Social Media Outcry Brings Competition to the Table

The negative comments to this post are already coming.  I can feel it.  Why?  Because I am about to be completely ambiguous and talk theoretically, even though I have knowledge that the things I’m about to discuss are facts.

Ever since Digg, Slashdot, and the other early social news sites took hold of a new market on the internet, there have been clones and competitors popping up left and right.  The next “Digg Killer” has emerged and subsequently fallen almost every week for the last couple of years.  Newer networks have been proposed, many are getting worked on, and I know a lot about some, little about others.

All I can say is, a couple of them are so innovative, so promising, they just might make it.  Sadly, I can’t say much more than that at this stage.

Propeller has updates coming that are rumored to be able to give Digg a run for its money.  We’ve heard that before out of their camp, but reports from those who have seen the changes indicate that this may be the real deal.  Propeller has been on the edge of greatness since shortly after its inception.  The skeptics who believed that losing the type-in traffic from Netscape would spell doom for the social media site have been proven wrong.  Still, it hasn’t made the huge impact that it should have.  The changes coming up give it that last chance boost that it will take to finally “make it”.

Over the next 6 months, there will be several more new sites popping up.  What’s different now is the mindset of these new companies.  Before, everyone was making Digg Clones or innovations on the Digg theme.  The proposals I’ve seen for the newest batch of sites is different — they want to make sites that take the best parts of social media, turn them on their head, and present something that is unique.  Something new and different.

They all seem to be heading towards the goal of launching Web 3.0.

What makes now different from before?  The motivation now is to make something that is better.  More people are getting into the game, not because they want a piece of the pie, but because the pie the way it sits is getting old and stale.  People want something better, something fresh.  Social media users want something better.  In general, all of the social media sites have a stronger public outcry for a better site than ever before.  That is why the new players are primed to succeed where others who have tried to get in have failed.

Will they succeed?  Most of you don’t know because I can’t put out the details yet.  You have no reason to trust me when I say that a couple will most definitely work.  All I can say for sure that most will agree with is that sites like Digg, Reddit, and Mixx have strong points, but there are weak points that prevent them from being truly great.  The new sites, as they emerge, will be great.  Not all of them, of course, as I’ve seen some doozies of poorly-conceived ideas, but there are a few gems that will stand out from the crowd and pick up the traction they need to become real players.

Don’t hate me for being vague.  Just trust me when I say that the next big thing, the “Digg Killer,” is real and is not far in the future.  As soon as I can report more details, I will.  In the meantime, start throwing your rotten tomatoes at me for being an absolute tease.

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Comments

  1. says

    No rotten tomatoes – I’m very disillusioned with social media as it exists today and would love to see something that couldn’t be gamed and really would present superior content.

  2. says

    Hi, I would like to pitch you my social news website, fyynd.com. I wrote this website in two months based on algorithms from the book Programming Collective Intelligence. Please tell me what you think.

    It has two main features: the ability to identify related/similar links and suggestions/recommendations that actually work.

    The basis for all of the algorithms is a document similarity metric presented in Chapter 3: Discovering Groups. Basically, to compare document A with document B, we calculate the Pearson correlation coefficient between the word frequencies of document A and the word counts of document B. (You can imagine this as plotting a series of points of a graph: each point’s x coordinate is its frequency in document A and each point’s Y coordinate is its frequency in document B. The Pearson correlation coefficient is a measure of how well the line-of-best-fit fits the points.)

    Using this similarity metric, links can be clustered together using K-means clustering. This is what you get when you click on “related” at the bottom of each link. Clicking on “similar” gives the results of running K-NN. (“related” doesn’t work as well as it could be right now because there are too few links for a link to be similar with, but this is an example of where it does work: http://fyynd.com/links/197/related/ “similar” usually works better right now.)

    There are two algorithms for giving recommendations, “Suggested” and “Recommended”. “Recommended” generally works better than Suggested when you haven’t yet made many votes but Suggested should be more in tune to your preferences in the long run.

    In layman’s terms, the Recommendation algorithm works by “averaging” together the links that you liked and then find links that are similar to that while the Suggestion algorithm tries to determine whether you will like a particular link by seeing whether it is similar to any page that you have already rated highly. As a result, “Recommended” will list pages in your general interest area, but insensitive to any “niche” interest that you might have. The “Suggested” page will be sensitive to “niche” interests but will requires more votes to train. For example, if most of the link you rate highly are about computer science, with a only a few links about biology, when the recommendation algorithm averages them together, the biology links would count for very little. As a result, you wouldn’t see much on biology. On the other hand, the suggestion algorithm will not be hindered by this, though it will have trouble if you don’t vote much.

    By now, I hope to have convinced you that I know what I am doing and that my algorithms do work!

    Please note that because predictions are so computationally intensive, they are not updated in real-time but on a hourly basis. Thus, you have to wait a bit before they come out. Please be patient!

    Please check it out and tell me what you think! Any questions/comments/suggestions are more than welcome! You can email me at comments@fyynd.com

  3. says

    whoops, I didn’t know that my last comment was so long. I would have emailed you instead had I found your email address. I would totally understand if you deleted it.

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