RIP Spammy Twitter Marketers (#RIPTwitter)

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A Twitter meme — #RIPTwitter — took the social network by storm over the weekend. Users complained about a rumored change from a traditional Twitter user feed to an algorithmic-sourced feed next week. The angst was inspired by this BuzzFeed post reporting the change.

The algorithm would source the most popular stories in people’s Twitter feeds. Users believed the experience would be bad enough to kill the network. The meme was so overpowering it caused founder and current CEO Jack Dorsey to make a statement and allay concerns:

But in reality, would an algorithm really kill Twitter? I don’t think so. It would probably make the experience better by eliminating bad spammy link-based Tweets usually sourced by marketers and inane ranters.

Tweets that aren’t interesting, including the overwhelming majority of tweets marketers push out every business day from 9 a.m. to 5 p.m., would lose priority. Without engagement, most of those tweets would fail to trigger the algorithm. They would die in the machine.

Conversely, the tweets that get the most engagement in a stream would rise to the top. I think this would be a fantastic development that would make Twitter’s stream much more competitive with Facebook, LinkedIn and to a lesser extent Google+. And it would force brands to invest in real conversations instead of simply publishing.

Further, based on Jack’s tweet, afterwards power users can simply pull down their screen or refresh their feed to get the traditional timeline. So no, Twitter algorithms won’t kill the social network. But based on the incredible amount of spammy marketing junk and bad content on the social network — even those based on popular topics and hashtags — well, an algorithm can only improve the experience.

Letting Go of 2400 Followers

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Every time I blog about Twitter losing its mojo, I receive several comments about how that’s my fault. Specifically, that I followed the people who post spam, so shame on me.

After my last post about how Twitter can improve its experience, I decided to listen to them and unfollowed 2400 Twitter users. This isn’t one of these, “I unfollowed all of you posts” that bloggers drop for attention. I actually did most of this at the beginning of last month so if I was seeking to draw attention, that would have been the time. Plus I would have dumped another 1500 out of my remaining 2000 followers.

No, this was an experiment to get rid of what Malcolm Gladwell would call weak ties on my social network. Specifically, I cut people I did not know or just had a brief acquaintance with and who are also marketers. I also unfollowed people who simply use Twitter to drop links, marketing or not).

What happened?

My experience definitely improved, not enough to make Twitter thrilling again, but the stream did seem to liven up a bit. I began engaging more, too.

The funny thing was that I did not receive one peep about the mass unfollowing either, which substantiates my belief that these people weren’t vested in being engaged in a conversation with me, at least on Twitter. About 200 people have auto unfollow bots or noticed, and unfollowed me back. The rest stuck around for whatever reason.

I may go further and drop some more followers when I get a chance. Whenever I am in the network and I see someone just dropping links or posting ridiculous spam, I unfollow them then and there. It’s adding up to a better Twitter that I actually care about again.

What do you think?

Dangers of Algorithmic Sourcing

The increasing pervasiveness of algorithms in everyday life disturbs me.

At the behest of many friends, I finally joined the 500 Pixels community and have begun uploading some of my better photos there for licensing. It’s an awesome place filled with pro photographers competing for the highest scores on their photos.

Yet, scores are determined by the amount of likes, favs and comments you get over a short period of time.

For all intents and purposes, you have a homogenous community of primarily male photographers who are either very good enthusiasts or professionals voting on photos. What gets top ranked? The general popular stream is dominated by surreal landscapes and pics of almost nude models with the occasional wildlife pic thrown in for flavor.

500 Pixels Popular

If you want a top rank of 99 on 500 Pixels, bring epic photoshopped scenes and beautiful scantily clad women. These are amazed photos, and deserved their popular ranking. But you can look at the categories to dig deeper. Some of the lesser ranking photos strike me as a better representation of the many things you can do with a lens (and Photoshop).

Here’s the thing, I stopped posting anything I don’t think can get a peak rating of 80 or higher on 500 Pixels. I just won’t do it. Because I don’t shoot almost naked women for a variety of reasons starting with respecting my peers and wanting to stay married, I post landscapes. Since I shoot more than just landscapes, for that reason alone the site is limiting in its artistic and creative scope.

Algorithmic Determination

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Image by aloalo.

Algorithms impact our news choices, too. And our clothing choices. And what we read. And the movies we like.

It seems like algorithms are everywhere. Here are just a few examples:

  • Huffington Post and Mashable sourcing their news based on rising social media memes.
  • Colors and types of shirt you are most likely to buy (based on past purchasing history).
  • Books you should buy on Amazon.
  • Movies and TV programs you are most likely to enjoy on Netflix.

Is this healthy?

It depends. If you like the same type of thing over and over again, then perhaps algorithmic determination is OK.

Afterall, if you participate on the same sites and buy from the same vendors, then your general behavior will match your peers. As such the algorithms are likely to be correct most of the time.

Consider that 60 people eat the same seven meals every week.

