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The Drudge Report Meets Twitter: The TCOT Report

tcot-report

The Republican revolution will be tweeted…

I’m guessing you’ve not heard about the TCOT Report. I hadn’t until yesterday. It just started this month. But it got my attention, because it’s a really innovative use of Twitter for grass roots idea generation and discussions. Social media skeptics rail against the echo chamber of geeks talking to one another about how grand social media is. So when non-geeks start leveraging social media’s best characteristics to improve things, it warrants attention.

OK, so what is TCOT?

Top Conservatives on Twitter

At its core, TCOT is a site that tracks the top conservatives on Twitter. As TCOT founder Michael Leahy describes it:

This list was first placed on the web on November 28, 2008. In the short time since then, it has become a bit of a rallying point for conservatives on Twitter. I think all of us who are on the list can conceive of many additional ways to improve the list to strengthen and grow the conservative community on Twitter.

You must primarily tweet on conservative themes and cannot be merely a “campaign profile” “political office holder profile” or a “radio or television program or publication promotional profile” to be on this list. New participants are welcome. Just nominate someone you follow or yourself and show that you are primarily on Twitter as a conservative.

Hats off to Leahy on this. It appears anyone, not just those with authority, can be included in the list, so long as you talk conservative themes. Here are the top ten conservatives on Twitter right now:

tcot-top-ten

The list itself is a resource for other conservatives looking to find like-minded people on Twitter.

What I found interesting was the TCOT Report.

TCOT Report: Crowdsourced Drudge Report

Leahy has set up the TCOT Report to track the news, opinion and discussion around conservative principles and politics. The real-time element of the TCOT Report is a continuous stream of tweets based on the hashtag #TCOT. Anyone can join in, as they are using the Twitter search function for this. To confirm this, I did a #TCOT tweet. Sure enough, it showed up:

tcot-feedAnd I even got a reply from someone in the TCOT community. The site also includes links to various news articles, opinion pieces and blogs.

To really understand the import of this initiative, consider the Rush Limbaugh ditto-heads.

Grass Roots Conservatism

Rush Limbaugh has millions of listeners to his daily radio show. People who are interesting in the news, and have opinions about it. The “social media” experience of this was to listen to your radio at the same time as everyone else.

When it comes time for communicating with others, there are two online formats for that: email and forums. Both have their place. Email is a great way to direct an action campaign. Forums, such as lucianne.com,  are great for longer discussion threads where all comments are displayed. Twitter appears to occupy a third spot, with some overlap with those other two.

Twitter lets folks express major or minor points easily, without guilt or worrying about whether a forum thread will grow. The hash tag identifies both the message and the person. And Twitter lets everyone weigh in on the events of the day, establishing their own brand of conservatism through their series of tweets.

At its best, politics is a world based on ideas. The ability to put forth an idea and argue persuasively is a the basis for the presidential caucuses that Barack Obama did so well with.

Once the election is over, what’s a person to do with all these ideas and enthusiasm? Channel them into engaging your fellow philosophical travelers. And right now, the Republican Party is thinking hard about its next moves. Given the grassroots orientation of the party, use of social media to discuss and spread ideas seems like a terrific idea.

Michelle Malkin, the conservative commentator and Fox News personality, is a fan of TCOT:

And if you haven’t opened a Twitter account (or haven’t figured it out yet), make sure to join TCOT.

And so is former House Majority Leader Dick Armey:

#TCOT @michaelpleahy Great to see so many conservatives on Twitter. It’s clear why everyone at @FreedomWorks wants me to use this more.

Not bad for a site that’s been up for a few weeks so far.

The Live Real-Time Web Version of the Drudge Report

I imagine some readers of this blog don’t agree with the Republican Party. That’s not my point in writing about TCOT. What interests me is the way some basic social media tools are being used for potentially great effect.

Twitter? Never be mainstream. Hashtags? People can’t be bothered. Twitter search? Why would I want to read the garbage people write?

What TCOT is doing is showing the potential in these tools. It’s too early to tell how this initiative will turn out, but a quick scan of the #TCOT tweets shows a lot of interest in this. I suspect the Obama administration and Democratic Congress will give a lot of energy to the TCOT Report. No one will displace the Drudge Report, but adding the instant reaction, and multiple points of view on myriad subjects in real-time is something that has proven addictive elsewhere.

If nothing else, I’m glad to see the continuing experimentation with social media outside the geeksphere.

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22The+Drudge+Report+Meets+Twitter%3A+The+TCOT+Report%22&who=everyone

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How to Mess with Bloggers’ Heads Using FriendFeed Lists

Steven Hodson, blogger extraordinaire at Winextra, posted this on FriendFeed:

“Okay this is cool .. someone has setup a Curmudgeons list and I’m apparently part of the list ROFL”

Inside the blue highlight box, you can see a referring URL that someone used to get to his blog:

http://beta.friendfeed.com/list/curmudgeons

That is someone’s List on FriendFeed. They’re put him into a List called “Curmudgeons”.

You can customize your own referral URLs with FriendFeed Lists. The tags you use for a blogger will be seen by that blogger as they look at their referral traffic.

Oh the possibilities…

  • beta.friendfeed.com/list/brilliant
  • beta.friendfeed.com/list/dumbass
  • beta.friendfeed.com/list/stop-blogging-about-twitter
  • beta.friendfeed.com/list/free-trial-of-viagra
  • beta.friendfeed.com/list/can-you-come-upstairs-for-dinner-please

You can create a List, click through on it, and the blogger will see your special anonymous message. Lists are easy to create and delete, meaning you can do it as much as you want.

