Verizon Patent and Licensing Inc.

Yahoo Assets LLC

Changwei Hu

Yifan Hu

Kevin Yen

Fei Tan

Keqian Li

Maxim I Sviridenko

Shaunak Mishra

Chao-Hung Wang

Ben Shahshahani

Manisha Verma

Avinash Chukka

Yu Tian

Max Edward Beech

Kamil Michal Zasadzinski

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.

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New Providence, NJ, United States of America

Changwei Hu