Blind Five Year Old

I’m not the biggest fan of Facebook for personal use. Instead, I hang out at FriendFeed. The main reason is because FriendFeed revolves around content instead of people. The secret sauce is the Friend of a Friend (FoaF) feature. FoaF lets me see content that my friends commented on or liked. So instead of my world view being limited to just my friends, I let my friends bring interesting content to me from other people.

Using people as filters. Information discovery at its best.

Facebook Friend of a Friend

The other day I visited Facebook and lo and behold I saw something different … yet familiar.

I haven’t liked Dexter. He’s not my friend. But I’m seeing Dexter’s status because Oguz and Louis (who are my friends) liked it. The beauty of this is that I enjoy Dexter (though I’m way behind and am only on season 3.) Sure enough my friends (my filters) brought me the ‘right’ content.

Here’s another instance. I’m not connected to Jason Falls. Sure I know who he is but we’re not buds. Yet, I’m seeing his status update because Susan and Louis commented on it. Once again, it’s content that is interesting to me. I’ve been talking about follow and friend abuse for a long time so it’s great to see others pruning their connections.

And while I’ve used two status examples, I’ve seen FoaF on photos and links as well.

I’d have to have my head in the sand not to know who Loic is, but I’m not friends with him. I see his link because Oguz commented on it.

The FriendFeedification of Facebook

Facebook’s implementation of FoaF as well as duplicate detection and aggregation all make me like Facebook a lot more. Suddenly, I can use Facebook like I use FriendFeed. In fact, it may actually work better since there are (sadly) so many more people on Facebook.

I’ll likely be spending more time on Facebook. That’s something few people – myself included – thought they’d ever hear me say. The only (big) thing remaining is lists so I can create different views of the content my friends and their friends bring me.

Oddly, I’m more confident this will happen given the continuing FriendFeedification of Facebook.

The other day I wrote about the potential for inflated Like numbers. In particular, I was interested in how comments were factored into the Like total.  It was pretty clear that Likes and comments were not mutually exclusive. But were comments a count of unique contributors or simply a total count of comments.

The Like Experiment

So, I ran a small experiment using an old satirical blog post: LOLCats and Religion: A Dissertation.

This post originally had two shares but no Likes or comments. So I went ahead and Liked it and asked my colleague Jeremy Post to have a comment dialog on the item. In all, we generated 10 comments.

One of my concerns was that comments might not always relate to the item and interestingly enough we actually did switch topics during the dialog from LOLCats to Dune. Go figure. (Note to self – fix image being attributed to blog posts.)

The Like Results

So what was the result? How many Likes did this old post rack up due to this comment stream? Sure enough, every comment is counted as a Like.

A quick check using my Facebook Like Number Bookmarklet reveals how the number is calculated.

So, did 13 others like this? No, it’s just two people having a conversation on a shared item. And that’s how you could get …

100 Likes from 2 People on 1 Item

Want to know the Facebook Like statistics for the page you’re on? No problem.

Facebook Like Number Bookmarklets

Using the old REST API you can find out the Facebook Like statistics for any page. For easy access, simply drag these two links to your bookmark bar.

FB Stats: Current Page

FB Stats: Home Page

The Current Page bookmarklet will provide Like statistics for the page you’re on. So, if you were on the ReadWriteWeb article about Facebook Places Deals you can click on this bookmarklet and be provided with the Like statistics for that page.

The Home Page bookmarklet will provide Like statistics for the home page for the site you’re on. Please note that this is not showing the aggregate Like statistics for the entire site, but just that of the home page.

Like Number Use Cases

Why are these bookmarklets useful apart from abject curiosity?

First off, you can determine the true number of Likes. Second, they provide competitive intelligence and potential insight into Facebook’s search algorithm (aka Facebook SEO). Do pages with a higher distribution of comments get a higher weight? I’m not sure.

This is one way to begin understanding the ways in which pages enter the Open Graph and how they are treated based on Like activity.

As you surf the web today you’ll inevitably run into Facebook’s Like button.

There are a number of implementations but they all tell you how many Likes that item (or object in Open Graph speak) has received.

When a Like is not a Like

But did 938 people really Like this rather interesting Slate article about Netflix? No.

Only 130 actually liked this article. The rest of that 938 is composed of shares and comments.

