Abstract: Techniques for scam detection and prevention are described. In one embodiment, an apparatus may comprise 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; 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. Other embodiments are described and claimed.

Abstract: An online system reviews content for violation of one or more policies of the system. The system may evaluate various content policies to determine how usage of the policy affects user experience and what content is shown to each user. The system can, for example, run an A/B validation for certain policies, such as before launching a new policy. To enable the validation, when content is determined to be violating a policy, it is labeled with the specific policy violated as a shadow tag that is not visible to the user viewing the content. Then, the system may track user interactions with newsfeeds of content that include no policy violating content and newsfeeds that include some policy-violating content, and detect at a policy-by-policy level how each policy affects the newsfeed and user experience.

Abstract: Techniques for scam detection and prevention are described. In one embodiment, an apparatus may comprise 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; 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. Other embodiments are described and claimed.

Abstract: Techniques for scam detection and prevention are described. In one embodiment, an apparatus may comprise 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; 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. Other embodiments are described and claimed.