US 11,694,443 B2 · Automatic identification of misleading videos using a computer network

Abstract

Machine-based video classifying to identify misleading videos by training a model using a video corpus, obtaining a subject video from a content server, generating respective feature vectors of a title, a thumbnail, a description, and a content of the subject video, determining a first semantic similarities between ones of the feature vectors, determining a second semantic similarity between the title of subject video and titles of videos in the misleading video corpus in a same domain as the subject video, determining a third semantic similarity between comments of the subject video and comments of videos in the misleading video corpus in the same domain as the subject video, classifying the subject video using the model and based on the first semantic similarities, the second semantic similarity, and the third semantic similarity, and outputting the classification of the subject video to a user.

Claims20

Dependent claims

A method, comprising:

training, by a computing device, a model using a video corpus;
obtaining, by the computing device, a subject video from a content server;
generating, by the computing device, respective feature vectors of a title, a thumbnail, a description, and a content of the subject video;
determining, by the computing device, first semantic similarities between ones of the feature vectors;
determining, by the computing device, a second semantic similarity between the title of subject video and titles of videos in a misleading video corpus in a same domain as the subject video;
determining, by the computing device, a third semantic similarity between comments of the subject video and comments of videos in the misleading video corpus in the same domain as the subject video;
classifying, by the computing device, the subject video using the model and based on the first semantic similarities, the second semantic similarity, and the third semantic similarity;
and
outputting, by the computing device, the classification of the subject video to a user.

6 more elements

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

obtain a subject video from a content server;
generate respective feature vectors of a title, a thumbnail, a description, and a content of the subject video;
determine semantic similarities between ones of the feature vectors;
classify the subject video based on a weighted sum of the semantic similarities;
and
output the classification of the subject video to a user.

3 more elements

A system comprising:

a processor, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
obtain a subject video from a content server;
generate respective feature vectors of a title, a thumbnail, a description, and a content of the subject video;
determine semantic similarities between ones of the feature vectors;
classify the subject video based on a weighted sum of the semantic similarities;
and
output the classification of the subject video to a user.

4 more elements

Dependencies are inferred from the claim text.

Automatic identification of misleading videos using a computer network

Life of this patent

Automatic identification of misleading videos using a computer network

Life of this patent

Abstract

Claims20