What you’re looking at above is XML output from a links.getStats call from Facebook’s old REST API. The data definitions for the link_stat table detail what share, like, comment and total represent.

The Like number shown to users is actually the total_count – “the total number of times the URL has been shared, liked, or commented on.”

I’m not particularly perturbed by lumping share and like together – those two actions are similar. In both cases I’m explicitly choosing to interact and promote that item. And I suspect that they’re doing this for some amount of backwards compatibility.

But comments seems like a stretch to me. I’m choosing to interact with a combination of item and person. My comment might have little to do with the item and more to do with the person sharing it. In this instance I could have commented on the movie viewing habits of the person sharing the item. Does that mean I ‘Like’ that item?

Like Number Inflation

At a minimum, I think this is a manipulation of perception. The numbers are part of a Like marketing campaign. Large Like numbers throughout the Internet make it seem like the functionality is being used frequently. Yet, here we see that the specific Like feature isn’t as popular as we might have suspected.

I’m still a fan (pun intended) of the Like button and the Open Graph, but showing this inflated number (even if it can be rationalized) seems disingenuous. What do you think?

Facebook recently launched their new Q&A product, aptly named Facebook Questions.

The Q&A space is white hot right now, in large part because of the SEO potential of the content. Q&A, if done correctly, creates highly focused long tail content that gets gobbled up by search engines. Look no further than Demand Media’s eHow as an example.

But is that what Facebook has in mind for Questions?

Facebook Questions Not Crawlable

Search Engine Land reported that Facebook Questions could not be crawled by search engines, and that Facebook has no plans to change that policy. The news was surprising, resurfacing the notion of the walled garden and sending mixed signals on Facebook’s strategy.

Facebook Search Powers Questions

In the first few days after launch Facebook search would force users into Questions whenever a search started with the five Ws or one H. Facebook search results would shift to Questions after you entered one of those interrogative words and then hit the space bar. This search feature is no longer active. (Sadly, I did not capture this behavior.)

Perhaps it was a simple test or Facebook pushed enough traffic through the product to receive the necessary amount of feedback. Either way, it showed the power of Facebook search (both in flexibility and volume) and pointed to a reason for not allowing search engines to crawl and index Questions. Does Facebook need traffic from outside the walled garden?

Questions and the Open Graph

Where things get confusing is why Facebook is pursuing Questions and the Open Graph in parallel. There are plenty of other Q&A sites out there. Many of them are using the Open Graph protocol.

Questions from other sites could easily show up in Facebook search results. So, why build a whole new product if you could just suck in content from everyone else? Conversely, if you were going to spend the resources to build that product and create all that content, wouldn’t you expose it to search engines so you could attract more users to Facebook? (Yes, that’s still possible.)

Of course, Facebook might want just one more product that will keep people on Facebook. And the longer users are on the site, the more often they’re clicking on ads and performing searches.

Articles are Second Class Open Graph Objects

Questions would be classified as an ‘article’ type in the Open Graph. Yet, articles seem like second class citizens in the Open Graph. Reports indicate that an object is not created for a page with og:type=article. This also means you can’t administrate likes on article content. In other words, you can’t publish to people who have liked an ‘article’ on your site or blog.

So, maybe Facebook is trying to create its own content instead of indexing what’s already out there? Again, this seems contrary to the Open Graph concept. Yet, if you believe status updates are a form of content publishing, then perhaps Facebook believes they can be the ultimate content creator.

Facebook Questions SEO

Of course, sites are getting around the article prohibition. Answers.com is now populating the Open Graph with their content using the ‘website’ type.

While Facebook frowns on this, it’s the way smart search marketers are going to work the system.

Creator or Aggregator

The Open Graph would indicate that Facebook wants to be an aggregator, to suck more and more of the Internet into the walled garden, allowing their users to find Internet content on Facebook’s terms – through the news feed and through Facebook search.

Questions would indicate that Facebook wants to be a creator, generating content as a way of keeping, attracting and engaging users. Though making them invisible to search engines takes attracting users out of the equation.

Which does Facebook want to be? Who knows, maybe both. But my money is on aggregator given the purchase of FriendFeed, promotion of Bret Taylor to CTO, advancements in type ahead search and roll out of the Open Graph.