Yum, pizza.

Crazy People Like Orange

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Image by balotto.

Because I am crazy, five percent of the time I’d like to buy an orange shirt. Yup, it makes my skin look like shit, but I like orange.

Orange was my favorite color as a child. I had orange and green dinosaur wall paper, and one whole wall was painted exclusively orange. I still remember it fondly.

The algorithms don’t know that, but based on what they see online they have predetermined that I will buy black and red and maybe blue. I do like my black T-shirts, but I also like splashes of bright color. And 5% of the time that means I like orange.

What to do?

No Growth

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Image via View from the Blue Ridge.

How do things become popular? Someone has to try them first, and then they tell friends. Soon early adopters flock to the product.

Perhaps it becomes popular within a niche community (More surreal interior architecture shots, please). Enough people in the community participate in other social networks, and not just online. Work, family and neighborhoods count, too. People tell their friends, and show them the the new thing they like.

Suddenly, it is safe to try something new. But maybe it won’t be new. Because an algorithm already saw that seven percent of your friends tried something, and it knows you buy items as an early adopter. The site sources you an ad telling you your friends Manny, Moe and Jack bought it already.

Boom! You react and plunk down your credit card.

What’s so daring about that? Where’s the growth?

Cool to be Weird

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In a world where anything can be customized to a unique taste, niche stores are popping up all over the Internet to serve the terminally weird. Now it’s cool to be weird.

As database technology becomes cheaper and cheaper, niche stores will be able to serve a customer with algorithmic offerings. Even the daring will find themselves served with the predetermined.

And the algorithms will only get smarter.

How smarter more accessible algorithms impact the inevitable break from the norm remains to be seen. Perhaps that same percentage of the population will be able to resist precision marketing in this form. Or maybe we will all simply accept the endless stream of data driven sales pitches, some subtle, some obvious.

It’s a change that will happen whether we like it or not. The train has left the station.

What do you think?

Success Built on a Mountain of Failures

Two weeks ago, Jelly Founder and Twitter Co-Founder Biz Stone spoke at the Greater Washington Board of Trade about his lessons learned as an entrepreneur, as detailed in his new book, Things a Little Bird Told Me. The conversation with Board of Trade President Jim Dinegar inspired hundreds of executives.

“My success is built on a mountain of failures,” said Biz.

Biz continued and said that he attributed 99% of his success to failures and 1% to luck. He looked at failure as a method of experimentation. Failure tells you what doesn’t work, and allows you to move on to a different approach and find an answer.

Opportunity means a set of circumstances that makes it possible to do something, noted Biz Stone. Unfortunately, most people assume that they have to wait for those circumstances. “We can make the circumstances that create opportunity,” said Biz.

Twitter Success Came from Failure

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Biz had an impovershed upbringing. He was raised by his mother who was a single earner. He was busy as a high school student, and ended up starting and participating in his lacrosse team and working at night. He didn’t have time for homework, too, so he actively dialogued with his teachers and worked out an agreement. Later Biz dropped out of college… Twice.

He related how he had moved west to Silicon Valley to work with Ev Williams, and his mild successes and failures with Google’s Blogger platform. During the pre-Twitter success period of his life, Biz was struggling to make ends meet, and he and his wife slept on their floor.

Ev left Blogger, and Biz became the leader of the unit. Google then IPOed, which help relieve Biz’s financial woes. Biz decided to leave Google after the IPO because he wasn’t happy with the experience, even though he was the voice of the Blogger platform. He didn’t love what he was doing, and he had specifically come to California to work with Ev. So even though the Google IPO promised more wealth, he joined Ev and built the podcasting software company Odeo.

Odeo’s failure produced the concept for Twitter. Rather than simply close the doors, Ev and Biz held a hackathon to come up with cool ideas. By then Biz and Jack Dorsey, a programmer at Odeo, were becoming good friends. They hacked the idea for Twitter based on AOL’s Instant Messenger platform.

During the company’s initial successes, Twitter experienced severe technical issues, and the service kept collapsing. It was the era of the Twitter fail whale. Everyone was strained, and one day Biz — who again was the face of the company — came in and snapped. He yelled at the team.

Jack got up and asked Biz to talk privately. They went for a walk and Jack told Biz that he couldn’t behave that way. “I realized I was the leader of the company,” said Biz. “I always needed to present a positive outlook for the team.”

More stories were shared including a stiff conversation with Facebook Founder Mark Zuckerberg who inquired to buy Twitter.

All in all, I found Biz’s adventures to be very inspiring. I believe that success is something that could happen with hard work and faith. And that belief was reaffirmed. I liked the Jack lesson, too.

Biz did note that it was important for people to give back. He said it doesn’t matter how much money you have, you always need to give to people, even if it is just time or $5. It changes who you are and the benefit is that it make you a better person. Biz is actively involved with DonorsChoose.

A version of this post ran originally on the Vocus blog.