Posted by jeremyliew in attention, facebook, myspace, social media, social networks, structure.
Two interesting posts recently address the issue of the number and strength of online relationships within social networks. Andrew Chen notes that friendships are complex:
…friendship networks are actually very complex, and are poorly approximated by the “friends” versus “not friends” paradigm, or even the “friends”, “top friends”, and then “not friends” paradigm…
… in fact, once you have this social map drawn out, one of the most interesting questions you can ask people is how they figure out in what situations they should:
* call someone
* text someone
* e-mail someone
* poke them
* write on their wall
* write them a message
* meet them in person
* etc
…there’s a steady progression of “commitment” that it takes to go from writing on a wall (the least burdensome thing) versus meeting them in person (the most burdensome thing). In fact, one of the really useful things that social networks provide that e-mail doesn’t is a range of expressiveness in your communication such that you can use it for more things than sending notes or data across the wire.
danah boyd reaches related conclusions as she thinks about the value of inefficiency in communication.
Social technologies that make things more efficient reduce the cost of action. Yet, that cost is often an important signal. We want communication to cost something because that cost signals that we value the other person, that we value them enough to spare our time and attention. Cost does not have to be about money. One of the things that I’ve found to be consistently true with teens of rich and powerful parents is that they’d give up many of the material goods in their world to actually get some time and attention from their overly scheduled parents. Time and attention are rare commodities in modern life. Spending time with someone is a valuable signal that you care.
When I talk with teens about MySpace bulletins versus comments, they consistently tell me that they value comments more than bulletins. Why? Because “it takes effort” to write a comment. Bulletins are seen as too easy and it’s not surprising that teens have employed this medium to beg their friends to spend time and write a comment on their page.
Andrew found that sending or accepting a “friend” request was one of the least effort ways of communicating online (especially now that sending friend requests has largely been automated via email import tools). This leads to “friend” lists quickly growing to a size well over 150, Dunbar’s number, the theoretical maximum number of individuals with whom a set of people can maintain a social relationship. So interestingly enough, marking someone as a “friend” in a social network is not a terribly good test of whether or not they are actually a friend.
Mining implicit data on behavior to create this structure is actually a better indicator of the real strength of relationships. Xobni does this via email; do any readers know of any third party systems that do this for social networks?
Posted by jeremyliew in attention, data, semantic web, structure, user generated content.
A little while ago I started a series about the structured web where I claimed that Meaning = Data + Structure. I followed up with a couple of posts on ways that structure can be added to user generated content, through user generated structure, and through inferring structure from domain knowledge. The third way that structure can be inferred is from user behavior, otherwise known as attention. As Wikipedia notes:
Attention economics is an approach to the management of information that treats human attention as a scarce commodity, and applies economic theory to solve various information management problems.
Alex Iskold has a good overview of the attention economy elsewhere at ReadWriteWeb.
By watching user behavior, by inferring intent and importance from the gestures and detritus of actions taken for other purposes, you can sometimes also infer structure about unstructured data. Google does this with its PageRank algorithm, Del.icio.us uses individual bookmarking to build a structured directory to the web, and Xobni maps social networks through analysis of your emailing patterns. Behavioral targeted advertising is based on the assumption that users display their interests through the websites they visit.
Using implicit data to infer structure requires making some assumptions about what each behavior means, but it can be a useful supplement to the other two methods of inferring data. As with inferring structure from domain knowledge, it requires a well defined ontology so that people and things can be mapped against it
Would love to hear more examples of using attention data to infer structure.
Posted by jeremyliew in attention, Consumer internet, social media, social networks, user generated content, web 2.0.
In May I posted about a NY Times article that showed that making popularity data public made hits bigger and that talent was only one factor in this equation – the taste of the early adopters was more significant.
A recent Wharton research paper comes to a similar conclusion. Paid Content summarizes the results:
— “One, some common recommenders lead to a net reduction in average sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and ratings, they cannot recommend products with limited historical data, even if they would be rated favorably. In turn, these recommenders can create a rich-get-richer effect for popular products and vice-versa for unpopular ones. This finding is often surprising to consumers who express that recommendations have helped them discover new products.
— In line with this, result two shows it is possible for individual-level diversity to increase but aggregate diversity to decrease; recommenders can push each person to new products, but they often push us toward the same new products.
— Result three finds that recommenders intensify the effects of chance events on market outcomes. At the product level, recommenders can ‘create hits’ out of products with early, high sales due to chance alone. At the market level, in individual sample paths it is possible to observe more diversity, even though on average diversity often decreases.
— Four, we show how basic design choices affect the outcome. Thus, managers can choose recommender designs that are more consistent with their sales or product assortment strategies.”
These are largely consistent with my conclusions from May for people who run social media sites:
1. If you’re trying to iterate towards a “best answer” then keep feedback loops to a minimum, at least before users “vote” on their own. (e.g. Hotornot, espgame)
2. If you’re trying to create “hits” out of some of your content (and don’t care if it’s the “most worthy” content – you only care that they are hits), then display feedback and popularity constantly, as this will effect user behavior and exacerbate the size of the hits (e.g. Youtube, Digg, American Idol?
3. If you want to “guide” user behavior in a certain direction, provide feedback that validates or shows the popularity of that behavior. This is consistent with my prior post on game mechanics applied to social media: keeping score.
Posted by jeremyliew in attention, communication, email, metadata.
Since their launch at Techcrunch40, Xobni has been getting a lot of attention.
I don’t love the name (it’s inbox backwards) for reasons I’ve discussed before, but I do like the product. Xobni is an outlook plugin that contextually exposes metadata and links gleaned from within your .pst file. So when you’re highlighting a particular email you’ll see how often you email the sender and how often they email you, the times of day they typically email you, who else is connected to that person (in that they are on the same email threads), what other email threads you have with the sender, what attachments they have sent you, and much more. See below for an example of what comes up when I’m highlighting an email from my friend Dan Carroll:
This is a good example of attention data in action. Some people call it the implicit web.
There are three reasons that this creates a good user experience:
1. It requires no effort from the user (because it uses implicit metadata that is gleaned from watching normal activity)
2. It is presented contextually (so you’re not overwhelmed with data when you don’t need it)
3. It informs decision/actions that the user might take.
This reminds me of a similar approach applied to online dating that I heard about a few years ago (can’t remember the source). Apparently one of the issues with online dating is that men send out tons of email to women and don’t hear back from many of them, while on the flip side, women get inundated with emails and can’t easily sort through the volume. The proposed solution was to make transparent to each woman how many other women each man who contacted them had also contacted. This simple change also fulfills the three criteria listed above, and would likely decrease volume of contacts as well as increase likely response rates.
If you’re interested in the attention economy, Seth Goldstein has been talking about attention data for years. The Defrag Conference in Denver in November will be addressing a lot of these issues as well (I’ll be on a panel there). If you’re interested in attending, use the code “SPEAKER1” to get $100 off your registration
I’d be interested to hear from readers about other good applications of attention data to improve user experience.
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