content-filter-diagram

Public Wifi and the online content filters

Popular chains that offer free Wi-Fi to its patrons have begun implementing content filters. The popular coffee chain Starbucks being the most recent, deploying theirs in 2019. The sole reason for this? Porn. Prevalent usage of porn at all hours of the day, sometimes in broad daylight.

This article is not to discuss why or when deployed. There are many articles that go in to further details about this. No, I want to talk about content filters. The thing that you will most likely interact with next time you use the public Wi-Fi at any popular chain store or restaurant.

What is a content filter?

Let’s start at the beginning. Content filters also are known as parental control software or censorware; do as the name suggests. They filter content before it reaches the user. What content, you may ask. Well anything that the company considers illegal or not productive. This can be anything from porn sites to gamblings sites to even social media sites. 
They do so by using a web database of all known websites. The gist is that every website gets assigned a score as well broken down into separate categories and information. The content filter will then sort out websites by scores along with other criteria, which can be set by the company or parents(if used at home). Every vendor or developer will create their software a different way. Some use algorithms to decide the score or use humans to search as well as catalog websites. Some use keyword matching or even packet inspections. There are various tools and techniques that companies use but for the purposes of this article, let’s stick with public content filtering.

Challenges

With the understanding of how they work, one can see the problem in implementing this. Content filters are not smart! This is to say, there is no context to them. It is up to the company to decide what you should get allowed or denied. Let’s use the current example; porn in public areas. The content filter will decide to ban playboy.com including similar websites. In like manner, it will also include any websites that deal with sex trafficking either as educational material or non-profits. I can see one of two ways of fixing this. The vendor or the company fixes this. Equally important, the content filter gets tuned to better serve the needs of the business.

There are over 1.9 billion websites with more getting created daily. The sheer volume of websites that has to get analyzed, categorized, and documented is a daunting task. Algorithms must be smarter or better tuned. More people need to get hired. Each solution presents its own problems along with its challenges. Better algorithms mean more developers, something few companies can afford to do. Hire more humans and offer opportunities to see mental care providers that will help with tech burnout as well as all the negative content they must analyze. Which again, few companies can afford to do..

Keeping this is in mind, there are plenty of websites out there. 1.9 billion. In 2015 India attempted to ban all porn sites. This resulted in them banning 857 websites. This was quickly reversed, but the sheer number of websites that were blocked is immense. Now imagine, all the number of porn and similar websites. Remember Playboy.com, they used to be a nude magazine but have transitioned to a non-nude magazine style. Would they get classified?

Conclusion

In the long run, this is an improvement over what the current solution is. Despite all the negative aspects of one, it is better than what they are using. A content filter is a deterrent. Which is nothing. Make it a little bit harder for people to watch porn (or other dangerous activity), and they should, in theory, give up. Content filters do not stop people determined to get what they want. It is a great implementation and I am curious as to how these popular chains will use them.