where people’s basic need is to watch TV; they really don’t want to bother with the mental effort of thinking about all the possible related privacy implications of sharing what they choose to watch by default.
Anecdotal evidence also suggests that in general people do have reservations about automatically sharing their TV watching activities Beancounter-style, at least at this point in time. Therefore, in exploring our use case for integrating the Social Web with TV to help people decide what to watch, rather than generating recommendations based on what your friends have watched, we’ve been thinking about using publicly available Twitter trending information to generate recommendations for live TV instead.
This approach is based on the observation that during prime-time scheduling Twitter trending topics are often TV-related (at least in the UK), and that this Twitter activity can influence what people decide to watch: for example, people reported watching The Eurovision Song Contest on the basis of what was being said about it on Twitter, even though they wouldn’t normally have watched it.
In situations where people are led by the fundamental desire to be part of something that everyone else is watching, choosing to watch a programme based on Twitter trending topics might be the sort of social recommendations that people are looking for, especially since there are no uncomfortable associated risks to anyone’s privacy in this scenario.