Techniques for scam detection and prevention

1. A computer-implemented method, comprising:

generating a scam message example repository;

submitting the scam message example repository to a natural-language machine learning component, the natural-language machine learning component to generate a scam message model from the example repository using a message content reuse measure, the content reuse measure to compare a user's reuse of a phrase to the reuse of the phrase in a random message sample;

receiving a scam message model from the natural-language machine learning component in response to submitting the scam message example repository;

monitoring a plurality of messaging interactions with a messaging system based on the scam message model;

determining a suspected scam messaging interaction of the plurality of messaging interactions; and

performing a suspected scam messaging action with the messaging system in response to determining the suspected scam messaging interaction.

2. The method of claim 1 , further comprising:

determining a recognition measure for the suspected scam messaging interaction based on the scam message model; and

selecting the suspected scam messaging action from a plurality of suspected scam messaging actions based on the recognition measure.

3. The method of claim 2 , the plurality of suspected scam messaging actions comprising two or more of a shadow ban action, an explicit ban action, a scam education action, a scam reporting tool promotion action, and a human review flagging action.

4. The method of claim 1 , further comprising:

monitoring a second plurality of messaging interactions with the messaging system;

determining a plurality of suspicious messaging interactions based on a message content reuse measure;

flagging the plurality of suspicious messaging interactions for review;

receiving a plurality of confirmed scam messaging interactions of the plurality of suspicious messaging interactions; and

including the plurality of confirmed scam messaging interactions in the scam message example repository.

5. The method of claim 1 , further comprising:

collecting a sample of messaging interactions with the messaging system; and

including the sample of messaging interactions in the scam message example repository as example non-scan messages.

6. The method of claim 5 , further comprising:

anonymizing the sample of messaging interactions for inclusion in the scam message example repository.

7. The method of claim 1 , further comprising:

augmenting the sample of messaging interactions based on one or more of user scam reporting, administrator scam flagging, and regular-expression scam-flagging rules.

8. An apparatus, comprising:

an interaction processing component operative to generate a scam message example repository; submit the scam message example repository to a natural-language machine learning component, the natural-language machine learning component to generate a scam message model from the example repository using a message content reuse measure, the content reuse measure to compare a user's reuse of a phrase to the reuse of the phrase in a random message sample; and receive a scam message model from the natural-language machine learning component in response to submitting the scam message example repository;

an interaction monitoring component operative to monitor a plurality of messaging interactions with a messaging system based on the scam message model; and determine a suspected scam messaging interaction of the plurality of messaging interactions; and

a scam action component operative to perform a suspected scam messaging action with the messaging system in response to determining the suspected scam messaging interaction.

9. The apparatus of claim 8 , further comprising:

the interaction monitoring component operative to determine a recognition measure for the suspected scam messaging interaction based on the scam message model; and

the scam action component operative to select the suspected scam messaging action from a plurality of suspected scam messaging actions based on the recognition measure.

10. The apparatus of claim 9 , the plurality of suspected scam messaging actions comprising two or more of a shadow ban action, an explicit ban action, a scam education action, a scam reporting tool promotion action, and a human review flagging action.

11. The apparatus of claim 8 , further comprising:

a message reuse monitoring component operative to monitor a second plurality of messaging interactions with the messaging system; determine a plurality of suspicious messaging interactions based on a message content reuse measure; and flag the plurality of suspicious messaging interactions for review; and

the interaction processing component operative to receive a plurality of confirmed scam messaging interactions of the plurality of suspicious messaging interactions; and include the plurality of confirmed scam messaging interactions in the scam message example repository.

12. The apparatus of claim 8 , further comprising:

a message sampling component operative to collect a sample of messaging interactions with the messaging system; and

the interaction processing component operative to include the sample of messaging interactions in the scam message example repository as example non-scan messages.

13. The apparatus of claim 12 , further comprising:

the message sampling component operative to anonymize the sample of messaging interactions for inclusion in the scam message example repository.

14. The apparatus of claim 8 , further comprising:

the interaction processing component operative to augment the sample of messaging interactions based on one or more of user scam reporting, administrator scam flagging, and regular-expression scam-flagging rules.

15. At least one non-transitory computer-readable storage medium comprising instructions that, when executed, cause a system to:

generate a scam message example repository;

submit the scam message example repository to a natural-language machine learning component, the natural-language machine learning component to generate a scam message model from the example repository using a message content reuse measure, the content reuse measure to compare a user's reuse of a phrase to the reuse of the phrase in a random message sample;

receive a scam message model from the natural-language machine learning component in response to submitting the scam message example repository;

monitor a plurality of messaging interactions with a messaging system based on the scam message model;

determine a suspected scam messaging interaction of the plurality of messaging interactions; and

perform a suspected scam messaging action with the messaging system in response to determining the suspected scam messaging interaction.

16. The non-transitory computer-readable storage medium of claim 15 , comprising further instructions that, when executed, cause a system to:

determine a recognition measure for the suspected scam messaging interaction based on the scam message model; and

select the suspected scam messaging action from a plurality of suspected scam messaging actions based on the recognition measure.

17. The non-transitory computer-readable storage medium of claim 16 , the plurality of suspected scam messaging actions comprising two or more of a shadow ban action, an explicit ban action, a scam education action, a scam reporting tool promotion action, and a human review flagging action.

18. The non-transitory computer-readable storage medium of claim 15 , comprising further instructions that, when executed, cause a system to:

monitor a second plurality of messaging interactions with the messaging system;

determine a plurality of suspicious messaging interactions based on a message content reuse measure;

flag the plurality of suspicious messaging interactions for review;

receive a plurality of confirmed scam messaging interactions of the plurality of suspicious messaging interactions; and

include the plurality of confirmed scam messaging interactions in the scam message example repository.

19. The non-transitory computer-readable storage medium of claim 15 , comprising further instructions that, when executed, cause a system to:

collect a sample of messaging interactions with the messaging system;

anonymize the sample of messaging interactions for inclusion in the scam message example repository; and

include the sample of messaging interactions in the scam message example repository as example non-scan messages.

20. The non-transitory computer-readable storage medium of claim 15 , comprising further instructions that, when executed, cause a system to:

augment the sample of messaging interactions based on one or more of user scam reporting, administrator scam flagging, and regular-expression scam-flagging rules.