Fifth Transparency Report

One of our key objectives is to bring people together. To make sure that we can do this, it is important to prevent spammers and fakers from getting contact to real users.

In this transparency report – the fifth already – we explain what changed in the anti-spam systems over the last quarter and which observations we made.

What has changed since the last anti-spam report?

In the last quarter we focused mainly on hardening our systems rather than adding new features. We started the development of a completely new component which will make it even harder for spammers to reuse their profiles for future attacks.

spammers_per_dau_2017-07-01_2017-09-31.pngIn comparison to the last quarter the amount of spammers has remained stable. In mid-August we had a temporary delay in marking Spammer profiles automatically which is why we see a drop in Spammers per DAU for a very small timeframe. However, all profiles have been marked correctly immediately after the issue was resolved.

Quarter Spam Users
Q3/2016 0.3%
Q4/2016 0.2%
Q1/2017 0.2%
Q2/2017 0.3%
Q3/2017 0.3%

With these tools we managed to keep the amount of spammers in the entire user base at 0.3%. Although the number is somewhat higher than in the previous quarter, it does not mean that there were more spammers in the app. Since we improved the system we have simply been able to recognize spammers which we couldn’t detect before.

Spammer activity

Although there are only a few spam profiles, they are active above average and generate a fair amount of likes, i.e. positive votes in the Match.

Quarter Spam votes
Q3/2016 3%
Q4/2016 4%
Q1/2017 3%
Q2/2017 4%
Q3/2017 4%

The percentage of spam votes compared to the total votes is 4,47 %. This is a slightly larger figure than in the previous quarter. This is due to a higher amount of spam between August and September. Following September, the amount of spam votes per total votes decreases again.

votes_per_dau_2017-07-01_2017-09-31.png

Users meet fewer spammers

Spammers can be reported by users as well as identified automatically by the anti-spam system. Our goal is to block as many spammers as possible automatically, before the critical number of reports is reached or the support staff have to intervene manually.

Quarter AntiSpam
Q3/2016 76%
Q4/2016 90%
Q1/2017 85%
Q2/2017 89%
Q3/2017 87%

In this quarter we succeeded in blocking 87% of all spammers automatically using the anti-spam system. The remaining 13% spammers that got reported by users was also helpful. These reports help us to continuously improve our system. If it is a new type of spam or a completely new behavior of spammers, we include this information in the system. With this we make sure to be able to recognize spammers sooner and we can even retroactively block profiles which showed the same characteristics in the past. Our main goal, however is to get the anti-spam system to find 100% of all spammers so that no user is forced to encounter them.

users_vs_antispam_2017-07-01_2017-09-31.png

What happens to a report?

Quarter Reports Users Of which were no spammers
Q3/2016 1,051,560 819,710 82%
Q4/2016 1.100.966 859.483 81%
Q1/2017 1.317.714 990.074 80%
Q2/2017 1.145.683 883.431 79%
Q3/2017 1.385.517 1.057.697 77%

A question often asked is why a user is not blocked immediately after the first report. Very simple: users report other users for the most diverse reasons. Not all of these reasons justify blocking the reported user. Every day we receive a huge number of reports: In this quarter alone we got 1.385.517 reports that reported a staggering 1.057.697 of different user profiles. However, 77% of these profiles were, in fact, no spammers.

The anti-spam system is this fast on average:

We have to be very careful when blocking users automatically. On the one hand, we want to catch as many spammers as possible, but we must not be too quick to judge. Otherwise, we risk blocking real users who only briefly exhibit spam-like behavior. This can happen, for example, if Match is played very fast. The anti-spam system therefore waits until a user has exhibited negative behavior on several occasions.

Quarter Average duration
Q3/2016 2.2 h
Q4/2016 2.1 h
Q1/2017 1.1 h
Q2/2017 2.4 h
Q3/2017 2.7 h

On average in this quarter, we took 2.7 hours from the first event executed, e.g. a vote in the Match, till the profile got blocked through the anti-spam system. This is slightly longer than in the last quarter. The reason for this is that spammers continue adapting to our system: They act increasingly cautious to stay hidden and remain below the radar for as long as possible. They do so by using likes very sparsely. This means, that sometimes it can take up to several days until enough actions could be observed to identify a profile as a spam profile. The anti-spam system needs to observe negative behaviour several times until it can justify blocking a profile. Of course, a consistently high amount of likes is identified almost immediately and the corresponding user is blocked within a few seconds.

Are spammers more active on male or female accounts?

Quarter Sex

Mean age

Q3/2016 ♀ 84% ♂ 16% ♀ 29 ♂ 29
Q4/2016 ♀ 70% ♂ 30% ♀ 31 ♂ 29
Q1/2017 ♀ 77% ♂ 23% ♀ 29 ♂ 29
Q2/2017 ♀ 80% ♂ 20% ♀ 29 ♂ 39
Q3/2017 ♀ 81% ♂ 19% ♀ 29 ♂ 40

As in the previous quarters, spammers had predominantly female profiles. From all spam profiles a staggering 81.28% were female. On average these profiles were 29 years old. The male spam profiles were on average 10 years older. Why this discrepancy exists is a mystery.

 

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