People like to talk mostly about themselves. While some call it narcissism, others consider it a basic human need. Either way, it’s the rapid acceptance of the ‘timeline’ concept seen in many social networking services that has created a new way for people to connect. The movie The Social Network, which is loosely based on the story of Facebooks’s creator Mark Zuckerberg, shows that he simply craved social acceptance and rather than stepping into the real world, he thought he could bring the real world online, where he felt more comfortable. This gave a chance to people with a different skill set to get noticed and be heard. It was almost as if making billions of dollars was simply a by product to what originally was the plan to change the way people interact with each other.

Twitter’s Strength

So maybe they decided to go for an experiment, that is, to strip off the fat of both models (Facebook and Blogs in general) and come up with their own application: very basic, no confusing user/security settings, and just one objective “What’s on Your Mind?” with the added limitation of “in 140 characters or less”, of course.

This “limitation” turned out to be Twitter’s biggest strength. Other social media applications like Facebook have far more advanced timelines. What they drift away from, in search of user experience, is relevance. A sizable segment of the new social media is all about quick and relevant information traffic. The need for using the word new with social media, which itself is the product of the last five years only, is a testament to its rapid evolution. For an average user, being able to connect with thousands of people with shared interests has created a massive cloud of data that is being thrown at him/her. This data can only be considered as information when it is relevant to the reader. However, in the world of broadcast messaging, most writers are more concerned with what they’d like to share; rather than what each individual reader most of whom they know little to nothing about – would like to read

So the writer gets to broadcast information to up to millions of readers without always engaging with each of them on a personal level. On the other hand, the reader still gets to feel a first hand experience with the celebrity/organisation and may choose to either endorse the provided information, or to communicate with the writer directly.

People will always feel the need to share what they know or feel. While their need to learn and engage with more people keeps growing at an exponential rate, social media applications are finding it increasingly difficult to fulfill user requirements. Modern predictive algorithms are still too primitive to label computers as “intelligent”, so what could be the next step for social media applications?

Trust the Cloud

When using most of the downloadable utilities over the internet, multiple mirrors are discovered and replaced automatically, based on the amount of relevant and non-repetitive data they provide. Can a similar approach be used for social media updates? If the same model was to be used for social media, instead of associating dedicated addresses with the data, we also let it float in the Cloud. This way it would serve as a broadcast, not only for pre-defined destinations, but also for dynamic/on the fly destinations.

Ranking

Ranking may help in two ways: purging less appreciated writers’ feeds out of the reader’s timeline, and creating overall rankings for the writers themselves.

Rating system for blogs and commenting utilities already exist. Incorporating this concept in social media applications may help gradually fade out irrelevant data feeds, rather than having to unsubscribe from these feeds altogether. Doing this can also help in aggregating cloud ratings.

However, once a channel is lost, there is space for more data to be consumed. How can that space be filled automatically? That’s where classification comes in.

Classification

Single click tagging – tagging multiple people in just on click – is another popular concept that can be incorporated directly to each data feed, enabling readers to either choose from existing tags applied to that particular data chunk or creating their own tags. The most popular tags get published first, providing an overall classification based on the consolidated user feedback. This means that before entering the internet cloud, each data chunk may be carrying its own little “tag cloud”. Which again helps classify writers based on the aggregated results of their feeds’ tag clouds.

Putting It All Together

These and similar concepts can help with automatic discovery of new and relevant feeds, while silently pushing out the irrelevant ones. Here is a revelation: it becomes more about the content itself than the writer. It is not always possible to trust user profiles neither is it possible to write algorithms to accurately reflect the sentiments hidden in a string of words. So while we still depend on the readers to specify their preferences, we can’t expect them to fill out a two page form.

And lastly, not to forget our friends and our star crushes whose noodle soups we do care about, feed segregation helps conquer that front as well ( for example, the ‘Lists’ feature in Twitter).

As long as technology promises to keep up with the human need to learn, share and interact, social media applications like Twitter still have a long way to go, with out the nag factor, of course.