I know how to spot a genuine review

So, another flurry of stories about fake reviews on Yelp, TripAdvisor, and elsewhere — this time prompted by a Cornell algorithm that supposedly can, with 90 percent accuracy, spot a bought-and-paid-for fake.

(The New York Times and NPR, among others, covered this.)

As I wrote several years ago, it’s getting ever harder for humans — including me — to identify fakery. It’s nice that an algorithm can improve the odds, at least for now, but let’s not get ahead of ourselves:

  • Even the best algorithm won’t finger a talented faker, and it also won’t identify a talented programmer who is posting fake reviews algorithmically. (I don’t know for a fact that the latter occurs. But since I can imagine a way to do it, I expect someone smarter than me is already making money at it. Story of my life.)
  • Publicity about fake-sniffing algorithms and their methodologies will increase the average “quality” of fake reviews, making them even harder to spot for humans and algorithms alike. We’ve seen this type of arms race before: Algorithms are like antibiotics; they invite the enemy to evolve.

There’s no foolproof way to spot a fake review. However, I do have a foolproof way to spot a genuine review:

Was it written by a friend? If so, it is genuine.

Evolve around that!

Who wrote that review?

Via Andrew Shotland, I recently saw this post.  I know I’ll be called naive, but I was surprised at its blatancy.

This guy Stephen Espinosa (whom I don’t know) helps local businesses promote themselves online.  His advice is to get your “clients” to post reviews on popular sites — the quote marks are his, and he adds a smiley face in case we don’t get it:

I won’t spell it out fully, since he doesn’t, but this seems like an opportune moment to talk about fake reviews.

You need spend only a few minutes on most rate-and-review sites to understand that they contain fake reviews.  There are fake positive reviews posted by the business owners, and fake negative reviews posted by their competitors.  Many are amateurish and easy to identify if you’re looking for them, though I suspect that some casual users don’t realize they’re fake.

I’ve never put much store in reviews by strangers.  Still, I always thought that out-and-out fakes were a fairly limited and unorganized phenomenon.  Now that I see they might be promoted more systematically, I’ve lost confidence that I can even spot a fake.

Furthermore, I expect that such fakery will spread and become more sophisticated.  As local search reaches critical mass, it’ll be hard to trust anything.

I used to believe, for instance, that a Yelp reviewer with 10+ reviews and some kudos from friends was almost certainly a real person.  That’s probably still a safe assumption — but will it be next year?

If I’m a certain type of SEO consultant, right now I’m probably setting up a network of hundreds of fake Yelpers.  They’ll all have real-looking pictures, real-sounding profiles, and lots of reviews (some even genuine).  They’ll send each other kudos, enhancing each others’ credibility. 

And they’ll exist solely so I can be paid to deploy them for the benefit of my clients.

If done properly, this sort of fakery will be very hard to detect.  Probably the only way I’d get caught would be to advertise the service — or to include quote marks and smiley faces when I blogged about it.

And this is just the truly fake reviews.  There’s still reviews from friends of the business owner, and “real” reviews that have been solicited directly by business owners, some of whom will give discounts in exchange for posting on … well, on a certain site.

In such a world, reviews by strangers become devalued and personal trust is at a premium.

Not so long ago I heard that we need to see, on average, 20 reviews from strangers before we’ll believe the prevalent opinion that’s being expressed.

What will that number be in the future?  50?  100?

Wouldn’t it be simpler and better to get your advice from people you know and trust? 

Via, say, Loladex?

Getting people to use Loladex (Part 1)

Why would anyone start using Loladex?  We get asked this question a lot.

I’ve posted before about the chicken-and-egg issue, albeit in general terms.  Probably I should update those thoughts: Since we started focusing on social networks, we’ve learned a bunch.

But for now, let me address Loladex’s specific challenge: How do we motivate people to rate local businesses via a Facebook application?  Why would anyone do such a thing?

