System for real-time fact-checking of multimedia content

A system, comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors to:
• receive content;
• using a machine learning model query, automatically identify one or more statements in the content that meet one or more candidate criteria for correctness verification, wherein identifying one or more statements that meet the one or more candidate criteria include distinguishing purported facts from subjective opinion statements based on machine linguistic analysis;
• for at least one statement of the identified one or more statements, using a machine learning model, perform a first assessment of correctness of the at least one statement including by searching resources associated with the at least one statement to determine a search result and automatically evaluating the search result as an input to the machine learning model using retrieval-augmented generation to determine whether the at least one statement is verifiable, wherein the first assessment includes categorizing the at least one statement as missing context when an ambiguity associated with the at least one statement prevents a definitive assessment of correctness;
• in response to categorizing the at least one statement as missing context, request a clarification on the ambiguity;
• receive a user feedback response clarifying the ambiguity;
• perform a second assessment of the correctness of the at least one statement based on the user feedback response;
• and
• automatically determine based on the second assessment, a machine learning model generated revision of at least one statement assessed to be incorrect in the identified one or more statements.

A method, comprising:

• receiving a selection of content;
• generating a content fact-checking request, wherein the content fact-checking request includes the selected content;
• providing the content fact-checking request to a content fact-checking service that performs machine linguistic analysis for distinguishing a purported fact from a subjective opinion statement for the selected content;
• and
• in response to providing the content fact-checking request to the content fact-checking service, receiving a content fact-checking result, wherein the content fact-checking result is generated by the content fact-checking service searching resources associated with the selected content to determine a search result and evaluating the search result as an input to the machine learning model using retrieval-augmented generation to determine whether the selected content is verifiable including by:
• categorizing at least a portion of the selected content as missing context due to an ambiguity, requesting a clarification on the ambiguity, receiving a user feedback response clarifying the ambiguity, performing an updated assessment of correctness of the selected content based on the user feedback response, and automatically determining based on the updated assessment, a machine learning model generated revision of the selected content;
• wherein the content fact-checking result includes:
• one or more statements of the content identified as meeting one or more candidate criteria for correctness verification, and a correctness assessment of each of the one or more identified statements.

A method, comprising:

• receiving content;
• using a machine learning model query to automatically identify one or more statements in the content that meet one or more candidate criteria for correctness verification, wherein identifying one or more statements that meet the one or more candidate criteria include distinguishing purported facts from subjective opinion statements based on machine linguistic analysis;
• for at least one statement of the identified one or more statements, using a machine learning model to identify an ambiguity associated with the at least one statement that prevents a definitive assessment of correctness and categorize the at least one statement as missing context including by searching resources associated with the at least one statement to determine a search result and evaluating the search result as an input to the machine learning model using retrieval-augmented generation to determine whether the at least one statement is verifiable;
• in response to categorizing the at least one statement as missing context, request a clarification on the ambiguity;
• receiving a user feedback response to clarify the ambiguity of the at least one statement;
• perform a second assessment of the correctness of the at least one statement based on the user feedback response;
• and
• automatically determine based on the second assessment, a machine learning model generated revision of at least one statement assessed to be incorrect in the identified one or more statements.