Now Talk to Twitter Through Personalized and Responsive Information Interfaces

The Twitter based APP released by TrialX delves into a potentially huge but relatively unexplored aspect of Twitter as a medium of communication. That is, the potential of Twitter to be a medium to enable users to not just "send updates" but a way to "push" tailored information to them based on their tweets.

The TrialX/Twitter APP demonstrates that one can "Talk to Twitter" or in more general terms one can visualize it as a potentially powerful 'Q and A' medium. From a data and information availability perspective, the TrialX/Twitter APP demonstrates that one can use Twitter as an interface to information locked in databases of thousands of sites or (excitingly), Twitter could be extended to become a platform for 'Personalized and Responsive' Information Agents(PRIA).

Lets see how? The TrialX APP allows you to find out which clinical trials are running in your state personalized to your health condition (e.g., diabetes), gender and age by simply sending a QuTweet (a query tweet, pronounced as cute-tweet) to TrialX. So if you send this QuTweet to TrialX , '@trialx CT i am looking for diabetes trials for 45 yr old male" ,you will get an instant twitter reply/response providing you a link to the matching trial results on TrialX (see more examples and usage details).

Some interesting and innovative aspects of the APP are

1. It inverts the commonly used communication paradigm on Twitter;instead of updating, one is now asking and "getting" information back

2. The Information provided is personalized based on user input. This is different to say, subscribing to a Twitter user (channel) for say latest information about clinical trials or a general health information channel. In the latter case one receives updates that are generic. The TrialX APP only sends personalized responses

3. The information is ON-DEMAND (with a minutes delay on average) and makes it appear that Twitter is "responding" to the users information need

4. The information provided is automatically generated and not a response from another human user. This makes it very interesting as one can envision many such automated information agents

5. Importantly, the information comes from a third-party database and not from the Twitterverse. This makes it at-least theoretically possible to envision twitter being a search Interface to millions of third-party databases (would this be a better way to make the deep web searchable, who knows?)

Apps based on the above characteristics of the TrialX APP can be envisioned (sure there may be already some existing. I found one APP for finding book prices based on tweeting the ISBN number). Here are some examples listed as illustration of the idea:

1. Send a QuTweet to a physician directory service to find a local specialist, 'Looking for a top-rated orthopedician in zip 10004'. Or send a QuTweet to a Hospitals Twitter Channel to find the hospital's specialist, 'Tell me your cardiologists'.

2. Send a QuTweet to a food site's twitter channel to find recipes. For example, QuTweet this to '@ifoodtv chicken recipe, indian style' could return you a link to a page containing chicken recipes, indian style, from ifood.tv.

3. Search flight tickets by QuTweeting to say Kayak, thus, 'need ticket for round-trip from new york to LA between 03/23/09-03/30/09'. This would return a link to the search results on kayak

4. Obtain matching profiles from online dating sites like Match.com by QuTweeting, "Looking for females between 27-35yr old in NY"

Twitter's updates being less than 140 characters, actually works as a great advantage here because parsing these natural queries for a specific domain/site is not that hard. With some patterns (and good user examples), its easy to pick up most of the queries.

Such APPs could work by responding to users in two modes. One, by instantly responding to individual QuTweets sent by users, such as the TrialX/Twitter APP. Two, they could push personalized information resources on a daily basis, based on scanning all the tweets of a user during the day.

The above APPs could also become a monetizing mechanism for Twitter. Since each QuTweet provides a link to the third party's site with the search results, Twitter could charge a "referral fee" for each transaction or charge on a cost per 1000 referrals. Since the users intention is clear, these referrals would likely be potential customers. For a third-party website, this could potentially be better than Google AdWords for several reasons (will list them later) other than the fact that this interface provides better search results based on a dynamic database query and not based on pages being indexed and linked back from Google