Top Tweets look like Promoted Tweets. That’s no mistake.

Top Tweets

Twitter is getting users used to seeing something ‘stuck’ at the top of search results. Today it’s Top Tweets but tomorrow it will be Promoted Tweets. Top Tweets are innocuous for the most part and leverage game theory psychology around being the best or most popular for a certain fiefdom. Twitter would likely say that Top Tweets deliver ‘resonance’ (aka relevance) for that search result and they’re probably right.

Resonance is Quality Score

Twitter’s resonance sounds an awful lot like Google’s quality score. The launch of Promoted Tweets touted the fact that low resonance would mean the removal of that Tweet. Perhaps a few will fall below that resonance threshold and be removed. More likely, resonance will be used to secure top placement for a search term and/or reduce the CPC paid for that placement.

Multiple Promoted Tweets

Right now you see just one Promoted Tweet per search result. But lets look at how Twitter is displaying Top Tweets.

Top Tweets are stacked at the top of search results. What does that remind you of?

So, how long until we see stacked Promoted Tweets?

Integrated Tweets

The difference in presentation between a Top Tweet and a Promoted Tweet is small. This allows Twitter to swap Top Tweets for Promoted Tweets with little visual dissonance. Not only that, but Twitter could integrate Top Tweets and Promoted Tweets, stacking them by order of resonance. What better way to make that real estate interesting to users. By doing so they’ll prevent ‘banner blindness’. Far-fetched?

Trojan Frog

Twitter is undertaking a boiled frog strategy for getting acceptance of ads within search results by using Top Tweets as a Trojan Horse. In doing so, Twitter may actually have a viable paid search business in their future, and they’ve already got a potential ‘network’ in place with application partners.

Friend and follow abuse is still pervasive on social networks. Sure, it depends on what you want to get out of those social networks, but I still believe that less is more. Social networks have paid special attention to creating connections but very little to breaking them. Yet, that’s a critical part of any social construct.

ManageFlitter

I’ve used ManageTwitter to prune who I follow. In late April Twitter threatened to shut them down because they were in violation of Twitter’s Automation Rules and Best Practices. Thankfully a few UI changes and a name change saved the service.

ManageFlitter helps you unfollow people in a few ways. It suggests you unfollow people who aren’t following you back. This is my least favorite option since it feels too much like high school. Don’t get me wrong, I do use it, particularly for those who follow, wait for a follow back and then unfollow.

It also identifies inactive accounts, as well as those that are talkative or quiet. Unfollowing on all three criteria can help remove noise and dead weight. Finally, ManageFlitter will also tell you which accounts are using the default avatar, which can be a good sign of a spammer or automated account.

My Imaginary Unfollow App

As much as I like ManageFlitter it’s still rather rudimentary. So I got to thinking about what type of signals I would use to unfollow people.

Tweets without Links

If you’re using Twitter as a source for information and news, having people who excessively Tweet without links might not be very productive. Your dining activities, traffic woes or inspirational quotes might not be adding enough value.

Tweets that are Retweets

Retweets aren’t necessarily a bad thing but it would be good to know if the lion’s share of a person’s Tweets are Retweets. This could, in fact, be a good thing if the quality of your Retweets is high. Human filters are a good thing and I thank folks like Atul Arora, Rob Diana, Louis Gray and Mahendra for their continuing efforts in making me smarter. However, it could also be a bad thing if it’s just a steady diet of day old TechCrunch articles.

Tweets that are @Replies (not to you)

Some people use Twitter as a conversation platform. Now, I think that’s a bit “square peg round hole” but to each their own. However, it can be a as exciting as watching paint dry to watch folks banter back and forth.

Tweets that are @Replies (to you)

This is a clear sign that you’ve got some sort of real relationship with a person. It likely makes them a keeper regardless of any other signal.

Tweets that have multiple @Replies

Multiple replies in a Tweet could be a sign of someone who is efficiently responding to others. Someone who is actually being social rather than asocial.

However, you’d want to see the percentage be a small portion of total Tweets. Otherwise, multiple replies in a Tweet could be a sign of automation or a ponzi-like follow scheme.

Tweets that have Hashtags

There’s nothing wrong with hashtags per se, but overusing them might be a negative signal. This probably wouldn’t be a strong signal but if other signals were weak it might tip the balance.