So have fun with your favorite bloggers – send ‘em those subtle messages with the tags you use in your Lists.

But you don’t have to do that with me…uh…we’re cool, right?

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22How+to+Mess+with+Bloggers%E2%80%99+Heads+Using+FriendFeed+Lists%22&public=1

I’m Doubling Down My Subscriptions Because of FriendFeed Lists

As discussed here before, FriendFeed’s beta version includes the ability to tag users, putting them in different Lists you create. You can create your own programming channels.

I’m loving this feature.

One effect for me has to been to add subscriptions left and right. Why? Two reasons:

  1. Now that I’ve got themed Lists, I want there to be some good content in them! Right now, I’m subscribing to a lot of FriendFeeders who are into Enterprise 2.0 or who have an amazing eye for pictures.
  2. Managing a high flow of content is a lot easier. You can take users out of your Home feed, and tag them into different Lists. Check the Lists at your leisure, and you can see content for many, many more people. It doesn’t all just go flying by you.

Here’s my rendition of how Lists have changed the FriendFeed experience:

How about you? You started your Lists yet?

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22I%E2%80%99m+Doubling+Down+My+Subscriptions+Because+of+FriendFeed+Lists%22&public=1

Toluu Rolls Out Tagging {cool} {powerful} {discovery} {easy}

Toluu, everyone’s favorite blog recommendation application, has released a powerful new feature: tagging.

Tagging is a ubiquitous element of Web 2.0. As content creators and consumers add tags, everyone benefits. Things are easy to find, and there’s fun in clicking those tags – you never know what you’ll discover. I’m also a fan of the idea that tagging illuminates someone’s interests. Know someone’s tags, know their interests. A look to the right side of this blog shows the areas that I like to cover. As Toluu founder Caleb Elston has previously noted:

Tagging is super powerful. A few simple words can bring a ton of order and new usefulness.

And Caleb has followed up on that observation with powerful new tagging features in Toluu. Here is what’s new:

  • Tag Tab: Clickable tab for each blog page in Toluu, with several tag related features
  • Explore by Tag: every tag is clickable, leading to a list of related blogs
  • Inline Tagging: Instantly tag a blog right from a list of blogs
  • Users’ Tags: See the tags that a user has applied within the Toluu application

All in all, the features bring a new level of sophistication and community-generated perspective to the blog recommendations. Let’s check ‘em out.

Tag Tab

The tag tab is chock full of information for every blog listed in the Toluu system. Here’s a screen shot for this blog:

Tagging: The top of the Tag Tab has an entry box for entering new tags. A couple of notes:

  • Tagging is multiple word, comma separated. Yes!
  • Tags are auto-suggested, based on tags you’ve used before. This is a great feature. It makes it easier to add tags, and enforces consistency, which is so important in tagging.

My Tags: These are the tags you have already applied to a given blog.

Top Tags: These are the most popular tags applied by others to a given blog.

Related Blogs: Each Tag Tab includes a list of related blogs. These are blogs that share similar tags to the ones applied to a given blog. This makes discovering a bunch of blogs with a specific area of focus very easy.

Follow the Bouncing Tag

In fact, it gets even easier to find blogs with a specific focus. Just follow a tag where it takes you.

Notice the nice distinction above? You can see blogs with specific tags applied by anyone in the system. Or you can take a deeper dive, and see what someone you trust has tagged.

Sorting: There are four different ways to sort the list of blogs by tag:

  1. Popular = blogs are ranked by the number of times they received a particular tag
  2. Recent = sort by how recently a specific tag was applied to a blog
  3. Subscribers = sort by the number of Toluu subscribers to a blog
  4. A-Z = alphabetical

Inline Tagging

To make tagging easy and pervasive, inline tagging is supported.

As you look at your list of blogs, you can quickly tag them. I like this because it makes the tagging process fast and easy. I don’t have to go to each individual blog’s page inside Toluu to add tags.

User Profiles Now Have a Tag Story

All this tagging by users has another benefit. You can quickly see what someone is all about when you visit their profile in the Toluu system.

This is great. Toluu isn’t a full-fledged social network, but you can use it to find like-minded people. From these like-minded people, you can discover other blogs of interest.

The tags of a user are essentially a form of self-identification. Like declaring what political affiliation you have, or saying where you work. This at-a-glance insight into someone’s interests is a great way to figure out new people to follow inside Toluu.

Are You Toluu-ing Yet?

Caleb and his team have done a really great job with this new functionality. A lot of attention was paid to ease of use, and the subtleties of information discovery. He has built in the notion of discovery via the collective wisdom of crowds, or discovery via trusted information filters.

Toluu continues to innovate. Click here to see all posts tagged ‘toluu’ on this blog. It’s an impressive list of activity.

If you’re on Toluu, follow me at http://www.toluu.com/bhc3. I’ll follow back. And if you’re not yet on Toluu, I’m happy to email you an invite. Just leave a comment.

Nice job Caleb!

FriendFeed Lets You Tag Users: Now Expertise Finds You

FriendFeed’s new beta version is out. There are a number of new features there, which are well described by Bret Taylor on the FriendFeed blog.

I want to focus on a particularly powerful new feature:

The ability to tag the people to whom you subscribe.

In an earlier post, On FriendFeed, We’re All TV Channels, I described people as programming. Via our lifestreams, Likes and comments, we send a stream of content downriver to our subscribers. People make their subscription decisions based on that river of content.

Tags are logical progression in distinguishing people based on programming. FriendFeed has made it very easy to set up channels based on tags, and seek out different content depending on your mood. My initial set of tags are shown above.

