While such reminiscences undoubtedly wrench a wistful sigh from the breast of those who lived and worked in those heady days (like, before 2002), educating young would-be journalists about how early adopters and the tech-minded are consuming and helping distribute news is a necessary step to ensure the evolution rather than the extinction of American news services. Northwestern University has taken productive steps in that direction this spring and is set to present five interesting, student-created news apps this week.

The students have focused on easing creation and consumption of news while reducing costs of news production and enabling journalistic standards of research and factuality.

The body of work from this experiment includes sports story generator (Machine Generated Sports Stories, or MGSS) that writes sports coverage all by itself from box scores and play-by-play; a Microsoft Word plug-in (Easy Writer) that allows journos to research and fact-check stories as they write them without having to use a separate search engine; an iPhone app (News Feed) that provides the daily news in five- 10- and 20-minute chunks for news-hungry readers with limited time to read; and two Twitter apps.

Twitter News Service sends pertinent news links to users based on their posts. Either the tool will run in the background of Twitter or from a designated Twitter account that users choose to follow (or un-follow) as they desire.

Tweedia will combine news stories with relevant personal opinion and information on a given topic. By integrating Tweedia into a news site, readers get instant access to relevant Twitter posts. News outlets can place a Tweedia link at the end of stories that will either open a widget on the page or redirect readers to the Tweedia site.

Last year, Medill students built News Mixer, a site that mashed up local news with Facebook, allowing users to comment as they read even though many old-school news organizations still don't allow for comments.

Now all Northwestern needs to do is throw in the business school kids and a couple hundred thousand dollars; Startup Semester, anyone?

How It Works

Social|median centers around the idea of "news networks." Anyone can create or join a network on the site, which acts as a sponge, soaking up news that matches predefined topics from specified sources and delivering them back out to the network members. Once a network is created, users begin to add topics and sources it wants the network to watch out for. Social|median searches the web for news sites and blogs it things match the topics defined by users and suggests them as source additions. Network members have complete control over which sources social|median keeps tabs on and which topics it should match stories to, and can vote sources and topics up or down to help train the algorithm to suit the network's tastes.

Because anyone can create a network, they can literally be about anything, from very broad topics like "politics" to very specific ones, such as "lymphoma research."

In addition to auto-fed stories, users have the option of submitting stories to the site via a bookmarklet. The site automatically parses the stories to the users' correct networks based on topic keywords it finds in the story. Goldberg noted that "relevant news submitted by users" is generally a top rated source in most news networks on the site.

Social|median has two ways of displaying news: recent -- which is based on time, relevance, and source weighting, and popular -- which takes into account user interaction, such as commenting, voting, and emailing of stories. We noticed that often stories would stay popular for days at a time, but Goldberg tells us that is a function of not enough people using the site (just over 1000 at press time), rather than necessarily a faulty algorithm.

Conclusion

"My inspiration for this service came when I was on the treadmill at the gym one day and observing that all 50 people there were watching the same 30 minutes package of CNN Headline News," Goldberg told us, "while all 50 of us have very different interests -- it hit me that at some point in the future we should be able to get more of just the news relevant to our unique interests -- so I started to think about how we might step our way towards that."

Information overload is something we've written a lot about on this blog. Social|median's collaborative filtering approach to cutting through the noise is an old one (see: Digg, del.icio.us, etc.) but the concept of news networks and stories fed automatically based on topical relevance (as determined by a machine) is an interesting twist. Clearly, social|median is an alpha service -- it has many rough edges -- but Goldberg and company are iterating quickly and we're keen to see what they come up with in the next few months. This is one to keep an eye on.

1000 ReadWriteWeb readers can gain access to the invite only alpha by going to the sign up page and using "RWW" (without the quotes) as the invite code. Invites are on a first come, first serve basis.