Automated viral content detection

A computer-implemented method, comprising:

• training, by at least one computing device, a machine learning model for sentiment analysis of a plurality of social media posts based at least in part on applying a machine learning algorithm to a training data set that comprises a sample text in a format of a respective social media platform, the machine learning model being trained to classify a respective social media post as at least one of positive, negative, or neutral;
• removing, by the at least one computing device, a media file from a plurality of user posts to generate a plurality of modified user posts in a plaintext format;
• calculating, by the at least one computing device, a negative sentiment score of a subset of posts with a negative sentiment based at least in part on the plurality of modified user posts, wherein the negative sentiment score is calculated by performing the sentiment analysis on the plurality of modified user posts to identify the subset of posts with the negative sentiment based at least in part on the machine learning model;
• determining, by the at least one computing device, the negative sentiment score exceeds a first moving average sentiment score;
• generating, by the at least one computing device, an alert in response to the determination that the negative sentiment score exceeds the first moving average sentiment score;
• formatting, by the at least one computing device, the alert according to a defined format of a recipient;
• and
• transmitting, by the at least one computing device, the alert to a client device associated with the recipient, wherein the alert activates a client application executing on the client device to cause the client application to display the alert and to enable the client device to connect to the at least one computing device across a network to view additional information about the alert;
• and
• halting, by the at least one computing device, the alert at the client device by instructing the client device to cease to display the alert on the client device based at least in part on a second moving average sentiment score that is less than a respective threshold value associated with the first moving average sentiment score.

A non-transitory, computer-readable medium, comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least:

• train a machine learning model for sentiment analysis of a plurality of social media posts based at least in part on applying a machine learning algorithm to a training data set that comprises a sample text in a format of a respective social media platform, the machine learning model being trained to classify a respective social media post as at least one of positive, negative, or neutral;
• remove a media file from a plurality of user posts to generate a plurality of modified user posts in a plaintext format;
• determine a negative sentiment score of a subset of posts with a negative sentiment based at least in part on the plurality of modified user posts, wherein the negative sentiment score is calculated by performing the sentiment analysis on the plurality of modified user posts to identify the subset of posts with the negative sentiment based at least in part on the machine learning model;
• determine the negative sentiment score exceeds a first moving average sentiment score;
• generate an alert in response to the determination that the negative sentiment score exceeds the first moving average sentiment score;
• format the alert according to a defined format of a recipient;
• transmit the alert to a client device associated with the recipient, wherein the alert activates a client application executing on the client device to cause the client application to display the alert and to enable the client device to connect to the computing device across a network to view additional information about the alert;
• and
• halt the alert at the client device by instructing the client device to cease to display the alert on the client device based at least in part on a second moving average sentiment score that is less than a respective threshold value associated with the first moving average sentiment score.