On Twitter/FriendFeed, I asked this question:

What’s more valuable in the realm of information discovery? Finding relevant content, or finding people with relevant expertise?

The preference was generally for expertise over content. Marco made a good point:

find the expertise and the content will likely follow

I like that. It well describes the value of FriendFeed’s new user tagging feature.

In fact, FriendFeed just filled a gap in the way people find information.

Here’s what I mean.

Social Media Filling Gaps in the Ways We Learn

The diagram below describes a spectrum of learning that has been enabled by the Web.

On the left is the search revolution led by Google. Google’s search was a revelation when it started, and it’s still going strong. On the right is a method of learning that dates back at least to Ancient Greece: question and answer.

Social media fills the gap between the two. Social bookmarking (Del.icio.us, Diigo, Ma.gnolia) was a very innovative approach. What content have other users found useful? Rather than depend on Google’s crawlers and algorithm, you could turn to the collective judgment of people. What did others think was useful?

Social bookmarking continues to be really good for directed searches, and serendipitous discovery.

But how about a different form of finding information?

I like how Mary Anne Davis described a shift to having the expertise of others brought to you, in the form of lifestreams, in this comment on FriendFeed:

A curated life. Lots of choices and more friends who I trust suggesting what they are passionate about influencing how I might spend time reading, listening or watching.

There are three reasons lifestreaming will emerge as an important new source of knowledge:

  1. A lot of good information and opinion occurs in conversational social media (e.g. Twitter). But this media isn’t usually bookmarked, and it doesn’t rank highly in search results.
  2. There are times you’re not actively trying to learn about a subject. But taking in a curated stream of content can be helpful down the road.
  3. You may not even know the questions to ask or the breadth of information you don’t know. Following the lifestream of someone who has knowledge about a subject is a great way to educate yourself.

The value of these lifestream apps really kicks in when there a lot of users. FriendFeed is growing, but you had to accept all lifestreams combined (which has its own merits). With the new tagging capability, you can set your “programming” the way you want.

I initially wasn’t sure about the new design of the FriendFeed beta, as I liked the spare quality of the original. But I’m warmed up to it now. Tagging people’s lifestreams….cool idea.

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22FriendFeed+Lets+You+Tag+Users%3A+Now+Expertise+Finds+You%22

How Would Social Media Help You in Your Job?

I’m having a ball with social media out in the consumer web. Blogging, FriendFeed, Twitter, Facebook. I’m learning so much about technology, new companies and people’s attitudes regarding Web 2.0. Along the way, some collaboration and a new job actually happened out of all this fun.

Now why can’t we see some of these same effects in the place where most of us spend a third of our day? We’re seeing live implementations of social media inside organizations (aka Enterprise 2.0). It’s a good sign.

I’m now in a job where I’m thinking about this a lot. And I figured I’d start with myself. Where would social media have made a difference in my two previous Big Corporate jobs:

Both companies were examples of today’s modern company, with a heavy information orientation. It’s been years since I worked at either, but here is how social media could have helped me in my jobs.

May Department Stores

The buying office of a retailer is responsible for picking the merchandise you see on the floor. Buyers also plan and execute promotions, set prices and ensure optimum amount of inventory on the floor and in the warehouse. We also had to communicate with the department managers of dozens of stores.

Here are the social media that would have helped me (if we had the Web back in 1990-1994):

  • Twitter: Yup, I would have loved Twitter. An easy way to fire off updates out to the field of department managers. And they would have sent back news of things they were seeing. Would have been a huge help during the crazy Christmas season.
  • Blog: I would have blogged about the weekly promotions. There’s a fair amount of work that went into them (promo prices, signage, focus of the ads), and documenting all that would have been useful. New products that we bought would have been good to discuss as well.
  • Bookmarking and notetaking: Assuming we had the world wide web back then, I would have bookmarked and noted a number of things for the job: competitor ads and pricing, product promotions I liked, new products I’d seen elsewhere.

Bank of America

At BofA, my group raised debt for corporations. Deals could run anywhere from $25 million to $6 billion. It was an information-intensive job.

The work consisted of three primary activities: (1) win the deal; (2) sell the deal; (3) close the deal via documentation. You had to stay on top of comparable deals, industry trends, capital market trends and general market chatter. Our group was divided into Structurers (me), who worked with clients to win and structure deals; and Distribution, who sold the deal to the market. Distribution always had the best information.

Social media I would have wanted:

  • Twitter: Again! I really would have wanted to see the ongoing chatter of the Distribution guys. They picked up all sorts of incredibly valuable market intelligence during the day. They used to IM. Now I’d want them to tweet.
  • Wiki: Every deal should have had a wiki space, with its “win the mandate” phase, its “sell it to the market” phase and the documentation phase. Wikis would have been good for handling the whole deal cycle.
  • Feed Reader: There were market data publications to which BofA subscribed. Getting a feed of deal information would have been a huge help. We were chasing information down in paper publications.
  • Bookmarking and notetaking: When deal, market or industry news came through, I needed a place to save it. I was always going back to find stuff I’d seen earlier. Bookmarking would have helped a lot. Note taking too – capture some information or thoughts, tag it and come back to it later.
  • Blog: My group wouldn’t have had much use for a blog amongst ourselves. But a blog that updated the rest of the bank as to what was happening in our particular capital market (syndicated loans) would have been perfect. We had other groups asking us often about market conditions.

I’d Love to Hear About You

Maybe you’re already using social media inside your company. Or perhaps you’ve been thinking, “my company really needs…”

If you’ve got any ideas to share, I’d love to hear them.

