SoLoMo: The next big thing to impact journalism (#jcarn)

This is my answer to the question posed in the February Carnival of Journalism, which just happens to be hosted this month by the Digital News Test Kitchen and me. Just because I’m the host doesn’t mean I don’t get to answer my own question, which is:

“What emerging technology or digital trend do you think will have a significant impact on journalism in the year or two ahead? And how do you see it playing out in terms of application by journalists, and impact?”

It’s difficult to choose just one, but I’m going to focus on what some have started to call “SoLoMo,” or Social Local Mobile. (I don’t know that this is universally accepted yet, so perhaps you prefer “LoSoMo” or “MoSoLo.”)

What the heck does that mean?!

Well, smartphone apps (Mo) are getting more sophisticated and capable of gathering and displaying mobile social activity (So) that’s happening around you (Lo).

An example of using “SoLoMo” in reporting, utilizing the smartphone app Banjo, by a Pennsylvania newspaper reporter during a shopping-mall incident. (Click for enlarged image)
SoLoMo Twitter reporting

If I launch Foursquare on my phone, I can see who among my Foursquare contacts, or Twitter and Facebook friends and followers, have used Foursquare to check in someplace. For journalistic purposes, that’s barely useful. But perhaps it will tell me that someone in my social network who is newsworthy is at the same conference as me, or is at a shop close by and I can run over and try to interview him.

I can launch my phone’s Facebook app, tap the Nearby feature, and see where my FB friends are at, if they opt to share that information on Facebook. Again, marginally useful other than for social purposes.

To get a bit more useful, I could launch a phone app called Sonar, which will tell me who is in my vicinity and has publicly checked in using Foursquare, Facebook, or Twitter. Sonar will show me the locations of people in my personal social network (friends, followers), as well as friends of friends, fellow alumni, and “like-minded strangers” (according to Sonar’s FAQ page). Sonar is mostly useful for finding out who else is at a conference, event, or restaurant at the same time that you are. Again, this app is primarily useful for new kinds of social interaction, including meeting with said likeminded strangers when at the same event or venue.

Of course, it will only find people who have “checked in” to a location using a mobile device. That’s increasingly common behavior, but hardly mainstream. Still, the number of people doing mobile check-ins is growing, so apps like Sonar should become more useful in the future.

But Sonar also could be useful for journalists. Let’s say that you as a reporter get a report of a shooting at a restaurant a few miles away. You pull out your phone, launch Sonar, and locate the restaurant, which shows that three people currently are there or were a short time ago. If one of them checked in very recently, you’ll have that person’s name and social-network information and can attempt to contact him or her as a possible eyewitness.

And now for something really useful…

OK, now let’s look at a smartphone app that takes a step beyond what I’ve described above and can be really useful for journalists under certain circumstances: Banjo. This is a “social discovery app” that its developers describe this way: “Banjo celebrates serendipity by connecting people in real time across social networks. Discover what people are sharing at any location.” (If you’re a journalist, that should ring a bell in your head!)

Here’s a little “news” caught on the Banjo smartphone app.
Banjo 2.0 app's map view

If you’ve got Banjo on your smartphone, you can launch it and see all the public sharing activity of all the people around you. Choose to filter only your social-network friends, or have Banjo show youeveryone (i.e., not part of your personal social-media sphere) who has in recent hours: checked in someplace using a location app like Foursquare of Facebook Check-in; posted a tweet from a mobile phone where the location of the tweet is recorded; posted an update to Facebook via their phone, so that the location is recorded; or posted a geo-located photo to Instagram.

You don’t have to be at the location to see all the social reports being posted at a specific place. Switch Banjo to map view and move it around to find any location — say, the shopping mall in another town where there’s been a report of a bomb exploding. Banjo will show you all the recent tweets and other social-media reports posted from the mall. Especially useful will be tweets from eyewitnesses, if in our scenario the bomb indeed has gone off. As a reporter, you’ll have a real-time stream of Twitter and social-media posts about the bombing from the scene, and you’ll have names and social-media account details so that you can reach out to the eyewitnesses.

A less-serious real-life situation occurred in January in Pennsylvania, and a newspaper reporter used Banjo to crowd-source a shopping-mall bomb threat. He described how he used Banjo to gather information about what was really happening when initial reports of an incident came in: “So when this ‘bomb device’ was reported, I quickly searched the area of the King of Prussia Mall using Banjo and quickly tweeted the users in the area that were shown tweeting, checking-in at or near the mall.”