Well, for many reasons, of course.  One day I’ll list them all.  But I’d like to highlight one reason in particular, partly because I think it’s powerful and partly because it illustrates a big difference between Loladex and two of its biggest competitors — Yelp and Angie’s List.

Here it is: Loladex believes people will rate local businesses to help their friends.

By friends, I mostly mean actual, real-world friends.  People you might have dinner with.  For most folks, that’s a subset of “Facebook friends.”

Let’s get specific.  Why would anyone use Loladex to rate, let’s say, a plumber?  Or a pediatric gastroenterologist?  Certainly it’s not something you do on a whim.  Loladex won’t be running ads that say “Rate pediatric gastroenterologists!” — and if we did, we wouldn’t expect many clicks.

But suppose you were asked directly by a friend whose kid needed a medical specialist?  If you knew of a good gastroenterologist, would you take a minute to make the recommendation?  If you were seeking such a specialist, would you value this sort of recommendation?

We think so.  Such recommendations are an everyday part of friendship, and numerous surveys tag them as a more powerful force than the Yellow Pages, a $14 billion industry.

With Loladex, we want to provide a channel for these person-to-person recommendations.

Contrast this to Yelp.  I always say I like Yelp — and I do — but Yelp isn’t about helping your real-world friends.  By and large, the people who rate businesses on Yelp do it for reasons of (a) self-expression; and (b) social standing in an online community that may overlap with their real-world friends, but doesn’t have to.

These mostly twentysomething Yelpers provide a service for us all, God love them.  But it’s almost never a person-to-person transaction.  Also, the motivation to rate something on Yelp fades quickly outside its core realm of restaurants & other social venues.

Or consider Angie’s List.  I’m not a fan of Angie’s List, simply because it’s a subscription service.  If it were free, I’d love it.  They’ve built something that’s clearly valuable to their users — and they’ve focused their brand admirably, defining it around home services.

Again, though, Angie’s List isn’t about helping your real-world friends.  It’s mostly a community of cooperating strangers who share ratings because they understand the value of the site’s virtuous circle.  There’s an implicit quid pro quo.

Both Yelp and Angie’s List have powerful models.  Loladex aims to tap many of the same motivations; we’d be silly not to.  But mainly we’re about recommendations from your friends.  We’re trying to bring this everyday personal interaction into your online world.

OK, so much for the theory.  How’s the “help a friend” strategy working for us, specifically on Facebook?

To be honest, it’s a learning experience.

More in Part 2. 

Local apps on Facebook (Part 3)

In my last post I did quick sketches of 14 Facebook apps with a local-search element.  I’m not considering Loladex at the moment, because we just launched.

OK, time for a reality check.  (You may be depressed by the following.)

The most popular app of the bunch, TripAdvisor’s Local Picks, has fewer than 2,000 “active” users = daily users, more or less.  Per Adonomics, it’s the 859th-ranked application on Facebook right now, lagging behind things like Vibrating Hamster at #673.

Like many Facebook apps, Local Picks rose fast and fell fast.  At one point in December 2007, it clocked more than 100,000 active users, but it’s fallen below 10,000 for most of 2008 — below 5,000 for the past two months.

More worrisome is the fact that, despite its collapse, Local Picks still has more daily users than the other 13 apps on my list combined.  Almost twice as many, in fact.

The second most popular app on my list is Restaurants by Hungry Machine with more than 500 daily users, down from a peak of more than 10,000.

And that application, in turn, has as many users as the remaining 12 apps combined.

In short, local-search usage on Facebook is loooooow right now.  And it’s heavily concentrated in a couple of apps, both of which have cratered in 2008. Based on Adonomics charts, I suspect that only a few other apps have any life in them:

•  DoYa? may be building a bit, perhaps based on its gift-cert giveaway

•  My Restaurants looks to have a small core of regular users that isn’t shrinking

•  iEat seems to be growing somewhat

But obviously, the numbers here are fairly inconsequential.  DoYa? is the biggest of the three, with 215 active users.

So — yikes, right?

Yes and no.

