Kill the “unread” count

Actually, the title for the post should have been “Why Twitter Works for Me,” but it does have some valid things to say about why it trumps email and RSS readers for me.

1) It has no unread count: Almost every application that has hit us since the advent of email has made a point to remind us constantly how far behind are we lagging on information accumulation, processing and categorization. Everything these days tends to tell you that you suck if you are 1) not overwhelmed by information overload and 2) not trying out the zany fancy ways to kill the overload.

2) Simple is the new complicated: There have been numerous attempts made till date to find a complicated explanation for why Twitter works, while the fact is that it works because it is quite simple. All information is presented in flat structure, in a hassle-free manner that saves you from having to tag/organise, categorize information. In Twitter there are no labels, folders or color coding. You can dive in and swim out of conversations at will and also pick your ideal rate/degree of involvement.

In a weird way, Twitter is exactly what you want it to be. It can be a social network, a meme tracker, time-lapse instant messaging or even email lite, which is why everyone has a hard time trying to define it. It means different things to different people.

3) 140 or bust!: Since there is a soft limit of 140 characters per message, Twitter, by virtue of its form, forces users to condense the matter into concise little capsules. This automatically means that value per message per follower or message is considerably higher than what you get from subscribing to an RSS feed. The form itself ensures filtering of the content, rather than having to rely on social categorization or machine categorization.

4) Single window system: The best thing about Twitter is that it does not enforce the use of any particular software or website to participate in the conversations, or just listen in. You can do all the activities specified in (2) using any of the numerous ways that are available to interact with the framework.

Social networks are bound to fail in the long run

While they seem to be the toast of the online world, it is only a matter of time before the social networking websites start losing their sheen and the crazy valuations they command these days.

The lifecycle of social networking activities on individual websites can be mapped under the following heads:

Insertion: Social networks are very much like nightclubs and if you have ever been a regular clubber, you would know that not all nightclubs are created equally.

The newer and fancier clubs are where most of the cool crowd will always head for, while the less exciting ones go away pretty much unnoticed or end up serving niches that never scales up either in terms of traction or in terms of revenue.

More often than not the case for being at a new club is to be one of the first ones to get in, granting those who manage it a feeling of exclusivity. And at least in the early days insertion is much more important than the stages that follow after it.

You can see the same behavior in the case of social networks. Both Orkut and Facebook have benefited enormously from the same exclusivity as a result of their restricted entry policies in the early days.

Replication: Once an individual joins a social network, he/she wanders about replicating connections they already have in real life. There are exceptions to this behaviour, especially in the dating, younger demographic. Quite a bit of the initial spurt of activity on social networks is just this – replication of your existing social graph.

Discovery: While almost every online social network is similar to the others, they also have their own little unique ways of doing things. For instance, you “scrap” on Orkut, “poke” and “banter” on Facebook. Everyone has their own little unique way of doing things and these days, with the introduction of various platforms, there are also truckloads of applications, alongside new users to discover on the social networking sites.

The determinants

Degree of participation: Each of the above three points have degree of participation numbers attached to it. Growth on social networks slows down primarily when the number of people being inserted (new registrations, invites) decline. Secondarily, growth also slows down when the replication is mostly done with as everyone you know is already on the network by then.

To counter this, social networks may try and induce more participation from users by rewarding more participation (like a more frequently updated social stream). This can end up being a counter-productive approach, with high risk of alienating the less-frequent users.

Degree of fulfillment: All three factors can also be measured in terms of fulfillment a user gets from them. When you join, (the insertion stage) has a high degree of fulfillment attached to it, which declines over time when it is not that cool anymore, it is not that new anymore.

Replication also has high fulfillment in the initial days, getting more people on to the same platform etc. But it comes at a price. After a while, everyone you know is on the same network.

Moreover, everyone being there also deprives you of privacy. With time, you need increasing degrees of effort to maintain your profile. It is not uncommon to see users withdrawing more and more from doing things which is reflected in the public activity streams.

Gradually, everything moves to the inbox and private messages, which is a need that is already excellently served by email.

Discovery also has a high degree of initial fulfillment with users finding their way around the new websites, exploring new applications, features and people. Eventually, users get bored of using the applications and they have already added most of the people they have wanted to discover and add, resulting in falling rates of fulfillment as time progresses.

The Eventual Failure

As demonstrated above, there is little use case for sustained high levels of usage on online social networks. Over time, it is hard to battle inactivity and increasing levels of boredom for existing users.

To offset this churn, and also to prop up their stellar growth numbers, it is imperative that these websites keep adding a steady or an increasing number of new users all the time. But that number is a finite figure, determined by the number of people who use the internet and not all of them are going to sign up with social networks.

