Nobody likes a troll, yet the web has proved a particularly fertile ground for them to set forth on their mission to annoy and upset people from behind the comfort of their computer screen. Of course, this isn’t confined to public social networks, with it increasingly occurring on enterprise social networks as well, albeit in a slightly more subtle form.
With Twitter launching on the stock market recently however, it seems understandable that attention will focus on them, for the time being at least. So a new platform that aims to stop trolling in its tracks should be of interest to us all.
The service, developed by SMC4 aims to stop the harmful messages being delivered to their intended target by using a smart algorithm to halt them in their tracks.
Users connect up their social media accounts to the service, before then identifying any harmful phrases they wish to have filtered out. Each message sent their way is then put through the filter to test it for indications of abuse, racism, swearing and so on. You can set up different responses for different types of messages, so for instance racism can be handled differently to a complaint.
The system also works the other way, so if an employee tries to send out something containing bad content, the app can detect this and put it into a holding queue to be checked by a supervisor before it’s let out into the ether.
Suffice to say, whilst the troll hunting application of this service could be quite useful, especially if you’re a public figure that has better things to do than wade through the garbage that must be an all too common factor of social media life, I do wonder how beneficial it might be to corporate users.
The temptation must exist to filter out negative comments and create a social media echo chamber where all you hear is good things. Lets be under no illusions here, this app isn’t going to delete the message from Twitter, it will just stop it reaching you. Hopefully brands will use such functionality wisely and not decide to neuter any potentially negative feedback.