On the one hand, it’s easy to see how Jon Carder of MojoPages concluded that local search isn’t worth doing on Facebook.  (See earlier post.)  On the other hand, I think we can learn some things that’ll help us crack the code:

•  Recognize the potential. Local Picks is a good app, and it grew nicely in November and December to top 100,000 active users. That’s a number worth noting. OK, in December it suddenly crashed. Maybe someone can tell me why?

•  Leverage success. The two biggest apps have more popular “sibling” products on Facebook; none of the others do. Hungry Machine explicitly presents its apps as part of a family, using a toolbar to link between them. TripAdvisor doesn’t do this, but it does some lesser cross-promotion from Cities I’ve Visited. Most of us can’t draft off an earlier app, but there are other ways to apply the lesson.

•  Attract repeat users. Most local apps either never took off, or peaked and then fell off a cliff. But several — Eating and Hangouts, for instance — took off and then went into a slow fade. While a fade isn’t as good as a rise, it’s better than falling off a cliff: These apps didn’t lose people immediately. Interestingly, they share a focus on broadcasting “status”-type messages, which may be a key to keeping users engaged.

•  Keep working the problem. It’s striking how many Facebook apps are abandoned, more or less, once they start losing users. The charts tell the story: No secondary upward blips as new solutions are tried. The users are allowed to melt away. This isn’t limited to local, of course. Building a Facebook app is an experiment rather than a strategy for many developers, and it shows. But given the potential of local — and its importance to some of these players — I’d expect a bit more dedication to figuring out what works.

That’s the end of this little series of posts. Soon I’ll tackle the question of how Loladex can avoid the fate of its Facebook competitors.

Local apps on Facebook (Part 2)

So who’s doing local search apps on Facebook?  What follows is my brief impression of various local apps I’ve used in the past few months. I’m sure my list is incomplete.  If I’ve missed something obvious, feel free to add it in the comments.

In my opinion, no one on this list competes with Loladex’s combination of functionality, comprehensive content and usability.  Oh, and style.  But of course I would think that.

This list is in no particular order.

MojoPages: Based on Jon Carder’s comments (see my earlier post), MojoPages is’t pushing this app anymore.  It’s a bit of a shame, because (a) they put some thought into it, improving it since it launched some months ago; and (b) the MojoPages.com site continues to push some social aspects that are, in my opinion, better suited to Facebook than to their standalone version.  It’s true that the FB app never got any traffic, and the execution still needs work, but I thought it deserved more promotion than they gave it.

Local Reviews: On its splash page, this app by eSesame claims to be “Facebook’s FIRST and LARGEST Local Reviews App!”  Even if this was true once, by some measure, it isn’t anymore.  It seems downright moribund, in fact.  I find the interface a bit confusing and scattered, and the content is limited to a tight selection of categories and cities.  Even in these areas, they don’t have comprehensive data — searching for Chinese restaurants in the Washington, DC area yields a single result, for instance.  I guess they have just what their users enter?

Restaurant Reviews: As the name implies, limited to restaurants.  This app comes from SuperPages.com, which is certainly a credible source, but it never got off the ground.  I’m not sure why they restricted it to a single category since they have the whole phone book at their disposal, literally.  The app is presented mostly as a way of browsing places that have reviews: You’ll need to click to uncover a search box.  It’s mostly a window into user comments from the regular SuperPages site, so there are few associations with actual Facebook users.  Not very social.

Local Picks: From TripAdvisor, the king of hotel reviews.  Limited to restaurants, despite the generic name — base data from the worldwide but long-in-the-tooth ChefMoz database, perhaps, supplemented by members?  (Someone correct me if I’m wrong.)  Users appear to be griping that their additions and deletions aren’t processed quickly enough by TripAdvisor, which is also my experience with the TripAdvisor hotel database.  Still, it must be said that this is a very nice Facebook app: Deep, engaging, well-designed.  They are making an effort, and they have more users than most local apps.  TripAdvisor also makes the ”Cities I’ve Visited” app, which is far more popular but not nearly as useful. 

iVouch: I recall that this app used to be a local thing, where users “vouched” for businesses, but lately it’s about users vouching for each other.  The local aspect still exists, but has been subordinated.  It’s not particularly useful, in any case, as I can’t search for places that others have vouched for.  Or I don’t think I can, anyway.  Strange.  Either the app isn’t fully conceived or I’m missing something.

