Systems and methods for analyzing user chance predictions to determine a user classification

A computer system for analyzing one or more user chance predictions to determine a user classification, the computer system comprising:

• a memory having processor-readable instructions stored therein;
• and
• one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions, including functions for:
• receiving a bullish threshold, a bearish threshold, social media data, and one or more time periods from at least one user;
• receiving a plurality of predictions corresponding to at least one prediction user from one or more prediction databases, wherein each of the plurality of predictions includes a basis set identifier, a date, and a basis set prediction value;
• determining, by the one or more processors, a user sentiment based on the social media data;
• based on the plurality of predictions and the user sentiment, determining at least one p-score for each of the one or more time periods;
• receiving one or more basis sets from one or more databases;
• calculating a null distribution of successful predictions based on the one or more basis sets and the one or more time periods;
• determining a prediction user p-value based on the null distribution and the at least one p-score;
• selecting a classification for the at least one prediction user based on the prediction user p-value;
• and
• displaying at least one graphical widget corresponding to the classification on one or more interfaces of a user device, the at least one graphical widget including a recommendation to follow or unfollow a social media platform of the prediction user.

A non-transitory computer-readable medium containing instructions for analyzing one or more user chance predictions to determine a user classification, the instructions comprising:

• receiving a bullish threshold, a bearish threshold, social media data, and one or more time periods from at least one user;
• receiving a plurality of predictions corresponding to at least one prediction user from one or more prediction databases, wherein each of the plurality of predictions includes a basis set identifier, a date, and a basis set prediction value;
• determining a user sentiment based on the social media data;
• based on the plurality of predictions and the user sentiment, determining at least one p-score for each of the one or more time periods;
• receiving one or more basis sets from one or more databases;
• calculating a null distribution of successful predictions based on the one or more basis sets and the one or more time periods;
• determining a prediction user p-value based on the null distribution and the at least one p-score;
• selecting a classification for the at least one prediction user based on the prediction user p-value;
• and
• displaying at least one graphical widget corresponding to the classification on one or more interfaces of a user device, the at least one graphical widget including a recommendation to follow or unfollow a social media platform of the prediction user.