*****

If you want an easy way to stay on top of Enterprise 2.0, I invite you to join the Enterprise 2.0 Room on FriendFeed. The room takes feeds for Enterprise 2.0-related items on Twitter, Del.icio.us and SlideShare. To see this room, click here: http://friendfeed.com/rooms/enterprise-2-0

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22How+Would+Social+Media+Help+You+in+Your+Job%3F%22&public=1

Tag Clouds for Our Lifestreams

We are marching down the lifestreaming road. There are a proliferation of lifestream apps, such as FriendFeed, SocialThing, Strands, Swurl and others. Lifestreaming is getting hotter, and there’s some thought that lifestreaming will be the new blogging:

Sites and social tools like these and many others encourage more participation on the social web than ever before. Although the social participants on these sites are often more active in socializing than they are in blogging, there’s still that need to stake out your own piece of real estate on the web. But we wonder: does that really need to be a blog anymore? Perhaps not.

It’s a great concept, one that Mark Krynsky has been chronicling for a while at the Lifestream Blog.

An area I think that is ripe for inn ovation here is the ability to find the meta data from one’s lifestream. On FriendFeed, people will have multiple services that fill up their lifestreams. A couple issues that crop up on FriendFeed are:

  • Figuring out whether to subscribe to someone
  • Catching up on what particular individuals have been streaming

Because there is one thing that has been noticed with all this lifestreaming – there’s a lot of information generated (or “noise” as some might say).

So here’s my idea:

Create tag clouds for our lifestreams

What do I mean? Read on.

FriendFeed Lifestream

I’ll use the lifestream service with which I’m most familiar, FriendFeed. Here are the tag clouds I’d like to see for each user’s lifestream:

  • Blog
  • Music
  • Google Reader shares
  • Bookmarks
  • Twitter
  • YouTube
  • Flickr
  • Digg
  • etc…

And I’d like to see tag clouds for what users Like and Comment on. Because on FriendFeed, Likes and Comments have the same effect as a direct feed of someone’s lifestream: they put the content into the feed of all their followers.

So via the tag cloud, I’m can quickly come up to speed on what someone is interested in.

Let’s Make Tagging Easy

I don’t propose that users suddenly tag their own streams. Rather, let’s leverage the work of others.

It’s de rigueur for Web 2.0 apps to include tagging. Bloggers tag. Social bookmarkers tag. Music lovers tag. Why not pull the tags applied to the source content into the lifestream?

Here’s what I mean. My blog has plenty of tags. These tags are included in the RSS feed of my blog. So any feed that includes my blog should include these tags. Let’s leverage:

  1. The tags that people apply to their own Web 2.0 content
  2. RSS/Atom feeds that include tags

For some background on this, click here for a page on Technorati that talks about tags in feeds.

By leveraging the tagging work already resident in user-generated content, one can quickly build up a tag cloud for lifestreams.

An Example: Google Reader Shares

Google Reader is a good example. People ‘share’ blog posts they read via their Google Readers. Sharing lets others see the articles that someone finds interesting and useful. And of course, those blog posts that someone is sharing have tags.

Here’s what the tag cloud of my recent Google Reader shares looks like. I’ve simulated the tag cloud by using Wordle for the tags.

You can see my interests lately: Enterprise 2.0, FriendFeed, social media. If someone wanted to get a quick sense of the things they’ll see by subscribing to me, this tag cloud answers that. And if someone is curious about the specific posts I’ve been sharing that relate to a subject, they could click on one of the tags and get a list of my Google Reader shares.

Curious, I ran the same analysis on the Google Reader shares of four people I follow on FriendFeed: Robert Scoble, Louis Gray, Sarah Perez, Mike Fruchter. Here are the topics they’ve been sharing lately:

Robert Scoble clearly is following the iPhone and Google. Louis Gray was following the happenings at Gnomedex. Sarah Perez is pretty even in her interests, with FireFox, social bookmarking, FriendFeed, Twitter, search and photos among her favorite topics. Mike Fruchter has been reading up on Twitter and social media.

Just like that, I’ve gotten a sense for their interests right now. And if those were true tag clouds, I could click the tag and see the Google Reader shares. Robert Scoble is really good at tracking useful relevant things. Clicking the ‘iPhone’ tag and reading his shares would be a quick way to understand what’s goin.

Tags + Wordles

As I said, most user generated content comes with tags these days. So pulling these into the feeds and representing them in a tag cloud would be a fantastic move forward in creating lifestream tag clouds.

But what about Twitter? There are no tags on tweets. Not a problem. FriendFeed and other lifestream services could do a Wordle-like tag cloud. Tally the most common words in someone’s tweets, represent it as a tag cloud. And make the tag cloud clickable, which would essentially run a Summize Twitter search of the user’s tweets for a given tag.

Use Existing Metadata to Solve Two Problems

The key here is to not make it onerous on the end user. Tag once, re-use everywhere. If desired, users could be given the option to add tags to their own lifestreams. But the core idea is to eliminate double tagging work for users.

If this could be done, you’ve got a visual representation of people’s lifestreams. And an easy way to find the specific entries in a lifestream that relate to a topic.

Lifestreamers – would you want something like this

I’m @bhc3 on Twitter.

Why Isn’t This the Tag Standard? Multi Word, Comma Separated

Tagging is a great way to put context on user generated content. The tag cloud to the right shows what the hundreds of thousands of blogs were talking about on the evening of August 21. (Click the image to see what bloggers are talking about right now).