Search Local: This is a fairly straightforward Yellow Pages app, except that it doesn’t seem to have much data.  (Or maybe its search doesn’t work properly.)  Its main distinction is that it wants to broadcast all your searches to your friends’ news feeds.  This may or may not be good for virality — not, I’d guess, based on its traffic — but either way it’s a bad idea.  No one wants this stuff to be public by default.  They have an opt-out checkbox, but it’s easy to forget and I can’t set it to opt out permanently.

DoYa?  This app has some of the same ideas as Loladex.  They’re trying to jump-start virality by offering $10 Amazon gift cards to people who “share” five recommendations with 10 friends.  Depending on how many of those friends become DoYa users, it might make sense.  I’m not crazy about the interface, but that’s mostly a matter of taste.  They’re on the right track.

Eat It!  Is this app working as designed?  It’s a restaurant-only product by big ol’ CitySearch, and someone spent time on it, but it lacks all of CitySearch’s deep content for each listing — no information about restaurant hours, no professional reviews, no user ratings, no photos, nothing.  Just an address.  Whole features like “QuickRate” appear to be pretty much broken, too.  Weird.  I have a vague memory that it used to work, but until today I hadn’t visited it for months.  Maybe its creators haven’t visited lately, either?

Eating: Yet another restaurant-only app, this one by Menuism.  It doesn’t take a local-search approach, exactly; it’s got a less utilitarian, more social vibe — more like one of the various “bookshelf” applications on Facebook, where people participate to express themselves and share their interests.  Or like Yelp, except with more bona fide social networking.  That’s my take, anyway.  Like Loladex, it incorporates Facebook networks, which is a good way to open up more friend-like content.  Data is far from comprehensive, however, and the sync to the “real” Menuism site seems imperfect.

Restaurants: Bulit by our neighbors in the nation’s capital, Hungry Machine LLC, creators of Visual Bookshelf.  Once again, restaurants only.  Once again, data is far from comprehensive.  Annoying popups that ask me to invite friends.  I don’t have many friends on the app, so it’s not clear to me whether their recommendations count more than those of strangers.  Be nice if they did.  (Anyone know?)  Generic food photos are used to illustrate specific restaurants, which can be misleading: Our local chain Ledo Pizza, for instance, is known for its square pizza; they probably aren’t thrilled to be represented by a round pie.  I’m not a fan of the overall design, which is busy, but the underlying functionality seems solid.

Following are some other apps that I haven’t used much yet, but that may be worth a look. None are “big” by Facebook standards.

[Added after initial post] Hangouts: OK, so this is Yelp’s application.  It’s not a local search, just a way of broadcasting what you’re supposedly doing this evening, with a search that sometimes helps you link to the right listing.  It launched fairly soon after the Facebook platform launch, and has the air of a side project rather than a committed effort.  Functionality is shallow and requires going to Yelp.com for detail on listings.  I could be wrong, but it seems to have been abandoned by Yelp.  I assume that if they ever return to Facebook it’ll be with a different approach.

My Restaurants: A modest app that seems like it’s doing many things right. 

Eat-a-Rama: Bills itself as the “top-rated restaurant application on Facebook,” whatever that means.  At a glance I’m not a fan: Overdesigned & gimmicky.  Someone is working hard, though.

iEat: This app doesn’t really appeal to me, either, but it’s a bit interesting because it has a two-level search. If it doesn’t find a restaurant in its database (a likely scenario, it seems), it does an “Internet search” and returns matches from … well, somewhere or other.   I enjoyed the stock photos shown for each restaurant; see the one for Domino’s below:

Search result from iEat

While I’m at it, I should mention a few notable absences:

Yelp: As far as I know, Yelp has no app on Facebook.  A few third-party apps try to track or broadcast your Yelp.com activity.  See above; thanks, Jon.

