US 9,959,503 B2Granted

Filtering automated selection of hashtags for computer modeling

AssigneeFacebook, Inc.

InventorsDavid Vickrey, Jeffrey William Pasternack

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Abstract

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.

Claims20

Dependent claims

A method comprising:

identifying a plurality of hashtags associated with a set of messages in an online system, the set of messages including a message having a textual string including a first hashtag and a second hashtag of the plurality of hashtags;
selecting a set of hashtags for which to train a plurality of hashtag classifiers by using a filter, wherein the filter excludes at least the first hashtag and includes at least the second hashtag;
training the plurality of hashtag classifiers for the selected set of hashtags, each hashtag classifier corresponding to a hashtag in the selected set of hashtags and trained with messages in the set of messages that include the corresponding hashtag, each hashtag classifier providing a classifier output indicating whether a message should be associated with the hashtag corresponding to the hashtag classifier;
identifying one or more hashtags for a subject message of a user of the online system based on the classifier outputs of the plurality of hashtag classifiers applied to the subject message, wherein the one or more hashtags indicate a topic of the subject message based on text included in the subject message;
selecting a candidate story likely to be of interest to the user using the topic indicated by the one or more hashtags;
and
sending the candidate story from the online system to a client device for presentation to the user.

4 more elements

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 an online system, the set of messages including a message having a textual string including a first hashtag and a second hashtag of the plurality of hashtags;
selecting a set of hashtags for which to train a plurality of hashtag classifiers by using a filter, wherein the filter excludes at least the first hashtag and includes at least the second hashtag;
training the plurality of hashtag classifiers for the selected set of hashtags, each hashtag classifier corresponding to a hashtag in the selected set of hashtags and trained with messages in the set of messages that include the corresponding hashtag, each hashtag classifier providing a classifier output indicating whether a message should be associated with the hashtag corresponding to the hashtag classifier;
identifying one or more hashtags for a subject message of a user of the online system based on the classifier outputs of the plurality of hashtag classifiers applied to the subject message, wherein the one or more hashtags indicate a topic of the subject message based on text included in the subject message;
selecting a candidate story likely to be of interest to the user using the topic indicated by the one or more hashtags;
and
sending the candidate story from the online system to a client device for presentation to the user.

4 more elements

A method comprising:

identifying a set of messages, each message of the set of messages including at least one hashtag;
identifying a plurality of candidate hashtags each corresponding to a unique hashtag included in at least one message of the set of messages;
selecting a set of hashtags for training hashtag classifiers by filtering the plurality of candidate hashtags;
for each hashtag in the selected set of hashtags, training a hashtag classifier for the hashtag based on messages of the set of messages that include the hashtag, the hashtag classifier providing a classifier output indicating whether a message should be associated with the hashtag;
identifying one or more hashtags for a subject message of a user of an online system by applying the subject message to the hashtag classifiers for the selected set of hashtags and based on the classifier outputs of the hashtag classifiers for the subject message, wherein the one or more hashtags indicate a topic of the subject message based on text included in the subject message;
selecting a candidate story likely to be of interest to the user using the topic indicated by the one or more hashtags;
and
sending the candidate story from the online system to a client device for presentation to the user.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 14/587,605Filed 31 Dec 2014
Published: US 2016/0189040 A130 Jun 2016
Granted: 1 May 2018
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

H04L 51/52
User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail: for…
G06F 40/284
Recognition of textual entities: lexical analysis, e.g. tokenisation or collocates
G06N 5/04
Computing arrangements using knowledge-based models: inference or reasoning models
G06Q 10/42
Business processes related to social networking or social networking services: determination of affinities or common interests between users