Trend detection in a messaging platform
First Claim
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1. A method comprising:
- receiving, by a computing device, a set of historical time series of social data;
labeling, by the computing device, each of the set of historical time series of social data as trending or non-trending;
selecting, by the computing device, and based on the set of historical time series of social data, a trend detection model;
receiving, by the computing device, a time series having a plurality of instances of social data, wherein the instances of social data share a countable parameter;
for each of a number of bins, counting, by the computing device, occurrences of one or more of the countable parameters in each instance of social data assigned to that bin;
determining, by the computing device, based at least in part on the trend detection model, on the count for each bin, and on the set of historical time series of social data and the one or more instances of social data in the time series of social data that correspond to a particular event, a measure of a trend associated with the countable parameter;
determining, by the computing device, a distance between at least one historical time series of the trend detection model and the time series of social data that corresponds to the particular event;
determining, by the computing device, based at least in part on the distance and a scaling parameter, a particular weight;
determining, by the computing device, a trending value based on a trending score generated using the particular weight, the trending value representing a ratio of a first aggregation of weights and a second aggregation of weights, wherein;
the first aggregation of weights is based on a first plurality of weights, the first plurality of weights based at least in part on one or more historical time series of social data labeled as trending,the second aggregation of weights is based on a second plurality of weights, the second plurality of weights based at least in part on one or more historical time series of social data labeled as non-trending, andthe particular weight is included in at least one of the first aggregation of weights or the second aggregation of weights;
implementing cycle-correction to the measure of the trend, based on pattern information associated with respective time data associated with each of the one or more historical time series of social data labeled, as trending to obtain a cycle-corrected measure of the trend;
determining, by the computing device, that the cycle-corrected measure of the trend satisfies a trend threshold; and
responsive to determining that the cycle-corrected measure of the trend satisfies the trend threshold, outputting, by the computing device, at least one indication of the detected trend.
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Abstract
A system and method for trend detection in a messaging platform. A trend detection model is selected and a time series having a plurality of instances of social data is received, wherein the instances of social data share a countable parameter. A count is made of occurrences of countable parameters in each instance of social data assigned to that bin and a trend detected based at least in part on the trend detection model and on the count for each bin.
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Citations
20 Claims
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1. A method comprising:
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receiving, by a computing device, a set of historical time series of social data; labeling, by the computing device, each of the set of historical time series of social data as trending or non-trending; selecting, by the computing device, and based on the set of historical time series of social data, a trend detection model; receiving, by the computing device, a time series having a plurality of instances of social data, wherein the instances of social data share a countable parameter; for each of a number of bins, counting, by the computing device, occurrences of one or more of the countable parameters in each instance of social data assigned to that bin; determining, by the computing device, based at least in part on the trend detection model, on the count for each bin, and on the set of historical time series of social data and the one or more instances of social data in the time series of social data that correspond to a particular event, a measure of a trend associated with the countable parameter; determining, by the computing device, a distance between at least one historical time series of the trend detection model and the time series of social data that corresponds to the particular event; determining, by the computing device, based at least in part on the distance and a scaling parameter, a particular weight; determining, by the computing device, a trending value based on a trending score generated using the particular weight, the trending value representing a ratio of a first aggregation of weights and a second aggregation of weights, wherein; the first aggregation of weights is based on a first plurality of weights, the first plurality of weights based at least in part on one or more historical time series of social data labeled as trending, the second aggregation of weights is based on a second plurality of weights, the second plurality of weights based at least in part on one or more historical time series of social data labeled as non-trending, and the particular weight is included in at least one of the first aggregation of weights or the second aggregation of weights; implementing cycle-correction to the measure of the trend, based on pattern information associated with respective time data associated with each of the one or more historical time series of social data labeled, as trending to obtain a cycle-corrected measure of the trend; determining, by the computing device, that the cycle-corrected measure of the trend satisfies a trend threshold; and responsive to determining that the cycle-corrected measure of the trend satisfies the trend threshold, outputting, by the computing device, at least one indication of the detected trend. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computing device comprising:
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at least one processor; and at least one non-transitory computer-readable storage medium storing instructions that are executable by the at least one processor to; receive a set of historical time series of social data; label each of the set of historical time series of social data as trending or non-trending; select, based on the set of historical time series of social data, a trend detection model; receive a time series having a plurality of instances of social data, wherein the instances of social data share a countable parameter; for each of a number of bins, count occurrences of one or more of the countable parameters in each instance of social data assigned to that bin; determine, based at least in part on the trend detection model, on the count for each bin, and on the set of historical time series of social data and the one or more instances of social data in the time series of social data that correspond to a particular event, a measure of a trend associated with the countable parameter; determine a distance between at least one historical time series of the trend detection model and the time series of social data that corresponds to the particular event; determine, based at least in part on the distance and a scaling parameter, a particular weight; determine a trending value based on a trending score generated using the particular weight, the trending value representing a ratio of a first aggregation of weights and a second aggregation of weights, wherein; the first aggregation of weights is based on a first plurality of weights, the first plurality of weights based at least in part on one or more historical time series of social data labeled as trending, the second aggregation of weights is based on a second plurality of weights, the second plurality of weights based at least in part on one or more historical time series of social data labeled as non-trending, and the particular weight is included in at least one of the first aggregation of weights or the second aggregation of weights; implement cycle-correction to the measure of the trend, based on pattern information associated with respective time data associated with each of the one or more historical time series of social data labeled, as trending to obtain a cycle-corrected measure of the trend; determine that the cycle-corrected measure of the trend satisfies a trend threshold; and responsive to the determination that the cycle-corrected measure of the trend satisfies the trend threshold, output at least one indication of the detected trend. - View Dependent Claims (13, 14, 15, 16)
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17. A non-transitory computer-readable storage medium encoded with instructions that, when executed, cause at least one processor of a computing device to:
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receive a set of historical time series of social data; label each of the set of historical time series of social data as trending or non-trending; select, based on the set of historical time series of social data, a trend detection model; receive a time series having a plurality of instances of social data, wherein the instances of social data share a countable parameter; for each of a number of bins, count occurrences of one or more of the countable parameters in each instance of social data assigned to that bin; determine, based at least in part on the trend detection model, on the count for each bin, and on the set of historical time series of social data and the one or more instances of social data in the time series of social data that correspond to a particular event, a measure of a trend associated with the countable parameter; determine a distance between at least one historical time series of the trend detection model and the time series of social data that corresponds to the particular event; determine, based at least in part on the distance and a scaling parameter, a particular weight; determine a trending value based on a trending score generated using the particular weight, the trending value representing a ratio of a first aggregation of weights and a second aggregation of weights, wherein; the first aggregation of weights is based on a first plurality of weights, the first plurality of weights based at least in part on one or more historical time series of social data labeled as trending, the second aggregation of weights is based on a second plurality of weights, the second plurality of weights based at least in part on one or more historical time series of social data labeled as non-trending, and the particular weight is included in at least one of the first aggregation of weights or the second aggregation of weights; implement cycle-correction to the measure of the trend, based on pattern information associated with respective time data associated with each of the one or more historical time series of social data labeled, as trending to obtain a cycle-corrected measure of the trend; determine that the cycle-corrected measure of the trend satisfies a trend threshold; and responsive to the determination that the cycle-corrected measure of the trend satisfies the trend threshold, output at least one indication of the detected trend. - View Dependent Claims (18, 19, 20)
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Specification