System And Method For Using Artificial Intelligence (AI) To Analyze Social Media Content

1 . A computing device, comprising:

a processing system configured to:

retrieve media content from a media-content knowledge repository;

partition the media content into a plurality of media-content segments;

select a query filter that includes rules, criteria, data parameters, or constraints for at least one media-content segment in the plurality of media-content segments;

execute the query filter to obtain a candidate data subset that corresponds to the at least one media-content segment;

select a feature graph that maps features of the obtained candidate data subset in a multidimensional space;

identify video-component attributes and audio-component attributes of the at least one media-content segment;

correlate the video-component attributes with the audio-component attributes to generate correlated modality data for at least one media-content segment;

determine segment attributes for the at least one media-content segment based on the correlated modality data; and

perform a responsive action that targets the at least one media-content segment in accordance with the determined segment attributes.

2 . The computing device of claim 1 , wherein the processing system is configured to determine the segment attributes for the at least one media-content segment based on the correlated modality data by aggregating viewer-engagement metrics that include at least one or more of a view count, a like count, a comment count, or a dwell time.

3 . The computing device of claim 1 , wherein the processing system is configured to select the feature graph that maps features of the candidate data subset in the multidimensional space by selecting one or more of:

a product-mention graph;

a sentiment-analysis embedding graph; or

a trend-analysis embedding graph.

4 . The computing device of claim 1 , wherein the processing system is configured to perform the responsive action by performing at least one or more of:

placing select influencers on a watchlist for future reference;

promoting specific posts;

publishing tailored content;

identifying appropriate content creators or platforms;

generating and sending offers for product sponsorship;

generating offers for product reviews;

sending the offers for product reviews to content creators;

generating and sending content designed to alter or enhance an influencer's viewpoint on a specific topic of interest (ToI) or brand;

reviewing published content;

identifying and monitoring influencers;

launching an advertising campaign tailored to specific audience segments identified through media content analysis;

updating or adjusting a content strategy based on the analysis of media content segment;

using the determined attributes from the media content segments to personalize content for users on a platform;

screening new media content segments for compliance with regulatory standards or platform guidelines;

identifying and communicating potential collaborations between brands and content creators based on matching attributes;

analyzing media content to generate keywords and tags;

generating and sending feedback or suggestions for improvement to content creators;

generating and publishing user engagement features or interactive elements for media content;

publishing or broadcasting select media content across various platforms or networks to increase its reach and impact;

triggering an alert when negative sentiment exceeds a threshold for a monitored brand; or

triggering an alert for immediate crisis management response detecting negative sentiment or controversial content associated with a brand or topic.

5 . The computing device of claim 1 , wherein the processing system is further configured to:

identify domain entities present in the at least one media-content segment;

identify discourse constructs present in the at least one media-content segment; and

map the domain entities and the discourse constructs to generate at least one analytical question.

6 . The computing device of claim 5 , wherein the processing system is further configured to obtain the analytical question from a question knowledge repository that stores analytical questions indexed by domain entities and discourse constructs.

7 . The computing device of claim 5 , wherein the processing system is further configured to generate the analytical question dynamically by referencing topics of interest obtained from an external source.

8 . The computing device of claim 5 , wherein the processing system is further configured to:

execute the analytical question as a generated query against the media-content knowledge repository to obtain a query result set;

generate a control instruction based on the query result set; and

transmit the control instruction to an external system configured to manage advertisement placement.

9 . The computing device of claim 8 , wherein the processing system is configured to generate the control instruction based on the query result set by generating the control instruction to prevent display of an advertisement for a risk-averse brand adjacent to media content flagged with high-risk brand-safety attributes.

10 . The computing device of claim 5 , wherein the processing system is configured to:

identify the video-component attributes and the audio-component attributes of the at least one media-content segment further by identifying text-component attributes of the at least one media-content segment; and

correlate the video-component attributes with the audio-component attributes to generate the correlated modality data for the at least one media-content segment by correlating the text-component attributes with the video-component attributes and the audio-component attributes to generate the correlated modality data.

11 . The computing device of claim 10 , wherein the processing system is further configured to analyze the video-component attributes, the audio-component attributes, and the text-component attributes of the at least one media-content segment to identify a plurality of data points that identify complexity of the at least one media-content segment, the plurality of data points including:

identities of objects detected in video frames;

spatial locations of the objects within the video frames;

temporal interactions among the objects across successive video frames;

sentiment indicators associated with the objects or with discourse associated to the objects; and

relationships that link the objects to one another.

12 . The computing device of claim 11 , wherein the processing system is further configured to generate a training data record that stores the plurality of data points, the correlated modality data, and the segment attributes.

13 . The computing device of claim 12 , wherein the processing system is further configured to use the generated training data record to train a model to recognize at least one of object identities, temporal interactions, or sentiment indicators in subsequent media content.

14 . The computing device of claim 13 , wherein the processing system is further configured to update the machine-learning model using weak-supervision techniques that incorporate additional media content and associated annotations.