Zagat: Zagat has a Facebook app, but it’s for use by restaurants rather than regular folks — the virtual equivalent of putting a “Zagat rated” sticker in your restaurant’s window.

Angie’s List: Not terribly surprising that AL doesn’t have an app; as a subscription site, it doesn’t really translate.

(Continued in Part 3)

Local apps on Facebook (Part 1)

Responding to our TechCrunch coverage last week, Jon Carder of our competitor MojoPages said the following:

Local search just isn’t gamey or sexy enough to make a local search Facebook App worth the time.

I disagree with this analysis on several levels.  It’s true, so far, that many popular Facebook apps have a game-like aspect.  And it’s true that their success can teach us some lessons.

But such apps are just the baby steps of social networking; the meaningful stuff lies ahead, when we’ve learned to walk.  And run.  And climb.  And that’s definitely “worth the time.”

Indeed, I think it’ll make Facebook as a whole “worth the time.”

Social networking is already more than games.  It allows our online actions to be informed and enhanced by the participation of our friends.  The Web has become more like the real world.

In the case of local search, it means we can finally get advice from our friends — something we’ve always done offline, but that’s new on the Web.

Say you’re looking for a good Italian restaurant.  Offline, you can consult the Yellow Pages.  Online, you can do the same.  You’ll find a restaurant, but not necessarily a good one.

Offline, you can see what’s recommended by the local newspaper or magazine.  Online, you can do the same.  They review a very limited number of places, however.  (And beyond restaurants, virtually nothing.)

Offline, you can consult Zagat.  Online, you can do the same.  Or check Yelp.  You’ll get the wisdom of a certain crowd.

Offline, you can seek recommendations from your friends.  According to every poll, these are the recommendations you’ll actually trust — the advice you’ll actually take.

Yet until recently, it’s been pretty hard to do the same thing online.  Facebook has made it easier.  And increasingly, Facebook is where your friends are.

To me, that means Facebook is a great fit for local search.

Of course, we’re still figuring out how local search should work in a social environment.  Jon observes:

The existing apps [on Facebook] are all fairly good, well designed and user friendly yet none of them is gaining any sustained traction.

I don’t agree with the “fairly good, well designed and user friendly” part, but it’s absolutely true that no one has gotten traction.

Why?  I decided to survey the current apps to see if I can draw any conclusions.

(Continued in Part 2)

Local is weird

Not long ago I was talking with Gib Olander from Localeze, our main data supplier.  The topic was local data, and how weird it can be: Some things look like they must be mistakes—except they’re not.

Gib’s example was a place that sells custom rims and also cellphone service.  If you saw the business listed under both categories, you might figure one was wrong. But Gib can show you a photo of Cell ‘n’ Wheels that proves otherwise.

I laughed. The example wasn’t so terribly outrageous, and Localeze certainly has an interest in promoting this idea.  :)  Yet at the same time, I happened to know of a much better illustration.

I get my hair cut at a barber’s shop that also sells seafood.   Oysters, specifically.  During the holiday season it does a roaring trade in hams, too; they’re piled into a shopping cart by the door.  No one seems to mind buying dinner from a place that can get ankle-deep in human hair.

Whatever about rims and phones, I’d definitely suspect an error if my search for [oysters] near [Leesburg, VA] returned Plaza & Tuffy’s Barber Shop as the top result.  But it’s the only place in Leesburg that advertises oysters on a sign:

 Fresh oysters at Tuffy’s

I shot this photo right before getting a haircut.  When I went inside, my barber Bobby asked why I had been taking pictures.  I told him I had a friend in Chicago who didn’t believe that a barber shop also sold seafood.

“We’re still country here,” he replied. “You tell him that.”

It got me to thinking: In reviewing YellowBot last year, I included a screengrab of something I portrayed as an error—YellowBot’s tags said that a hair-removal place here in Leesburg also sells bail bonds.