Pretty much any web 2.0 service that has user-generated content supports tags. Flickr. YouTube. Del.icio.us. Google Reader. Last.fm. Tagging is entrenched in the web 2.0 world, and it’s one of those idea that spread without any standards.

But there is a problem of no single standard…

Beta, VHS.

Blue-Ray, HD-DVD.

Space or comma delimited?

What’s happened is that tagging formats are all over the map. Each web 2.0 service came up with what worked best for its product and developers:

This post at 37signals described the same tag formats above, and it got a lot of comments. Good energy around the subject. Brian Daniel Eisenberg thinks the failure to have a consistent tag method may undermine its adoption by the masses.

To me, there really is one best format.

Multiple Words, Comma Separated

I tweeted this on Twitter/FriendFeed:

Can there be a universal standard for tags? Multi-word tags, comma separated. Odd combos (underscore, dot, combined) are messy, inconsistent.

You can see the comments on the link. The gist of them? Multiple words, comma separated is the best format. Here’s why I think so:

  • Forced separation of words changes their meaning (“product management” or “product” and “management”)
  • Forced separation of words creates tag clouds that misrepresent subjects (is it “product” content? or “management” content?)
  • With single terms, too many ways for users to combine the same term:
    • productmanagement
    • product.management
    • product_management
    • product-management
  • Writing multiple words with spaces between them is the way we learn to write
  • Putting commas between separate ideas, context, meanings and descriptions is the way we write

Let people (1) use more than one word for a tag, (2) written naturally without odd connectors like under_scores, and (3) using commas to separate tags. These rules are the best fit for germanic and romance languages, and I assume for most other languages as well.

To Brian’s point about the masses, let’s make tagging consistent with writing.

For Developers, It’s Pretty Much a Non-Issue

In The Need for Creating Tag Standards, the blog Neosmart Files writes:

Basically, it’s too late for a tagging standard that will be used unanimously throughout the web.

A lot of developer types weighed in on the comments. For the most part, they’re sanguine about the issue of different formats. Rip out any extraneous characters like spaces, periods, underscores, etc. What’s left is a single string that is the tag.

It’s About the Users

The issue fundamentally is how boxed in people are if they want to tag. In the Neosmart Files post, commenter Jason wrote this:

As this topic suggests, there are issues in resolving various tags that whilst literally different they are contextually equivalent. I believe this to be the critical juncture. Perhaps the solution lies not in heaping upon more standards, but improving the manner in which tags are processed by consumers.

From my perspective, multiple word, comma separated format is the most wide open, flexible way to handle tags. If a user likes running words together, he can do it. If a user wants to put underscores between words, she can do it. If a user likes spaces between words, not a problem.

But making users cram together words in odd combinations takes them out of their normal writing and thinking style. Tags should be formatted with humans in mind, not computers.

That’s my argument. What say you?

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22Why+Isn%E2%80%99t+This+the+Tag+Standard%3F+Multi+Word%2C+Comma+Separated%22&public=1

Delicious and Diigo: Differ in What It Means to Be “Social”

Andy Brudtkuhlhas has a nice post, 6 Reasons Diigo is Better Than Delicious. In the post, one of the reasons he cites for Diigo’s being better is its social aspects:

Diigo has an extra level of social networking that Delicious does not provide – at least not in a usable manner. You can connect with people that have similar interests based on what you tag.

I’ve been playing with them a bit. Here’s an initial impression I have of how “social” works in the two bookmarking services:

  • Diigo uses content to find people
  • Delicious uses people to find content

Delicious, an original web 2.0 company, still has “user-generated” as its core raison d’être. Diigo has the later-stage web 2.0 philosophy of being a “social network”.

Diigo: Social Is as Social Does

Diigo has been built to find people based on common bookmark and tag interests. It has social network features throughout:

  1. Finding people on Diigo is much easier than on Delicious
  2. Diigo generates user matches based on tag and bookmark compatibility
  3. User profiles
  4. You can see who has visited your profile page
  5. You can comment on the bookmarks of others
  6. You can share bookmarks with specific users

Networking on Diigo Is Easier

A basic function – finding other users – is much easier on Diigo than on Delicious. The graphic below shows the results of a search for my name:

On Delicious, you have to know someone’s Delicious handle. On Diigo, you can use a person’s regular name. Diigo’s approach is more like that of today’s various social networks:

Social networks make finding users easy. So does Diigo. Delicious doesn’t.

Diigo Social Recommendations

Diigo attempts to match you to others based on common bookmarks and tags. As the graphic below shows, it’s not exactly Toluu-like in its matching.

Levels of compatibility at 2% and 3% don’t quite inspire clicks for further investigation. Social recommendations are a work-in-progress at Diigo. Delicious doesn’t do recommendations.

Diigo User Profiles

For each link, Diigo provides a user profile of everyone who bookmarked the link:

So when you check out others who bookmarked something you like, you can quickly determine if they are someone to whom you want to subscribe. Delicious also lets you look at someone’s activity, but you have to click on their handle to see their page. There’s no profile provided on the list of users who bookmarked a link.

Diigo Visitors Info, Commenting, Bookmark Sharing

I’ll skip the screen shots for these Diigo functions. But here’s how they foster social networking:

  • Who visited my profile? Potential matches. Also lets you know when your social network paid your bookmarks a visit.
  • Commenting. Commenting enables discussion with others. Socializing.
  • Bookmark sharing. You can call out specific users with whom to share a bookmark. Very social.

