Systems and methods for analyzing veracity of statements

A computer-implemented method using a veracity analyzer (VA) computing device including at least one processor in communication with a memory device, the method comprising:

• using machine learning or artificial intelligence (AI) tools and a plurality of historical text, video, and/or audio statements some of which include inaccurate aspects as a model training input, generating at least one machine learning model, each of the at least one machine learning model being a text analysis model, a body language analysis model, or an inflection of voice analysis model, each machine learning model generated to (i) analyze a plurality of text data, a plurality of voice data, or a plurality of audio data, and (ii) output one of a plurality of reference indicators associated with at least one inaccurate aspect of a user statement made via text, audio, or video, the plurality of reference indicators generated based upon an analysis of a plurality of historical statements using the machine learning or AI tools;
• receiving other text, video, and/or other audio statements;
• identifying one or more inaccurate aspects of the other text, video, and/or audio statements;
• further training the at least one machine learning model using the machine learning or AI tools and the other text, video, and/or audio statements including the identified one or more inaccurate aspects;
• receiving a data stream including one or more types of data including text data, video data, and/or audio data corresponding to a current statement made by a first user;
• inputting at least a portion of data from the data stream into one of the at least one trained machine learning model configured to analyze of one of the one or more types of data of the portion of data;
• outputting, from the one trained machine learning model, at least one candidate indicator included in the portion of data from the data stream matching at least one of the plurality of reference indicators, the at least one candidate indicator indicating that the portion of data includes one or more inaccurate aspects of the at least one inaccurate aspect included in the plurality of historical text, video, and/or audio statements;
• generating, in real-time and in response to outputting the at least one candidate indicator, a flag for each segment of the portion of data including the at least one candidate indicator, wherein each flagged portion includes the one or more inaccurate aspects;
• displaying, on a user device of a second user, an alert message including each flagged segment of the portion of data including the one or more inaccurate aspects;
• and
• displaying, in conjunction with the alert message, a prompt to the second user to perform at least one action to address the one or more inaccurate aspects of each flagged segment.

At least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by a veracity analyzer (VA) computing device including at least one processor in communication with a memory device, the computer-executable instructions cause the at least one processor to:

• using machine learning or artificial intelligence (AI) tools and a plurality of historical text, video, and/or audio statements some of which include inaccurate aspects as a model training input, generate at least one machine learning model, each of the at least one machine learning model being a text analysis model, a body language analysis model, or an inflection of voice analysis model, each machine learning model generated to (i) analyze a plurality of text data, a plurality of voice data, or a plurality of audio data, and (ii) output one of a plurality of reference indicators associated with at least one inaccurate aspect of a user statement made via text, audio, or video, the plurality of reference indicators generated based upon an analysis of a plurality of historical statements using the machine learning or AI tools;
• receive other text, video, and/or other audio statements;
• identify one or more inaccurate aspects of the other text, video, and/or audio statements;
• further train the at least one machine learning model using the machine learning or AI tools and the other text, video, and/or audio statements including the identified one or more inaccurate aspects;
• receive a data stream including one or more types of data including text data, video data, and/or audio data corresponding to a current statement made by a first user;
• input at least a portion of data from the data stream into one of the at least one trained machine learning model configured to analyze of one of the one or more types of data of the portion of data;
• output, from the one trained machine learning model, at least one candidate indicator included in the portion of data from the data stream matching at least one of the plurality of reference indicators, the at least one candidate indicator indicating that the portion of data includes one or more inaccurate aspects of the at least one inaccurate aspect included in the plurality of historical text, video, and/or audio statements;
• generate, in real-time and in response to outputting the at least one candidate indicator, a flag for each portion segment of the portion of data including the at least one candidate indicator, wherein each flagged portion includes the one or more inaccurate aspects;
• display, on a user device of a second user, an alert message including each flagged segment of the portion of data including the one or more inaccurate aspects;
• and
• display, in conjunction with the alert message, a prompt to the second user to perform at least one action to address the one or more inaccurate aspects of each flagged segment.