15 . A computer-implemented method performed by a processor of a media-analytics platform for analyzing media content, the method comprising:

retrieving media content from a media-content knowledge repository;

partitioning the media content into a plurality of media-content segments;

selecting a query filter that includes rules, criteria, data parameters, or constraints for at least one media-content segment in the plurality of media-content segments;

executing the query filter to obtain a candidate data subset that corresponds to the at least one media-content segment;

selecting a feature graph that maps features of the obtained candidate data subset in a multidimensional space;

identifying video-component attributes and audio-component attributes of the at least one media-content segment;

correlating the video-component attributes with the audio-component attributes to generate correlated modality data for at least one media-content segment;

determining segment attributes for the at least one media-content segment based on the correlated modality data; and

performing a responsive action that targets the at least one media-content segment in accordance with the determined segment attributes.

16 . The method of claim 15 , wherein determining the segment attributes for the at least one media-content segment based on the correlated modality data comprises aggregating viewer-engagement metrics that include at least one or more of a view count, a like count, a comment count, or a dwell time.

17 . The method of claim 15 , wherein selecting the feature graph that maps features of the candidate data subset in the multidimensional space comprises selecting one or more of:

a product-mention graph;

a sentiment-analysis embedding graph; or

a trend-analysis embedding graph.

18 . The method of claim 15 , wherein performing the responsive action comprises performing at least one or more of:

placing select influencers on a watchlist for future reference;

promoting specific posts;

publishing tailored content;

identifying appropriate content creators or platforms;

generating and sending offers for product sponsorship;

generating offers for product reviews;

sending the offers for product reviews to content creators;

generating and sending content designed to alter or enhance an influencer's viewpoint on a specific topic of interest (ToI) or brand;

reviewing published content;

identifying and monitoring influencers;

launching an advertising campaign tailored to specific audience segments identified through media content analysis;

updating or adjusting a content strategy based on the analysis of media content segment;

using the determined attributes from the media content segments to personalize content for users on a platform;

screening new media content segments for compliance with regulatory standards or platform guidelines;

identifying and communicating potential collaborations between brands and content creators based on matching attributes;

analyzing media content to generate keywords and tags;

generating and sending feedback or suggestions for improvement to content creators;

generating and publishing user engagement features or interactive elements for media content;

publishing or broadcasting select media content across various platforms or networks to increase its reach and impact;

triggering an alert when negative sentiment exceeds a threshold for a monitored brand; or

triggering an alert for immediate crisis management response detecting negative sentiment or controversial content associated with a brand or topic.

19 . The method of claim 15 , further comprising:

identifying domain entities present in the at least one media-content segment;

identifying discourse constructs present in the at least one media-content segment; and

mapping the domain entities and the discourse constructs to generate at least one analytical question.

20 . The method of claim 19 , further comprising obtaining the analytical question from a question knowledge repository that stores analytical questions indexed by domain entities and discourse constructs.

21 . The method of claim 19 , further comprising generating the analytical question dynamically by referencing topics of interest obtained from an external source.

22 . The method of claim 19 , further comprising:

executing the analytical question as a generated query against the media-content knowledge repository to obtain a query result set;

generating a control instruction based on the query result set; and

transmitting the control instruction to an external system configured to manage advertisement placement.

23 . The method of claim 22 , wherein the control instruction prevents display of an advertisement for a risk-averse brand adjacent to media content flagged with high-risk brand-safety attributes.

24 . The method of claim 19 , wherein:

identifying the video-component attributes and the audio-component attributes of the at least one media-content segment further comprises identifying text-component attributes of the at least one media-content segment; and

correlating the video-component attributes with the audio-component attributes to generate the correlated modality data for the at least one media-content segment comprises correlating the text-component attributes with the video-component attributes and the audio-component attributes to generate the correlated modality data.

25 . The method of claim 24 , further comprising analyzing the video-component attributes, the audio-component attributes, and the text-component attributes of the at least one media-content segment to identify a plurality of data points that identify complexity of the at least one media-content segment, the plurality of data points including:

identities of objects detected in video frames;

spatial locations of the objects within the video frames;

temporal interactions among the objects across successive video frames;

sentiment indicators associated with the objects or with discourse associated to the objects; and

relationships that link the objects to one another.

26 . The method of claim 11 , further comprising generating a training data record that stores the plurality of data points, the correlated modality data, and the segment attributes.

27 . The method of claim 12 , further comprising using the generated training data record to train a model to recognize at least one of object identities, temporal interactions, or sentiment indicators in subsequent media content.

28 . The method of claim 13 , further comprising updating the machine-learning model using weak-supervision techniques that incorporate additional media content and associated annotations.

29 . A non-transitory processor-readable medium having stored thereon processor-readable instructions configured to cause a processor in a computing device to perform operations for analyzing media content, the operations comprising:

retrieving media content from a media-content knowledge repository;

partitioning the media content into a plurality of media-content segments;

selecting a query filter that includes rules, criteria, data parameters, or constraints for at least one media-content segment in the plurality of media-content segments;

executing the query filter to obtain a candidate data subset that corresponds to the at least one media-content segment;

selecting a feature graph that maps features of the obtained candidate data subset in a multidimensional space;

identifying video-component attributes and audio-component attributes of the at least one media-content segment;

correlating the video-component attributes with the audio-component attributes to generate correlated modality data for at least one media-content segment;

determining segment attributes for the at least one media-content segment based on the correlated modality data; and

performing a responsive action that targets the at least one media-content segment in accordance with the determined segment attributes.