At&t Intellectual Property I, L.p.

Verizon Patent and Licensing Inc.

Yahoo Assets LLC

Oath Inc.

Verizon Media Inc.

Yifan Hu

Kadangode K Ramakrishnan

Jeffrey Erman

Jeffrey Pang

Robert Bell

Changwei Hu

Kevin Yen

Fei Tan

Emden Gansner

Keqian Li

Amanda Joy Stent

Aasish Kumar Pappu

Christopher Volinsky

Maxim I Sviridenko

Steven S Skiena

Baris Coskun

Meizhu Liu

Shaunak Mishra

Sungjin Lee

Chao-Hung Wang

Stephen Kobourov

Xiang Li

Ben Shahshahani

Manisha Verma

Avinash Chukka

Yu Tian

Max Edward Beech

Kamil Michal Zasadzinski

Junting Ye

Wei Yu Lin

Hua-Ying Tsai

The disclosed systems and methods provide a framework for a proactive prediction of the toxic propensity of an article. Prior to the publication and/or reception of comments to online content, the disclosed framework determines the toxic propensity of the content's context and/or specific words, sentences, sentiments, tone or other messages receivable from consumption of the content. Thus, disclosed framework performs proactive forecasting of the content's toxicity propensity', which quantifies how likely the content is prone to incur or attract toxic comments. The framework can function and/or be configured to operate in a manner that can perform specifically adherent moderation actions that correspond to the content and control how the content can be interacted with, based on the toxic propensity determination, prior to the content's publication in an effort to thwart, prevent or stop toxic environments surrounding or stemming from the content from coming into existence.

Computerized system and method for automatic moderation of online content

The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including an original token and a ground truth token label for the original token, a manipulation is applied to the original token to generate a manipulated token based on which to generate manipulated input data. Training data is generated based on the manipulated input data, the ground truth token label, and a ground truth action that, when applied to the manipulated token, yields the original token with the ground truth token label. A text moderation model is trained based on the training data.

System and method for text moderation via pretrained transformers

One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Performance metric prediction and content item text suggestion based upon content item text

The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including a plurality of text strings, a plurality of manipulated text strings are generated for each of the plurality of training text strings by first applying a manipulation to each of at least one original token in the text string to generate a manipulated token, where the original token has a ground truth token label and then determining, with respect to each manipulated token, a ground truth action which, when applied to the manipulated token, yields the original token with the ground truth token label. Training data are generated with a plurality of training data packs, each of which corresponds to one of the plurality of text strings in the input data and includes a manipulated text string with at least one manipulated token, at least one ground truth token label, and at least one ground truth action which, when applied to the at least one manipulated token produces the at least one ground truth token label. The training data are for training text moderation models that facilitate text moderation.

A method that incorporates teachings of the subject disclosure may include, for example transmitting, via a connection to a network having an available digital bandwidth, a first pre-fetch media content item of media content items included in the a media recommendation transmitted to a communication device, the first pre-fetch media content item having a first rank. Responsive to a determination of a change in the available digital bandwidth, the transmitting of the first pre-fetch media content item is stopped and a second pre-fetch media content item of media content items included in the media recommendation is transmitted via the connection to the network. The second pre-fetch media content item has a second rank. Other embodiments are disclosed.

Method and apparatus for content distribution over a network

The present teaching relates to generating user profiles with semantic knowledge. A first information associated with a user is obtained. One or more entities are identified from the first information. The one or more entities are augmented based on second information to generate a set of augmented entities. The set of augmented entities are clustered into a set of hierarchical clusters. A set of user profiles is generated based on the set of hierarchical clusters so that the user profile is to be used to personalize content recommendation.

Method and system for user profiling for content recommendation

Communication accounts may contain information such as account holder names, contact lists, and communication logs. Such information may be processed for generating features that may be used as corpus for a machine learning algorithm for developing classifiers of names. Specifically, names and contact names of the accounts may be arranged in to a document according to the manner in which account holders communicate with the contacts. The document may be used for generating word embedding of the names. Names prelabeled with ethnicity together with their word embedding may be used as training data for developing ethnicity classifiers based on machine learning algorithms.

Name-based classification of electronic account users

A method that incorporates teachings of the subject disclosure may include, for example performing, by a wireless communication device comprising a processor, wireless telephony communications via a first wireless interface, transmitting information associated with media-related activities to a server, receiving from the server a media recommendation that is generated according to the information associated with media-related activities, initiating communications with a network via a second wireless interface, downloading a media content item of the media recommendation from a content provider via the second wireless interface, and presenting the media content item at a display. Other embodiments are disclosed.

Relational data can be visualized in a map representation. A plurality of vertices and edges are determined based on a set of data identifying objects (i.e., vertices) and relationships (i.e., edges). The vertices and edges are embedded in a planar graph and a Voronoi diagram is generated based at least the plurality of vertices within a set of bounding-points located at least a distance away from the vertices of the map. A bounding-box is associated with each of the vertices, and a set of points generated along each bounding-box. The Voronoi diagram is generated based on the vertices and the set of points of each bounding-box. Each resulting Voronoi cell associated with a common vertex is merged to form a plurality of common cells (i.e., map). The size of each bounding-box can correlate to the importance of its associated vertex, and the points along the bounding-box can be randomly perturbed.

Visualization and representation of data clusters and relations

Methods, systems, and products are disclosed for graphing data. A layout is retrieved that comprises locations for vertices. A proximity graph is generated using triangulation. Nodal overlaps are removed.

Methods, systems, and products for graphing data to reduce overlap

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Mountain Lakes, NJ, United States of America

Yifan Hu