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 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.
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.