Seriously. @twittersuggests is a White Hot Bucket of Fail. Er, Win. Gah! I Don’t Even Know Anymore

Posted on August 31, 2011 by manima

I first noticed the new experimental @twittersuggests feature a couple months ago when it @mentioned me in a tweet to a newly registered Twitter user. At the time I thought this was a cool way for the company to actively use their own product to help solve a discovery problem for new users to the service. My Twitter account was included in a series of tweets that mentioned other notable accounts (@superamit, @juliebenz, and @sacca), so the secondary reaction was a positive emotional one — I was flattered.

Twitter describes the service on its help pages as:

…an experimental feature that helps you find interesting new accounts to follow by tweeting Who To Follow suggestions, personalized just for you! This feature was created by Twitter, and it looks like a normal Twitter account – it will Tweet recommendations which you can reply to, retweet or mark as favorites.

Pretty cool, right?

Since then not every mention has been as flattering (obviously, the purpose of this service isn’t to dole out flattery to nobodies like myself), but for the most part they have been decent overall. Over time, the quality of the mentions declined. Today tipped the scale. In a tweet posted earlier I was @mentioned alongside what can only be described as a spam account. Nay, a porn spam account. See for yourself:

So, I may be guilty for tweeting a lot. I may also be guilty for running my mouth off from time to time. But how in the world am I in the same class as a porn spam account? Better yet, how can this possibly be acceptable from an official Twitter account?

How does it work?

@twittersuggests is a feature which looks like a Twitter account – it algorithmically generates suggestions of users to follow and sends them to you.

@twittersuggests will tweet recommendations to you via @mentions, and this Tweet will appear in your @mentions timeline.

Sure, the company describes this with words like “algorithmically” and “experimental,” but it’s really hard to believe that this was launched with any sort of testing whatsoever. If there are any resources applied to this experiment, they certainly don’t appear to be doing any tuning that is having a positive impact. To the contrary, the quality appears to be decreasing over time. The sad thing is, if I were new to Twitter I might find a service like this valuable if the accounts recommended remained of decent quality, but that’s just not the case here. Worse still is that there are so many simple ways this could be avoided.

Before I get pummeled with the argument the “false positives are expensive” argument (Yes, I’ve read @kellan’s excellent write-up, and have firsthand experience with this as well) let me call out that this is an entirely different scenario. The cost of false positives is only applicable when you choose to deny accounts access to basic services. If a company restricts an account from using the basic functionality of a site because of an unsubstantiated suspicion, then sure…that’s expensive.

However, tweeting account recommendations that might otherwise trip overly sensitive spam-detecting algorithms is a ~~choice~~ mistake. Twitter owns this account, they have the right to be overly choosy about the accounts featured in their recommendations, and an account that includes obvious keywords like “sex” and “porn” is a safe one to filter out of that list, just to play it safe. Now, building a recommendations engine is tough. It’s not easy to get these things right, and I’m certainly sympathetic to this. I guess I’m reacting so strongly here because this feels like one of those avoidable mistakes, especially because there is literally no harm in restricting an account like this from being recommended.

In other news…

Speaking of mouthing off…I shared my thoughts on the news of the Beyonce-pregnancy-VMA induced milestone Twitter reached in terms of TPS (FYI — that’s, obnoxiously, “tweets per second”) this weekend, and look what happened. Awesomesauce.

The Vancouver Canucks 7th Man? Their Twitter Fanbase.

Posted on May 22, 2011 by manima

It’s been said that the Sharks’ 7th man on the ice has been their fan base. Anyone in attendance at the Shark Tank during a crucial game can attest, the aural onslaught can be quite intimidating.

Vancouver has a 7th man as well, only it’s not as rooted at a single location as the Sharks is. It’s mobile, and it uses social media to organize. Oh, and did I mention that it is fully endorsed by Vancouver’s official Twitter account?

Here’s how it works, Vancouver fans organize themselves at tweetups where they watch the game and tweet using various hashtags like #nowayjose and #windaturd. Anecdotally, I’ve noted that during the first two post-season series the Sharks fans have been pretty noisy on Twitter using hashtags like #gosharks and #sjsharks. These have been entertaining to watch, and if you set up a search query that includes the opposing team’s preferred hashtags it provides a nice stream of back channeled discussion during games.

This isn’t the case with Canucks hashtags. When added to San Jose search queries these tweets absolutely flood the streams and snuff out any Sharks tweets posted.

That’s right, the Canucks 7th man is their fanbase on Twitter.

Add to this the insult that was the silent Shark Tank during Game 4 and Vancouver’s tendency to dominate the third period and suddenly the Canuck hashtags take on a whole new light.

So what say you @sanjosesharks? Why not organize fans around a group of hashtags? Encourage viewing parties in San Jose, and motivate fans to do their part?

Thoughts on Jessi Slaughter and Online Bullying

Posted on July 21, 2010 by manima

I’m not sure what to make of this meme, in part because the entire thing confounds me. For those of you scratching your heads, Know Your Meme has a pretty decent blow-by-blow.

Tweens posting tell all video diaries on YouTube is nothing new. It’s unfortunate that Jessi has been exposed to some of the things she discusses at such a young age (she’s 11), but that has little to do with the meme itself. However, it does relate to her ability to behave in this manner without full awareness of what she was exposing herself to. Specifically, her parents had absolutely no idea what she was up to online, and regardless of their individual Internet prowess (or ignorance), they seemingly ignored some red flags that should’ve alerted them to dig a little deeper.

First, the fact that she was spending so much time on a web cam that’s connected to the Internet. Now, I don’t expect them to fully understand the culture and inner workings of the Internet, it’s not their thing and I get that. But, if your 11 year old daughter is spending time on a camera attached to something you don’t fully understand, isn’t that a clue that you might want to engage with her? Yes, it can be difficult as someone approaches the teenage years, but still…Some of this could have gone a long way.

Let’s face it, her parents aren’t solely to blame. Everyone involved carries some of that weight. /b/ is certainly responsible for a good portion of the outcome here.

But none of this is what confounds me. What confounds me is that we are all somehow involved in the perpetual meme play that drives this sort of thing in one way or another. I’m not saying this directly drove the bullying and harassment of Jessi Slaughter, but the need for “watercooler” conversations in the forms of links to memes that can be shared to friends does help push this sort of thing along.

My initial reaction to /b/’s involvement here was, “Well, what do you expect from /b/?” But that’s not right. In fact, it’s dismissive in a way that allows this type of behavior to persist. Granted, not all memes are harmful. But an anonymized crowd’s decision to punish an 11 year old girl, regardless of what she’s said or done online, is a frightening concept, and it’s something that we as a culture need to recognize and address.