Tweets that have multiple Hashtags

Another potential weak signal but it could be helpful in unfollowing those who seem solely interested in traffic generation without any engagement.

Tweets with an Exclamation Point

Stupid Fight uses this in calculating the ‘intelligence’ of a group of users. I’m not sure it would produce a valuable signal, but I’d want to find out.

Tweets with ALL CAPS

This is another Stupid Fight signal. I’m not sure if you’d base it on the % of capital letters in a single Tweet, for a collection of Tweets or look for capital letters in more than four straight characters. The latter would help exclude normal slang like OMG and WTF from this signal.

Tweets with Repetitive Links

Tweeting the same link multiple times is annoying and could be an indication of some sort of publishing configuration error or just poor Twetiquette.

Tweets with Links that others in your network have also shared

Here is the most interesting (and likely the toughest) signal of them all. How much unique content does a user contribute to your network? If 100% of the links Tweeted by an individual were also Tweeted by others in my network, are they really worth following?

In the end, you can’t look at everything. So, you need to make sure that the best content is coming into your worldview. Noise and clutter are your enemy. I’d give this signal a substantial weight, though you’d clearly have to recompute this signal frequently as you pruned who you followed.

Unfollow Algorithm

For each user, I’d want to know the raw number and % of total for each of these signals. I’d then score each signal on a relative scale and assign it a weight to come up with unfollow recommendations. I know this is easier said than done, but that’s why it’s my imaginary unfollow application. Maybe those with experience with the Twitter API could chime in. Are these signals viable?

What other signals would you use to unfollow people?

As we begin 2010, it’s time for me to go on the record with some predictions. A review of my 2009 predictions shows a few hits, a couple of half-credits and a few more misses. Then again, many of my predictions were pretty bold.

This year is no different.

The Link Bubble Pops

At some point in 2010, the link bubble will pop. Google will be forced to address rising link abuse and neutralize billions of links. This will be the largest change in the Google algorithm in many years, disrupting individual SEO strategies as well as larger link based models such as Demand Media.

Twitter Finds a Revenue Model

As 2010 wears on Twitter will find and announce a revenue model. I don’t know what it will be and I’m unsure it will work, but I can’t see Twitter waving their hands for yet another year. Time to walk the walk Twitter.

Google Search Interface Changes

We’ve already seen the search mode test that should help users navigate and refine search results. However, I suspect this is just the beginning and not the end. The rapid rate of iteration by the Google team makes me believe we could see something as radical as LazyFeed’s new UI or the New York Times Skimmer.

Behavioral Targeting Accelerates

Government and privacy groups continue to rage against behavioral targeting (BT), seeing it as some Orwellian advertising machine hell bent on destroying the world. Yet, behavioral targeting works and savvy marketers will win against these largely ineffectual groups and general consumer apathy. Ask people if they want targeted ads and they say no, show them targeted ads and they click.

Google Launches gBooks

The settlement between Google, the Authors Guild and the Association of American Publishers will (finally) be granted final approval and then the fireworks will really start. That’s right, the settlement brouhaha was the warm up act. Look for Google to launch an iTunes like store (aka gBooks) that will be the latest in the least talked about war on the Internet: Google vs. Amazon.

RSS Reader Usage Surges

What, isn’t RSS dead? Well, Marshall Kirkpatrick doesn’t seem to think so and Louis Gray doesn’t either. I’ll side with Marshall and Louis on this one. While I still believe marketing is the biggest problem surrounding RSS readers, advancements like LazyFeed and Fever make me think the product could also advance. I’m still waiting for Google to provide their reader as a while label solution for eTailers fed up with email overhead.

Transparent Traffic Measurement Arrives

Publishers and advertisers are tired of ballpark figures or trends which are directionally accurate. Between Google Analytics and Quantcast people now expect a certain level of specificity. Even comScore is transitioning to beacon based measurement. Panel based traffic measurement will recede, replaced by transparent beacon based measurement … and there was much rejoicing.

Video Turns a Profit

Online video adoption rates have soared and more and more premium content is readily available. Early adopters bemoan the influx of advertising units, trying to convince themselves and others that people won’t put up with it. But they will. Like it or not, the vast majority of people are used to this form of advertising and this is the year it pays off.