Perhaps I was too hasty in calling it a mistake?

Structured vs. unstructured

Yellow Pages folks surely do love structure — especially when it comes to data. Here at the latest Kelsey conference, where YP folks abound, the only good datum is a structured datum.

Consider the title of yesterday’s most interesting panel:

Building a Better Database: Acquiring Content in a Dysfunctional Environment

The title is a bit grad school, but “dysfunctional” is a strong word that caught my eye. Here it mostly means “resistant to structure.”

And them’s fightin’ words in the world of Yellow Pages.

By now I’ve gone to a bunch of YP-oriented conferences. All of them featured a discussion about how to gather structured data. But I’m starting to suspect that this isn’t the most important problem to solve — and not just because these conference discussions never go anywhere.

Here’s my thinking:

In what a YPer would call a functional environment, every business location, small or large, would authorize a regularly updated master version of its “attributes” (hours, certifications, parking facilities, etc.), and would post this information in some microformat on its Web site, or supply it directly to each data vendor, or send it to an industry-wide data clearinghouse that’ll probably never exist.

In addition, lots of other data sources — licensing bodies, rating sites, whatever — would distribute structured information that’s already normalized and can be correlated perfectly to these master records.

All this data would then be collated by data vendors such as Localeze and sold to Web companies such as Google or, for that matter, Loladex.

Finally, the Web companies would build applications that use the structured data for searching by consumers (input) and display to consumers (output).

This worldview may be summarized thus:

More structured data in → Better answers out.

Or as Marchex‘s Matthew Berk (who’s a smart guy) said at the panel here: “We think local search is about structured search.”

Berk gave a very good example, which I also use when discussing Loladex: If you’re looking for a doctor, you need to know whether he takes your insurance. That’s true, without a doubt.

But here’s the problem I have:

The majority of information available about any company, and particularly about any small company, will never be structured. It’ll exist only on the general Web, where it must be searched on its own terms — that is, as unstructured text.

To me, this suggests that the most pressing data problem isn’t how to gather more structured data, but how to search unstructured data (on Web pages) and return structured answers.

I live on both sides of this equation, by the way. My wife runs a small cookie bakery, and I’m in charge of distributing her data to online sources.

Because of my background, I’m more informed and motivated than most small business owners. And yet, to be honest, just keeping her Web site up-to-date is a chore. On Yelp right now, I’m sorry to say, her hours are incorrect. I should update it, but I just haven’t.

Accuracy on our own Web site is always my #1 priority, because that’s our official voice. Also it’s where most people land when they search for “Lola Cookies.”

Keeping Yahoo Local accurate is on my list, too, but it’s lower down. Ditto Google and YellowPages.com and the other big sites.

I never think about the data vendors one layer back, like InfoUSA, unless they happen to call the store. (Which InfoUSA does, to its credit.)

Meanwhile, plenty of interesting and searchable information about the bakery exists in other places on the Web, in formats that aren’t even addressed by the concept of “attributes.”

A TV broadcast from the bakery aired live on the local morning news recently, for instance. If you watched the show, you might search for us with a term like “fox 5 cookies virginia.” Where does that fit in the world of structured data?

I raised this general issue at yesterday’s panel. What were the panelists doing about this wealth of unstructured Web data, which right now is the dark matter of the local-search universe?

The answer I got was, basically, “Not much.”

Most panelists said they do only highly targeted crawls, focusing on sites that have structured data that can “extend or validate” their own data, in the words of Localeze’s Jeff Beard. An example might be the site of a professional group such as the American Optometric Association.

No panelist was ready to start indexing the sites of individual businesses, or locally focused blogs, or any other sites that are unstructured but potentially rich in content.

The only (mild) exception was Erron Silverstein of YellowBot, who also said his company limits itself to targeted crawls — but included local media, such as newspapers, among his targets.

A few players are indexing the broader Web and then associating pages with specific businesses (which is the important part). Most notable are Google and Yahoo, who do it for their local search products.