Delicious Has More of a Crowdsource Feel

Where Diigo is social, Delicious emphasizes the interests of all users. What are people finding interesting. That’s not to say it doesn’t have social network aspects. On Delicious, you can:

  • Add users to your network
  • View your network’s bookmarks
  • Become a ‘fan’ of someone

But Delicious pretty much stops there on the social aspects. The rest of Delicious is centered around bringing order to the huge volume of crowdsourced bookmarks.

Delicious: Who Bookmarked That Link When?

The new Delicious has a really cool timeline that shows who bookmarked a given link when:

That timeline is a thing of beauty. Users, dates, tags, notes. Where Diigo wants to get you socializing around a bookmark, Delicious wants to provide you with information about how a link fared with the public at large.

As mentioned above in the Diigo user profile section, Delicious doesn’t provide user profiles in this listing.

Wrapping It Up

The new Delicious continues its mission of organizing a massive number of user-generated bookmarks and tags. It looks cleaner, and I like the way information is presented. Information organized by an army of user librarians. “Social” in this context means your bookmarks and tags are exposed to others, and you can find related content based on what others are bookmarking and tagging. People are the basis for discovering content.

Diigo wants people to interact via common interests in content. It has a lot of social network hooks. “Social” in this context means establishing and building relationships with others. Content is the basis for finding people.

I’d love to hear your thoughts on the different approaches of Delicious and Diigo. And you can find me on both services:

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22Delicious+and+Diigo%3A+Differ+in+What+It+Means+to+Be+%E2%80%9CSocial%E2%80%9D%22&public=1

I’ve Joined Connectbeam, and Social Media Got Me the Job

On Wednesday August 13, I start my new job as Senior Product Manager for Connectbeam. Connectbeam provides social bookmarking and networking to the enterprise. The goal is to foster better information management and discovery, and to connect colleagues around projects and common interests.

Going a bit further, here is a note from privately-held Connectbeam’s about page:

Connectbeam’s architecture and core application (Spotlight) were designed to help people in any role, across the enterprise, connect with both the growing pool of information and colleagues with the expertise and experience to help them get their jobs done more intelligently and more quickly. We enable this by aggregating the social metadata that is generated naturally by using the web into a single repository that everyone in the company can access and use.

Current customers include: Procter & Gamble, CSC, Bristol-Myers Squibb, Honeywell, 3M, Intel, Pfizer and Booz Allen Hamilton.

Why Connectbeam?

The problem Connectbeam is tackling greatly interests me. How to manage information to make individuals smarter, help people find information and determine the ways in which common interests establish and build relationships. There are many posts on this blog along those lines. Here are six of them:

  1. FriendFeed ‘Likes’ Compatibility Index
  2. Hey Yahoo! Forget MSFT, GOOG. Change the Search Rules.
  3. Who Is Your Information Filter?
  4. Knowledge & Innovation: The Journey Is as Valuable as the Destination
  5. Tag Recommendations for Content: Ready to Filter Noise?
  6. Social Media Consumption: You Want Signal or Discovery?

I also like Connectbeam’s delivery model. I am a fan of cloud computing, and in my experiences at eFinance and Pay By Touch, customers got comfortable. But I also ran into companies that only wanted applications behind their firewall, which is what we sold at BEA Systems. Security, control and reliability are still important, and recent outages at Amazon S3 and Gmail highlight those concerns. Connectbeam runs as an appliance behind companies’ firewalls.

Connectbeam delivers its model as an integration with existing search engines and other applications. For instance, Connectbeam now has an integration with Microsoft’s SharePoint, the most pervasive collaboration software out there. The Microsoft SharePoint Senior Technical PM even tweeted about it.

I’m a big believer in the ability of enterprises to improve the ways that information is created, disseminated and managed by employees. Those that get this right will be better-positioned in our information-centric economy.

FriendFeed Has Opened My Eyes

I joined BEA Systems to do product marketing for enterprise 2.0. Prior to that, I had done a little tweeting and had a Facebook profile. But not a whole lot of social media. I started blogging in February to eat my own dog food when I was marketing web 2.0 to companies. I needed to immerse myself in the world to really understand it.

Well, blogging has become quite important for me. FriendFeed has become just as important.

FriendFeed opened my eyes to the possibilities of knowledge as the basis of relationships. The ways in which content from a variety of sources is a powerful, addictive basis for learning, conversations and collaboration. How activity streams are compelling reads. I’ve been active on FriendFeed since March, and it shocks me how much I know about web 2.0 and technology in general versus last year. I’ve still got much to learn, and FriendFeed will continue to be a good source for that.

So why can’t companies get better around that too? Having eaten my own dog food on FriendFeed, I’m ready to work with employees and companies to improve the ways in which information is created, tracked and shared.

How Social Media Got Me the Job

You’ve probably seen more than a few posts saying that today’s resume is your Google search results. Your social network sites, content, updates, what others say about you…all of it is searchable.

Like me, Connectbeam CEO Puneet Gupta subscribes to Google Alerts for “enterprise 2.0″. Well one of my blog posts was listed in an alert. It caught Puneet’s attention, so he read the blog a bit more. Liking what he saw, he then investigated my name out on the web. Among the sites he found was one where I was a recommended blogger to follow (thanks Daryl, Franklin, Louis, Mark, Mike, Rob, Steven). Those recommendations were in part made due to the wonderful effects FriendFeed has for bloggers.

It didn’t hurt that I had been involved with enterprise 2.0 at BEA Systems. So after doing some due diligence, he left this comment on my blog:

Hutch:
Would love to connect with you and discuss some ideas.

I reached out to him, did some interviews, and the rest is history.

