The man behind our industry’s most accurate filters

We talk weapons, water heaters, challenges of weeding out false positives and how to create accurate filters with Besedo filter manager, Kevin Martinez.

Interviewer: Great to meet you, could you tell us a bit about yourself?
Kevin: I’m Kevin Martinez; originally from Spain, but raised in France, now working out of Besedo’s Malta office. I’ve been with the company for five years. In 2016 I had the honor of setting up Besedo’s first client filter. And we still have the client – so I must have done something right!

Interviewer: Excellent! So, tell us more about what you do.
Kevin: The short answer is ‘I’m a filter manager’. I make sure that our clients’ filters are working as well as they should be – monitoring filter quality across all Besedo assignments.

I manage three filter specialists – two in Colombia and another in Malta. Being from different cultures, speaking different languages, and having a presence in different time zones means we can work with clients across the world.

The longer answer is that I assess decisions that our automated moderation tool Implio has made. Quality checks like these are done at random. I take a sample of content that’s been approved – items that have been filter-rejected and filter-approved – and identify if any mistakes were made. I then learn from these mistakes and make appropriate adjustments to the filter. This way we maintain and improve the accuracy rate of our filters over time.

Quality checks take time, as we’re really thorough. A single one can take half a day! But tracking the quality day-by-day is vital to keeping the filters accurate and it allows us to provide a report with a quality rate for our clients at the end of each month.

Interviewer: That sounds like a complex task… What kind of things are you looking for?
Kevin: Typically, we’re looking for false positives in filters: terms that are correctly filtered according to the criteria set, but aren’t actually prohibited.

Take Italian firearms brand, Beretta, for example. Weapons are prohibited for sale online in some nations, but not in others. So, for many sites a filter rejecting firearms would make sense.

However, there’s another Italian brand called Beretta – but this company manufactures water heaters (!). There’s also a Chevrolet Beretta car, and an American wrestler who goes by Beretta too. The filter can’t distinguish between these as completely different things until we know that they need to be distinguished between. So, lots of research is needed to ensure that, say, a Beretta water heater parts ad isn’t mistakenly rejected from an online marketplace.

A good filter will reduce the time the moderators spend on the content queue and will also reduce the length of time it takes to get a piece of content live on the site. It’s an ongoing process, one that gets better over time: gradually improving automation levels and making the manual moderator’s job a lot easier.

Interviewer: What’s the overall effect of a ‘bad’ filter, then?
Kevin: It depends. If the filter is set up to auto-reject matched words and phrases, it leads to a bad user experience as genuine ads might get rejected (as the case with water heaters illustrated).  If, the filter is set up to send matched content for manual moderation, the automation level decreases. We agree to a certain automation level when we sign a contract with a client, so if there are more items for the manual moderation team to approve; it puts pressure on us to reach our service level agreement.

Interviewer: Which rules are hardest to program into a filter?
Kevin: Scam filters are the most complex to implement; mostly because scams evolve and because scammers are always trying to mimic genuine user behavior. To solve this, we monitor a number of things in order to detect ‘suspicious’ behavior, including email addresses, price discrepancies, specific keywords, IP addresses, payment methods (like PayPal and Western Union) – among other things.

One of the biggest challenges is that on their own, elements like these aren’t suspicious enough to warrant further investigation; so we have to ensure the filter recognizes a combination of them for it to be effective. We perform a lot of research and collaborate closely with clients, to ensure each filter is as accurate as possible.

Interviewer: Sounds like you need a lot of expertise! What does it take to be a good filter manager? 
Kevin: You need to understand how moderation works, and most filter specialists have a good grasp of computer programing (particularly the concept of regular expression) too. But equally you need to have a curious, analytical, and creative mind.

Admittedly, filter quality checks can be a bit repetitive, but they are very important. Being able to investigate, test, and find ways to setup and improve filters is crucial. This means understanding how the filter will interact with words in practice, not just in theory. The most important thing is to have the drive to keep pushing; to find the perfect solution for the client’s needs.

Interviewer: What do you enjoy the most about your work?
Kevin: I love the beginning of every new project. I help onboard each new client from the very start, setting up the filters and creating a report for them. Each one is different, so lots of investigation is involved as there are different rules to consider: depending on who the client is, what they do, and where they’re based.

As mentioned, rules can differ between countries. For instance, in South America, you don’t need to apply a gender discrimination filter for something like jobs or housing – unthinkable in Europe, which has stringent equality laws.

Each day I look at the quality of the client data by opening a random filter, reviewing at the ads going through that filter and seeing everything’s working correctly. There are many parameters involved, and it involves going over the finer details, but this is the stuff I’m passionate about. I can be quite obsessional about it!

Nothing is impossible. I aim to get the client what they want and will try again and again and find a creative way to deliver it!

Kevin Martinez

Kevin is a filter manager at Besedo. He combines creativity, perseverance and in-dept research to create highly accurate filters in the all-in-one moderation tool; Implio.

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