1. A system for providing a language classification of a media item, comprising: one or more processors; and a memory storing instructions that, when executed by the system, cause the system to perform operations for providing a language classification of a media item, the operations implementing: a context classifier to determine a context characteristic indicating one or more users who have interacted with the media item; wherein the context characteristic corresponds to a computed likelihood that the media item is in one or more languages based on determined language abilities of the users who have interacted with the media item; and wherein the context classifier is further to compute, based on the determined context characteristic and corresponding computed likelihood, a context prediction that the media item is in one or more first languages; a trained classifier to compute a trained prediction that the media item is in one or more second languages; wherein computing the trained prediction comprises an n-gram analysis of the media item, for one or more n-grams in the media item having a particular length, which analyzes a specified probability distribution that the n-gram is in a specific language; and a language classifier to combine the context prediction with the trained prediction.
1. A system for providing a language classification of a media item, comprising: one or more processors; and a memory storing instructions that, when executed by the system, cause the system to perform operations for providing a language classification of a media item, the operations implementing: a context classifier to determine a context characteristic indicating one or more users who have interacted with the media item; wherein the context characteristic corresponds to a computed likelihood that the media item is in one or more languages based on determined language abilities of the users who have interacted with the media item; and wherein the context classifier is further to compute, based on the determined context characteristic and corresponding computed likelihood, a context prediction that the media item is in one or more first languages; a trained classifier to compute a trained prediction that the media item is in one or more second languages; wherein computing the trained prediction comprises an n-gram analysis of the media item, for one or more n-grams in the media item having a particular length, which analyzes a specified probability distribution that the n-gram is in a specific language; and a language classifier to combine the context prediction with the trained prediction. 5. The system of claim 1 , wherein the context classifier identifies the one or more users who have interacted with the media item as an author of the media item, and wherein the corresponding computed likelihood is based on a language associated with the author indicating the author is facile with the one or more first languages.