Looking Ahead

The new job will give me a more structured basis for looking at the ways in which information is managed. I plan to look more deeply at some of the consumer social bookmarking sites.I’m a product manager for Connectbeam, but a lot of my job will involve product marketing too.

I expect working in this area will influence my blogging subjects some. But I’ll blog about other fun stuff along the way as well.

Gotta go – my commute is from San Francisco to Mountain View. Need to battle the 101 traffic.

*****

If you want an easy way to stay on top of Enterprise 2.0, I invite you to join the Enterprise 2.0 Room on FriendFeed. The room takes feeds for Enterprise 2.0-related items on Twitter, Del.icio.us and SlideShare. To see this room, click here: http://friendfeed.com/rooms/enterprise-2-0

*****

See this post on FriendFeed: http://friendfeed.com/search?q=%22I%E2%80%99ve+Joined+Connectbeam%2C+and+Social+Media+Got+Me+the+Job%22&public=1

Filtering FriendFeed – How Crowdsourcing Can Solve This

It would be nice to have filters on FriendFeed. For instance, it would be nice to be able to hide any post containing the word “Obama” without having to hide someone’s other stuff. Or the ability to hide any entry containing the word “ubuntu”, etc.

Thomas Hawk, FriendFeed direct post, May 1, 2008

The need for filters on FriendFeed is a recurring topic. Click here to see the numerous entries that contain the words ‘friendfeed’ and ‘filters’. Louis Gray notes the need for this in a recent post.

I want present an idea for filters that has two pieces:

  • Category filters
  • Keyword filters

The two pieces are interrelated, and crowdsourcing will be used to build out the category filters.

Let’s get to it, shall we?

Category Filters

FriendFeed already has a “Feed Preferences” page for each member. Here is where you can manage your category and keyword filters. The graphic below is mockup of this:

A. Category Filters

Various categories will be displayed, along with a link to the full list of categories. In the example, above, I say that I’d like to filter out all FriendFeed entries that relate to politics.

The value of category filtering is that it prevents you from having to manage every keyword that might relate to a category. In a recent post, I noted Dave Winer’s 38 different politics-related terms. For instance, he used the terms: Hillary, HRC, Clinton, Edwards, Obama, Rove, etc. Having the ability to automatically filter those out without having to set up keyword hides over and over would be a great benefit to many members. Particularly as FriendFeed gains traction with a flood of new members.

Now how would FriendFeed know that Hillary, Obama, HRC, etc. are part of the politics category? Keep reading.

B. Keyword Filters

Members will need the ability to see what words they have hidden. They can un-hide keywords, or add new keywords to hide directly on the Feed Preferences UI.

Keyword-Based Hides

FriendFeed currently supports hiding specific entries, plus entries from specific members and services. For instance, you can hide all Twitter updates. What is lacking is the ability to filter out entries with specific terms in them.

For instance, shown below are three tweets from Dave Winer regarding politics:

What I’d like to do is apply the Hide function to anything with ‘Harold Ickes’ or ‘Henry Waxman’. This is a mock up of that screen below:

A. Full Text of Entry Displays

The full text of the entry appears. Each word of the entry includes a link. The links are easy ways for members to populate the ‘hide terms’ input box.

B. Hide Terms Input Box

Commas separate each term.

C. Categorize the Terms to Be Hidden

As the member hides the terms, they will be asked to apply a category. The most popular categories previously applied to the keywords will be displayed. Or the member can type a category into the input box, and FriendFeed will auto-suggest different categories with each character entered.

Why do this? This is the basis of the crowdsourced solution.

Let the People Decide

People will have a much better handle on the categories that apply to a keyword than will a heavy-duty algorithm. Such human filtering is the basis of tagging.

Two elements are relevant here:

  • The need to prevent bad categories being assigned to keywords
  • The motivation to do this categorizing

Use Bayesian Stats to Prevent Bad Categories

Here’s the issue you want to avoid. Some prankster assigns the football category to the term “Paris Hilton” while hiding all entries containing her name. Suddenly, members who are filtering out football entries stop seeing their Paris Hilton updates (yes I know, horrors…).

Enter Bayesian statistics. Carl Bialik, a columnist for the Wall Street Journal, has a great column on the use of Bayesian stats for online ratings. The gist of this approach is that all items in a rating system are born with identical ratings. Their ratings only change as people vote, and it takes a sufficient number of votes to really move the rating of an item. Here’s an example of this from the WSJ column:

For instance, as noted in the column, IMDB.com doesn’t use straight averages to list the top 250 movies of all time, as voted on by its users. Instead, each movie starts out with 1,300 votes and a ranking of 6.7, which is the site’s average. That helps smooth the effects of a few intense votes; it takes a lot of votes to budge the IMDB meter up or down from 6.7

That same approach would be applied inside FriendFeed. It would take a large number of people putting a keyword into the same category before the keyword actually became “part” of that category.

Once a keyword graduates to a category, any users filtering that category won’t see entries with those keywords.

Motivation

Why would anyone bother to categorize the keywords they hide? One answer – not everyone will. But there are two drivers of members doing some keyword categorization.

First, members need to recognize that they are contributing to a system from which they are likely benefiting. If you filter any category, you will be benefiting from the work of others’ who have categorized keywords.

Second, the categorization experience has to be simple and fast. You’ve got the member right there, motivated to hide a term. Make it easy for them to channel that motivation into a simple categorization. The most popular previous categories are displayed, making it easy check them. And the auto-suggest feature can be done fairly quickly. I like how Faviki is doing it:

Faviki draws from thousands of different Wikipedia entries for this list.

