Venue Link Detection for Social Media Messages
First Claim
1. A method for inferring linkage between social media messages and venues, comprising:
- at a computer system having one or more processors and memory storing one or more programs configured for execution by the one or more processors;
accessing a social network graph comprising nodes representing social media users, nodes representing social media messages generated by the social media users, and nodes representing venues, wherein venues represented in the social network graph comprise a plurality of primary venues and a no-venue, and wherein a link in the social network graph between a social media message node and a node corresponding to the no-venue indicates that the social media message does not correspond to any of the primary venues;
constructing a plurality of training feature vectors, wherein each training feature vector includes a respective plurality of features that use paths through the social network graph to measure connectedness between a respective social media message and a respective venue;
using the training feature vectors to train a classifier to estimate probabilities that social media messages are associated with venues;
receiving a new social media message from a user;
constructing a feature vector for the new social media message, wherein each feature vector includes a plurality of features that use paths through the social network graph to measure connectedness between the new social media message and the no-venue;
executing the trained classifier using the feature vector as input to compute a probability that the new social media message is associated with the no-venue;
when the computed probability is greater than a predefined threshold value, determining that the new social media message is not associated with any of the primary venues; and
when the computed probability is less than or equal to the predefined threshold value, determining that the new social media message is associated with one of the primary venues.
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Abstract
A method associates social media messages with venues. A social network graph includes users, messages from users, and venues. The venues include multiple primary venues and a no-venue. A link between a message and the no-venue node indicates that the message is not associated with a primary venue. Training feature vectors are constructed that measure connectedness between messages and venues. The process trains a classifier to estimate probabilities that messages are associated with venues. A new social media message is received, and the process constructs a feature vector using the same features as the training vectors, measuring connectedness between the new message and the no-venue. The classifier computes a probability that the new message is associated with the no-venue. When the probability exceeds a predefined threshold, the new message is not associated with any of the primary venues. Otherwise, the new message is associated with one of the primary venues.
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Citations
20 Claims
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1. A method for inferring linkage between social media messages and venues, comprising:
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at a computer system having one or more processors and memory storing one or more programs configured for execution by the one or more processors; accessing a social network graph comprising nodes representing social media users, nodes representing social media messages generated by the social media users, and nodes representing venues, wherein venues represented in the social network graph comprise a plurality of primary venues and a no-venue, and wherein a link in the social network graph between a social media message node and a node corresponding to the no-venue indicates that the social media message does not correspond to any of the primary venues; constructing a plurality of training feature vectors, wherein each training feature vector includes a respective plurality of features that use paths through the social network graph to measure connectedness between a respective social media message and a respective venue; using the training feature vectors to train a classifier to estimate probabilities that social media messages are associated with venues; receiving a new social media message from a user; constructing a feature vector for the new social media message, wherein each feature vector includes a plurality of features that use paths through the social network graph to measure connectedness between the new social media message and the no-venue; executing the trained classifier using the feature vector as input to compute a probability that the new social media message is associated with the no-venue; when the computed probability is greater than a predefined threshold value, determining that the new social media message is not associated with any of the primary venues; and when the computed probability is less than or equal to the predefined threshold value, determining that the new social media message is associated with one of the primary venues. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A computer system for inferring linkage between social media messages and venues, comprising:
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one or more processors; memory; and one or more programs stored in the memory configured for execution by the one or more processors, the one or more programs comprising instructions for; accessing a social network graph comprising nodes representing social media users, nodes representing social media messages generated by the social media users, and nodes representing venues, wherein venues represented in the social network graph comprise a plurality of primary venues and a no-venue, and wherein a link in the social network graph between a social media message node and a node corresponding to the no-venue indicates that the social media message does not correspond to any of the primary venues; constructing a plurality of training feature vectors, wherein each training feature vector includes a respective plurality of features that use paths through the social network graph to measure connectedness between a respective social media message and a respective venue; using the training feature vectors to train a classifier to estimate probabilities that social media messages are associated with venues; receiving a new social media message from a user; constructing a feature vector for the new social media message, wherein each feature vector includes a plurality of features that use paths through the social network graph to measure connectedness between the new social media message and the no-venue; executing the trained classifier using the feature vector as input to compute a probability that the new social media message is associated with the no-venue; when the computed probability is greater than a predefined threshold value, determining that the new social media message is not associated with any of the primary venues; and when the computed probability is less than or equal to the predefined threshold value, determining that the new social media message is associated with one of the primary venues. - View Dependent Claims (17, 18, 19)
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20. A non-transitory computer readable storage medium storing one or more programs configured for execution by a computer system having one or more processors and memory, the one or more programs comprising instructions for:
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accessing a social network graph comprising nodes representing social media users, nodes representing social media messages generated by the social media users, and nodes representing venues, wherein venues represented in the social network graph comprise a plurality of primary venues and a no-venue, and wherein a link in the social network graph between a social media message node and a node corresponding to the no-venue indicates that the social media message does not correspond to any of the primary venues; constructing a plurality of training feature vectors, wherein each training feature vector includes a respective plurality of features that use paths through the social network graph to measure connectedness between a respective social media message and a respective venue; using the training feature vectors to train a classifier to estimate probabilities that social media messages are associated with venues; receiving a new social media message from a user; constructing a feature vector for the new social media message, wherein each feature vector includes a plurality of features that use paths through the social network graph to measure connectedness between the new social media message and the no-venue; executing the trained classifier using the feature vector as input to compute a probability that the new social media message is associated with the no-venue; when the computed probability is greater than a predefined threshold value, determining that the new social media message is not associated with any of the primary venues; and when the computed probability is less than or equal to the predefined threshold value, determining that the new social media message is associated with one of the primary venues.
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Specification