Chrome Grabs 15% of Browser Market

Depending on who you believe, Chrome has already surpassed Safari. And this was before Chrome was available for Mac. That alone isn’t going to get Chrome to 15%. But you recall the Google ‘What’s a Browser?‘ video, right? Google will disrupt browser inertia through a combination of user disorientation and brand equity. Look for increased advertising and bundling of Chrome in 2010.

Real Time Search Jumps the Shark

2009 was, in many ways, the year of real time search. It was the brand new shiny toy for the Internati. Nearly everyone I meet thinks real time search is transformational. But is it really?

A Jonathan Mendez post titled Misguided Notions: A Study of Value Creation in Real-Time Search challenges this assumption. A recent QuadsZilla post also exposes a real time search vulnerability. The limited query set and influx of spam will reduce real time search to an interesting, though still valuable, add-on. The Internati? They’ll find something else shiny.

Twitter finally got around to launching lists and immediately created a whole new competitive mania that may render them useless.

Listed

By simply showing Listed as a major metric Twitter encourages comparisons. Listed will be the new Followers. We’ll see Followers to Listed ratios cranked out by the companies who traffic in these sorts of meta data measures of authority and influence.

Instead of using lists to help users manage the stream of data, they’ve turned them into a competition.

Following Twitter Lists

The ability to follow lists also creates competition. Which SEO list is best? Who’s Ruby Rock Stars list should you follow? Lists allow users to segment, but how many instances are you really going to follow? Does it help me to follow 25 instances of an SEO list? Probably not.

Suddenly a person’s lists are going to have an attached Followers metric. You could argue that the number of Followers helps define comparative quality, but that hasn’t worked for users has it? So why would it work for lists?

My motivation for creating a list isn’t to attract followers, it’s to help me turn data into information.

Twitter Lists Do Not Equal Authority or Influence

The impetus for creating a list is for the user to manage their data flow. (Or it should be!) Using a data segmentation taxonomy as a proxy to show authority just doesn’t compute. The motivation is not to grant authority to those on a list, but to simply shape data.

Lists is a grouping, but it assigns no weight to an individual within that group. We all know people who are ubiquitous but might not be well regarded. Nevertheless you’d likely put them on a topical list.

Volume is essentially what Listed measures. People with varied interests will be added to many lists. People who have played the Followers game will be added to many lists. Quantity wins, not quality.

Furthermore, once people understand that lists are the new Followers you’ll have people asking to be added to lists, trading list additions and new accounts will spring up for the sole purpose of getting users on the ‘right’ lists. It’s an obvious gaming nightmare.

Twitter Lists Don’t Define You

There’s an offhand defense of lists I’m hearing many employ: “lists show how others think of you.”

Who cares! Guess what, I stopped caring about that my sophomore year in high school. But isn’t that the DNA of Twitter? A navel-gazing popularity contest that somehow is supposed to validate value and contribution.

Thanks but no thanks.

The Real Problem with Lists

The idea behind lists seems to be around user discovery.

It’s supposed to help you find “interesting accounts.” But lists (of any kind) don’t effectively do this because people are multi-faceted.

I have a varying level of expertise and contribution in many fields. My inclusion on Danny Sullivan’s Search Marketing list is nice, but will people following that list get value from my bicycling and book related tweets?

Lists give you a complete timeline for a group of people to whom someone has assigned a certain user defined attribute. It doesn’t mean you’re going to actually get content matching that user defined attribute. This mismatch makes it difficult to find ‘interesting accounts’.

Lists are simply a blunt instrument in the transformation of data into information.

The other day I read Steven Hodson’s Shooting At Bubbles post regarding Twitter 2.0. And it finally dawned on me!

Twitter is the Underpants Gnomes of the Internet

If you’re not familiar with the Underpants Gnomes, they were featured in a South Park episode in which the Gnomes devised an … interesting business plan.

Replace underpants with users (or VC cash) and you’ve got Twitter. Oh, sure they’ve alluded to some sort of business plan, but even as recently as a few weeks ago Evan Williams wasn’t willing to divulge a real revenue model despite John Battelle’s prodding.

I remember the Web 1.0 days of grow fast, grab market share and monetize later. Only a few survived this kowabunga style of business.

But who knows, maybe Twitter can turn underpants into profit.