Tracking changes in user-generated textual content on social media computing platforms

A computing system for analyzing social media posts regarding a story on one or more social media platforms, the computing system comprising:

• a processor;
• and
• a memory comprising computer-readable instructions that, when executed by the processor, cause the computing system to at least:
• analyze occurrences of terms in the social media posts and co-occurrences of the terms in same social media posts to generate metrics associated with the terms over time;
• update a graph data structure over time based on the metrics, the graph data structure comprising nodes representing the terms and edges connecting the nodes and representing co-occurrences of the terms, the nodes and edges being weighted based on the metrics, weights of the nodes indicating frequencies of the occurrences of the terms, and weights of the edges indicating frequencies of the co-occurrences;
• select a set of the terms by comparing the weights of the nodes or the edges to a threshold, wherein the set of terms represents a set of terms of interest;
• and
• identify a trend of the story based on changes over time to the weights of nodes and edges corresponding to the set of terms, wherein the story comprises the set of terms of interest.

A computer-readable storage medium storing instructions for analyzing social media posts regarding a story on one or more social media platform, the instructions when executed on a computing device configure the computing device to perform operations comprising:

• tracking occurrences of terms in the social media posts;
• tracking co-occurrences of the terms in the social media posts by identifying terms that occur together in same social media posts;
• updating a graph data structure over time based on metrics associated with the occurrences and co-occurrences, the graph data structure comprising nodes representing the terms and edges connecting the nodes and representing the co-occurrences of the terms, the nodes and the edges being weighted based on the metrics, weights of the nodes indicating frequencies of the occurrences of the terms, and weights of the edges representing frequencies of the co-occurrences;
• and
• detecting a trend of the story based on changes over time to the weights of the nodes and the edges in the graph data structure.