Unlike a Digg, Gmail or a news website, the value addition accrued from sustained usage of social networks is comparitively low and the need that it addresses is fairly artificial.

Another major issue of privacy and it is an issue without having to bring something like Beacon into the equation. If you do not fine-grain access control on your social stream, it is hard to figure out who all are getting to see what all parts of your life.

And if you do fine-grain access controls on social streams, it is either too much of work or it ends up being a better deal to use specialized services for it (email for communication, Flickr/Smugmug/Picasa for photo sharing, WordPress.com for blogging and so on).

Lastly, advertising inventory on social networks has till date been a major failure. Google tripped on the expectations it had from the Myspace.com inventory, advertising on Facebook or any other social network has not taken off much and the click through rates have been pretty poor on them. Unlike search or news, users don’t get on social networks to find ancillary information related to their activities. You don’t have to try too hard to imagine why there is not much context to one person poking another. It is, well, just a poke at the end of the day.

Eventually, even nightclubs need to reposition and redefine themselves every couple of years to stay in the game. Unfortunately, that is not an option that online social networks get to have and that is what will kill them

First Impressions: Persai

“Blogging Persai” is the title of the blog run by the Persai guys. If you needed an indication of how this post is going to proceed, a major hint would be that I was sorely tempted to give the title “Flogging Persai” to it. For a bunch of guys who have been extremely trigger happy during their Uncov.com days to stamp almost everything with the dreaded “FAIL,” it is rather interesting that their own product is nothing short of a half-baked proof of concept that has been cobbled together for reasons that don’t go beyond, well, the fact that it can be done.

Persai, according to the founders, is an ad-supported content recommendation system. Over time, the guys have crawled a truckload of RSS feeds(there used to be a blog entry which said as much, but is not there on the bog anymore, but Sam Ruby has the list here), indexed and classified them and this in turn powers the recommendation system. You can subscribe to “interests” (known as keywords for the rest of humanity) and get sources thrown at you which the system thinks are relevant to you. While you can’t do much else with the sources, since Persai does not have a built-in feed reader, you can reject sources. And that is all there is to see about Persai. Well, at least for now.

The problems

Use Case: Recommendation systems have not traditionally fared too well on the internet. Previous players like Greg Linden’s Findory used to do a lot more than what Persai even does today and have not done too well at all. In fact, Findory, rather sadly, shut shop recently. The only recommendation system (which works in a stealthy manner) is Google News, which works because they don’t blatantly involve you in the recommendation process.

Once you find content on Persai, there is not much to do with it. Fulfillment is a term that is at best very vague on Persai. You can, as they claim, track the topics, but those links lead out the website anyway. Individual interests have RSS feeds that you can subscribe to, but you can already do that with Google News Alerts and other products. I do doubt if anyone is going to use Persai just to have search term driven RSS feeds.

Accuracy: The approach that Persai has taken to classification involves the usage of training data. This approach works well on similar data sets, but the moment you deviate from the similarity, the entropy will be of a magnitude which will send the classifier on a wild goose chase. And as expected, this has an adverse impact on the accuracy of the results. For instance, one of my interests — “mameo” — throws back results at me which has nothing to do with Mameo in the first five results. I could, of course, reject these sources and help improve Persai, but why would anyone do that when there are other avenues that provide me with much more accurate results?

Speed: To do classification, Persai is already using Hadoop’s MapReduce. Mapreduce does an amazing job of distributively processing huge chunks of data (freshly crawled data to be indexed and classified in this case), but it may only help Persai to a certain extent. The reasons for this are simple: If they process interests as unique to each user, it just won’t scale up. There will be numerous threads doing classification for the same interests since they are unique.

And if the interests are not tracked as a unique item per user, it can play havoc with the results with different users rejecting different sources for different reasons. Of course, there are workarounds for it by using a mix of both approaches (classify as non-unique, filter on display by excluding user-specific rejection criteria), but in the end it ends up being a hack.

In any case, the approach results in tremendously outdated results. Some of the interests have really old articles on top. This could also be due to the fact that the sources are manually added into the system, which means that the quality and spread of the sources will be dependent on the bias of the person who is selecting them. Moreover, it another issue that sites without RSS feeds will not be able get into Persai.

Splogs: Possibly the group that will be over the moon about Persai would be the thugs who run splogs. With Persai it becomes ridiculously easy to set up automated blogs based on topics and, honestly, I see more people using Persai for this than anything else.Considering that Persai is still in beta, I would not give it the “FAIL” rating, but I would certainly give it the “FRAIL” rating. I hope it becomes a much better by the time it comes out of private beta.

Break, unintended

The blog has been suffering from semi-neglect due to the usual refrains: work and life. While I will be pushing out a few drafts that have been in the deep freeze for a while, it will take a while longer to organise things so that I can start blogging again regularly.