Training data quality for spam classification

A method comprising:

• by a computing device, accessing a training data comprising a plurality of posts in a social-networking system that have been flagged by users of the social-networking system as being spam;
• by the computing device, training a first machine-learning model using the training data;
• by the computing device, filtering the training data to identify a first subset of the plurality of posts as spam posts and a second subset of the plurality of posts as non-spam posts;
• and
• by the computing device, training a second machine-learning model using the filtered training data, wherein:
• the first machine-learning model is trained to predict whether a particular post is likely to be flagged by the users of the social-networking system as being spam;
• and
• the second machine-learning model is trained to confirm whether the particular post is spam or not spam.

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• access a training data comprising a plurality of posts in a social-networking system that have been flagged by users of the social-networking system as being spam;
• train a first machine-learning model using the training data;
• filter the training data to identify a first subset of the plurality of posts as spam posts and a second subset of the plurality of posts as non-spam posts;
• and
• train a second machine-learning model using the filtered training data, wherein:
• the first machine-learning model is trained to predict whether a particular post is likely to be flagged by the users of the social-networking system as being spam;
• and
• the second machine-learning model is trained to confirm whether the particular post is spam or not spam.

A system comprising:

• one or more processors;
• and
• one or more computer-readable non-transitory storage media coupled to one or more of the processors and comprising instructions operable when executed by one or more of the processors to cause the system to:
• access a training data comprising a plurality of posts in a social-networking system that have been flagged by users of the social-networking system as being spam;
• train a first machine-learning model using the training data;
• filter the training data to identify a first subset of the plurality of posts as spam posts and a second subset of the plurality of posts as non-spam posts;
• and
• train a second machine-learning model using the filtered training data, wherein:
• the first machine-learning model is trained to predict whether a particular post is likely to be flagged by the users of the social-networking system as being spam;
• and
• the second machine-learning model is trained to confirm whether the particular post is spam or not spam.