Of course, they’re already indexing the entire Web. It’s less of a stretch for them.

Google and Yahoo also buy structured data from InfoUSA, Localeze and others, so it’s not like such data is obsolete. But they’re getting the same info directly from some businesses, and those updates are likely more timely, more accurate, and more complete.

Meanwhile, their Web indices are opening up a realm of data that traditional vendors like Acxiom — represented by Jon Cohn on yesterday’s panel — simply don’t care to address.

I suspect that, sooner than you’d imagine, Google and Yahoo will be buying structured data not so that users can search it directly, but for two less-flattering reasons:

  1. To help find Web pages they can associate with each business
  2. To fill ever-smaller gaps in the coverage that results from #1

Matthew Berk of Marchex argued that a good local search must be structured to “help someone walk down the decision trail” by using filters to narrow their search progressively:

I need a orthopedist in Boston … in the Back Bay … who accepts United Healthcare.

I think users are more likely to learn that they can go to Google and type “orthopedist back bay united healthcare” — particularly if it produces a good top result the first time they try.

The burden of local search, it seems to me, is to do something that Google can’t match with an unstructured Web search.

In any case, the search portals will ultimately use their indexed Web pages to extract and cross-check structured data directly. Over time — probably just a couple of years — such automated processes will yield data that’s more current and detailed than anything that’s produced by scanning phone books or calling stores.

The resulting search functionality, integrating both structured and unstructured data, will be sold to other companies as a Web service, and data vendors such as InfoUSA will become irrelevant to local search.

Now that would be a dysfunctional environment for many of the Kelsey attendees.

I’m not sure exactly how companies like InfoUSA and Acxiom should tackle the unstructured Web. It’ll demand a new way of thinking, and probably a new way of selling.

But I’m certain that they ignore unstructured data at their peril.

Greg Sterling sums it up

Analyst/consultant Greg Sterling obviously had some time to think Big Thoughts while recovering from a medical procedure, and now posts a kind of State of the Union on “local social” sites. I agree with practically every word, most especially his conclusion that a successful local site…

must embody the historical value of traditional, offline word of mouth: trust and efficiency

“Trust” is one of my favorite words here at Loladex. I hadn’t quite crystallized my issues with competitive sites into the word “efficiency” — by which he means a quick & painless transfer of key information — but it’s a perfect summary. Indeed, I believe the Web can be more efficient than real-life word of mouth (although it certainly isn’t yet).

When asked how I want Loladex to differ from other sites, I now have my two-word answer.

Wired, Google Maps & Hyperlocal

I generally don’t read Wired magazine unless I’m flying, so I haven’t seen much of it lately. But yesterday, in Dulles airport on the way to California, I picked up the July issue & noted this cover line:

Google Maps and the Rise of the Hyperlocal Web

Turns out there were two loosely related stories inside: A sloppy kiss for Google Maps as a platform for the coming geoweb, and a “dispatch from the hyperlocal future” from cyberpunk author & pundit Bruce Sterling.

I agree that Google Maps — Google generally, really — is setting some of the terms of debate in local, and that KML, the emerging standard it acquired via its purchase of Keyhole, is a Good Thing.

Still, the story went a bit far in its “game over” portrayal of Google Maps as the epicenter of a movement that’s (according to me, anyway) far too young to have a leader, let alone a winner.

The story’s broader points were well taken, however, and the overall thesis — that people with tools, not companies with algorithms, will power this geostuff — captured something real. As always, I don’t like the facile equation of local=maps, but what can you do?

All of this dovetailed nicely with another July feature, a nice profile of Luis von Ahn — a MacArthur winner with a human-centric outlook on computing. The most interesting article in the issue, by far, and obviously applicable to local.

Bruce Sterling’s riff on hyperlocal, alas, was speculative quasifiction, and darn near unreadable. I’d like to see Wired tackle what “hyperlocal” actually means, but this was just a parade of buzzwords, mostly made up.