6. A method for building an n-gram classifier trained for analysis of social media content items, comprising: storing training media items gathered from a social media source, wherein at least some of the training media items are associated with a language identifier indicating a language the media item is in, wherein each language identifier that indicates the language the media item is in is assigned based on one or more of: a language model, associated with a user who created the media item, indicating that the user who created the media item is mono-linguistic; or a common language identified by both a first language model associated with the user who created the media item and by a second language model associated with a user who received the media item; and generating, for each of multiple selected n-grams from the training media items, a corresponding probability distribution that a particular media item is in a particular language given that the n-gram is in the particular media item; wherein at least one probability distribution corresponding to one of the multiple selected n-grams is based on an analysis of a frequency with which that n-gram occurs in a subset of the training media items having the same language identifier.
6. A method for building an n-gram classifier trained for analysis of social media content items, comprising: storing training media items gathered from a social media source, wherein at least some of the training media items are associated with a language identifier indicating a language the media item is in, wherein each language identifier that indicates the language the media item is in is assigned based on one or more of: a language model, associated with a user who created the media item, indicating that the user who created the media item is mono-linguistic; or a common language identified by both a first language model associated with the user who created the media item and by a second language model associated with a user who received the media item; and generating, for each of multiple selected n-grams from the training media items, a corresponding probability distribution that a particular media item is in a particular language given that the n-gram is in the particular media item; wherein at least one probability distribution corresponding to one of the multiple selected n-grams is based on an analysis of a frequency with which that n-gram occurs in a subset of the training media items having the same language identifier. 8. The method of claim 6 , wherein the multiple selected n-grams comprise all possible character sequences of a particular length in the training data.