A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: identifying a plurality of hashtags associated with a set of messages in a social networking system; applying one or more exclusionary filters to identify a set of eligible hashtags by excluding hashtags in the plurality of hashtags that meet at least one of the exclusionary filters; training a plurality of hashtag classifiers, each hashtag classifier corresponding to an eligible hashtag in the set of eligible hashtags, the hashtag classifier providing a classifier output indicating whether a message should be associated with the hashtag corresponding to the hashtag classifier; and identifying one or more hashtags for a subject message based on the classifier outputs of the plurality of hashtag classifiers applied to the subject message. 2. The method of claim 1, wherein the one or more exclusionary filters include a trendiness filter that excludes a hashtag from the set of eligible hashtags based on a trendiness value of the hashtag that indicates a recent frequency of the hashtag relative to previous occurrences of the hashtag. 3. The method of claim 2, wherein the trendiness filter excludes the hashtag from the set of eligible hashtags when the trendiness value is below a threshold. 4. The method of claim 2, wherein the recent frequency of the hashtag identifies a number of occurrences of the hashtag within the past day. 5. The method of claim 1, wherein the one or more exclusionary filters include a frequency filter that excludes a hashtag from the set of eligible hashtags based on a hashtag frequency value of the hashtag that indicates the frequency of the hashtag in the set of messages relative to terms associated with the hashtag in the set of messages. 6. The method of claim 5, further comprising identifying the terms associated with the hashtag based on a character string of the hashtag. 7. The method of claim 6, wherein identifying the terms associated with the hashtag is based on an n-gram language model. 8. The method of claim 1, wherein the subject message is received from a composing user, and further comprising suggesting to the composing user that the composing user include the one or more identified hashtags in the subject message. 9. The method of claim 1, further comprising identifying one or more topics for the subject message based on the one or more identified hashtags for the subject message. 10. The method of claim 1, further comprising identifying one or more social networking objects for the subject message based on the one or more identified hashtags for the subject message. 11. A non-transitory computer-readable medium comprising instructions executable by a processor that cause the processor to perform steps of: identifying a plurality of hashtags associated with a set of messages in a social networking system; applying one or more exclusionary filters to identify a set of eligible hashtags by excluding hashtags in the plurality of hashtags that meet at least one of the exclusionary filters; training a plurality of hashtag classifiers, each hashtag classifier corresponding to an eligible hashtag in the set of eligible hashtags, the hashtag classifier providing a classifier output indicating whether a message should be associated with the hashtag corresponding to the hashtag classifier; and identifying one or more hashtags for a subject message based on the classifier outputs of the plurality of hashtag classifiers applied to the subject message. 11. The non-transitory computer-readable medium of claim 11, wherein the one or more exclusionary filters include a trendiness filter that excludes a hashtag from the set of eligible hashtags based on a trendiness value of the hashtag that indicates a recent frequency of the hashtag relative to previous occurrences of the hashtag. 13. The non-transitory computer-readable medium of claim 12, wherein the trendiness filter excludes the hashtag from the set of eligible hashtags when the trendiness value is below a threshold. 14. The non-transitory computer-readable medium of claim 12, wherein the recent frequency of the hashtag identifies a number of occurrences of the hashtag within the past day. 15. The non-transitory computer-readable medium of claim 11, wherein the one or more exclusionary filters include a frequency filter that excludes a hashtag from the set of eligible hashtags based on a hashtag frequency value of the hashtag that indicates the frequency of the hashtag in the set of messages relative to terms associated with the hashtag in the set of messages. 16. The non-transitory computer-readable medium of claim 15, the steps further comprising identifying the terms associated with the hashtag based on a character string of the hashtag. 17. The non-transitory computer-readable medium of claim 16, wherein identifying the terms associated with the hashtag is based on an n-gram language model. 18. The non-transitory computer-readable medium of claim 11, wherein the subject message is received from a composing user, and further comprising suggesting to the composing user that the composing user include the one or more identified hashtags in the subject message. 19. The non-transitory computer-readable medium of claim 11, further comprising identifying one or more topics for the subject message based on the one or more identified hashtags for the subject message. 20. The non-transitory computer-readable medium of claim 1, further comprising identifying one or more social networking objects for the subject message based on the one or more identified hashtags for the subject message.