Final Thoughts

One thing to consider here is that every entry coming into FriendFeed would need to be filtered for keywords. Serious processing power will be required. Fortunately, the FriendFeed guys have firsthand experience with high volumes of real-time queries for keywords at Google.

With regard to this proposal, I haven’t (yet) seen anything on the market that will provide the category tags that would help filter FriendFeed. Since it’s the members who are most in tune with what they want to filter, their common sense and motivation should be leveraged.

As FriendFeed grows, imagine new members easily managing the flow of information by simply filtering the politics category rather than having to set up an extensive list of new keywords. It would make the experience that much better for everyone.

*****

See this item on FriendFeed: http://friendfeed.com/search?q=%22Filtering+FriendFeed+-+How+Crowdsourcing+Can+Solve+This%22&public=1

Tag Recommendations for Content: Ready to Filter Noise?

In a recent post, I suggested that the semantic web might hold a solution for managing noise in social media. The semantic web can auto-generate tags for content, and these tags can be used to filter out subjects you don’t want to see.

As a follow-up, I wanted to see how four different services perform in terms of recommending tags for different content.

I’ve looked at the four services, each of which provide tag recommendations. Here they are, along with some information about how they approach their tag recommendations:

  • del.icio.us: Popular tags are what other people have tagged this page as, and recommended tags are a combination of tags you have already used and tags that other people have used.
  • Twine: Applies natural language processing and semantic indexing to just that data (via TechCrunch)
  • Diigo: We’ll automatically analyze the page content and recommend suitable tags for you
  • Faviki: Allows you to tag webpages you want to remember with Wikipedia terms.

Twine and Diigo take the initiaitve, and apply tags based on analyzing the content. del.icio.us and Faviki follow a crowdsourced approach, leveraging the previous tag work of members to provide recommendations.

Note that Faviki just opened its public beta. So it suffers from a lack of activity around content thus far. That will be noticed in the following analysis.

I ran the six articles through the four tagging services:

  1. The Guessing Game Has Begun on the Next iPhone – New York Times
  2. TiVo: The Gossip Girl of DVRs – Robert Seidman’s ‘TV by the Numbers’ blog
  3. Twitter! – TechCrunch
  4. Injury ‘bombshell’ hits Radcliffe – BBC Sport
  5. Why FriendFeed Is Disruptive: There’s Only 24 Hours in a Day – this blog
  6. Antioxidant Users Don’t Live Longer, Analysis Of Studies Concludes – Science Daily

The tag recommendations are below. Headline on the results? Recommendations appear to be a work in progress.

First, the New York Times iPhone article. Twine wins. Handily. At Diigo gave it a shot, but the nytimes tags really miss the mark. del.icio.us and Faviki weren’t even in the game.

Next, Robert Seidman’s post about Tivo. Twine comes up with several good tags. Diigo has something relevant. And again, del.icio.us and Faviki weren’t even in the game.

Now we get to the trick article, Michael Arrington’s no text blog entry Twitter! The table turn here. Twine comes up empty for the post. Based on the post’s presence on Techmeme and the 400+ comments on the blog post, a lot of people apparently bookmarked this post. This gives del.icio.us and Faviki something to work with, as seen below. And Diigo offers the single tag of…twitter!

Switching gears, this is a running-related article covering one of the top athletes in the world, Paula Radcliffe. Twine comes up the best here. Diigo manages “bombshell”…nice. del.icio.us and Faviki come up empty, presumably because no users bookmarked this article. And none of them could come up with tags of “running” or “marathon”.

I figured I’d run one of my own blog posts through this test. The post has been saved to del.icio.us a few times, so I figured there’d be something to work with there. Strangely, Twine comes up empty. Faviki…nuthin’.


Finally, I threw some science at the services. This article says that antioxidants don’t actually deliver what is promised. Twine comes up with a lot of tags, but misses the word “antioxidants”. Diigo only gets antioxidant. And someone must have bookmarked the article on del.icio.us, because it has a tag. Faviki…nada.

Conclusions

Twine clearly has the most advanced tag recommendation engine. It generates a bevy of tags. One thing I noticed between Twine and Diigo:

  • Twine most often draws tags from the content
  • Diigo more often draws tags from the title

Obviously my sample size isn’t statistically relevant, but I see that pattern in the above results.

The other thing to note is that these services do a really great job with auto-generating tags. For instance, the antioxidant article has 685 words. Both Twine and Diigo were able to come up with only what’s relevant out of all those words.

With del.icio.us and Faviki, if someone else hasn’t previously tagged the content, they don’t generate tags. Crowdsourced tagging – free form on del.icio.us, structured per Wikipedia on Faviki – still has a lot of value though. Nothing like human eyes assessing what an article is about. Faviki will get better with time and activity.

Note that both Twine and Diigo allow manually entered tags as well, getting the best of both auto-generated and human-generated.

When it comes to using tags as a way to filter noise in social media, both system- and human-generated tags will be needed.

  • System-generated tags ensures some level of tagging for most new content. This is important in an app like FriendFeed, where new content is constantly streaming in.
  • Human-generated tags pick up where the system leaves off. In the Paula Radcliffe example above, I’d expect people to use common sense tags like “running” and “marathon”.

The results of this simple test show the promise of tagging, and where the work lies ahead to create a robust semantic tagging system that could be used for noise control.

*****

See this item on FriendFeed: http://friendfeed.com/search?q=%22Tag+Recommendations+for+Content%3A+Ready+to+Filter+Noise%3F%22&public=1

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