I’m excited by the potential for Banjo during big news events. For example, at the University of Colorado Boulder, the annual “4/20″ pot smoke-in is usually one of the biggest in the U.S., attracting upward of 10,000 people to a field in the middle of campus. This year, I plan to monitor Banjo when the event starts and watch the tweets and posts from the crowd on Banjo’s map view. Should an incident occur — say, violence between police and the marijuana smokers — I would expect to see tweets show up on Banjo reporting what’s happening, and probably smartphone photos posted via Twitpic or another Twitter photo service.

The big challenge and the great need will be for ways to make sense of and filter this SoLoMo information

It should be a simple matter for me or another journalist to turn such social-media content from the event into a compelling Storify presentation. Also, eyewitness tweeters can be contacted via Twitter direct message (“DM”) or public tweet and asked to confirm information, or perhaps requested to take another photo and send it to an editor.

This is just scratching the surface of what’s possible. Google’s Eric Schmidt at the LeWeb conference in Paris late last year was reported to have said that all the “interesting applications” of the future are going to be a combination of social, local, and mobile. And, “All the best engineering is going into mobile apps.” (Source: Memeburn.)

The big challenge and the great need will be for ways to make sense of and filter this SoLoMo information. For an event like 4/20 at CU-Boulder, Banjo alone should do a nice job of helping reporters crowd-source what’s happening in the huge crowd and identify newsworthy incidents in near real-time.

Of course, mixed in with all the mobile tweets posted during the 4/20 event and from the scene will be lots of irrelevant chatter, so there’s the need for applications that can cull through the mass of tweets and flag the ones that are likely to be relevant to news. Researcher Nick Diakopoulos’ SRSR or “Seriously Rapid Source Review” project aims to do just that. He explains that the system “incorporates a number of advanced aggregations, computations, and cues that we thought would be helpful for journalists to find and assess sources in Twitter around breaking news events. … The SRSR interface allows the user to quickly scan through potential (Twitter) sources and get a feeling for whether they’re more or less credible and if they might make good sources for a story.”

What would be even more useful would be applications that automatically identify apparent news events based on geographic clusters of social-media activity, then alert journalists that something that looks like “news” may be happening. Then the application could filter out the “fluff” and display tweets and other social-media posts that were relevant to the news event (or use something like SRSR).

This should be an exciting and tremendously useful emerging technological trend for journalists in the coming year or two. And hey, if my thoughts are in sync with Google’s Eric Schmidt, then this probably is a pretty good prediction.

The on-demand angle to mobile news crowd-sourcing

Finally, there’s another element related to what I’ve discussed that should be mentioned, though it could stand on its own as an essay and answer to this month’s Carnival of Journalism question, so I’ll just touch on it briefly.

We’ve seen a wave of mobile apps that encourage “the crowd” to become eyewitness reporters and photographers when they experience news events firsthand. They include MeporterRawporter, andTackable, among others. Each provides in a smartphone app the tools to take photos or shoot video clips; write a text description and/or photo caption(s); and share this mobile, geo-located content on social-media sites and/or with news organizations.

While the models for such apps vary, an important feature is the ability for a journalist to request information or images from users of these apps who are in the vicinity of a known news event. In other words, a journalist could send an “assignment” to people who are at or near the site of a news event. To return to our shopping mall bombing scenario, an editor could use an app like Rawporter to alert Rawporter users in the vicinity of the mall that she would like information or photos from the scene. Incentive for an eyewitness to comply with such a request varies with the mobile apps, but some such as Rawporter aim to get eyewitnesses paid for their on-the-scene content by news organizations; Meporter and Tackable alternatively provide social and psychic rewards rather than money.

Such news crowd-sourcing mobile apps are worth watching as they evolve, but for now they may not be as powerful as simply monitoring crowd social-media activity at a news event. For example, if we return to the CU-Boulder 4/20 event, plenty of pot-smoking attendees will be posting text and photos to Twitter, Instagram, et al. Monitoring that flow from the event probably will turn up more crowd-sourced news and potential interview sources than requests to users of Tackable and Rawporter, because the latter requires that people in the crowd use those apps and thus will receive journalists’ requests.

SOURCE: Digital News Test Kitchen