SYSTEMS AND METHODS FOR INVESTING IN A COMMUNICATION PLATFORM THAT ALLOWS MONETIZATION BASED ON A SCORE
A method comprising using at least one hardware processor to: generate a que of users willing to respond to queries; receive a query and a required number of responses from an application; and assign from the que a plurality of users to respond to the query, wherein the number of the plurality of users is based on the required number of responses and wherein the users are assigned based on a score that is indicative of a quality determination of prior responses for each of the plurality of users, wherein the score is based at least on positive feedback including investing, wherein investing comprises: displaying a list of all responses of a particular user, receiving a selection of a response from the list, receiving a payment representative of the investment, initiating an investigation; and providing a results of the investigation.
1 . A method comprising using at least one hardware processor to:
generate a que of users willing to respond to queries;
receive a query and a required number of responses from an application; and
assign from the que a plurality of users to respond to the query, wherein the number of the plurality of users is based on the required number of responses and wherein the users are assigned based on a score that is indicative of a quality determination of prior responses for each of the plurality of users, wherein the score is based at least on positive feedback including investing, wherein investing comprises:
receiving, a selection of a response that the user desires to invest in,
receiving a selection of the desired number of reviews the user would like as part of the investment,
initiating an investigation during which the desired number of reviews of the selected response will be performed; and
based on the reviews, adjust the score or leave the score the same.
2 . The method of claim 1 , wherein the score is determined using the following equation:
SCORE=[{(ACTIVITY*0.25)+(TPF*0.25)+(ACCURACY*0.25)+((1—“NEGATIVE CONTENT” factor)*0.25)}*5]−[(# of warnings)/2]−(“don't know” factor * 3),
Where:
ACTIVITY: the relative value, compared to all other users, of a user's activity in an activity period;
TPF: the relative value, compared to all other users, of how much overall positive feedback during a TPF period;
ACCURACY: the relative value, compared to all other users, of what percent of positive feedback the user has overall for an accuracy period;
NEGATIVE CONTENT factor: the relative value, compared to all other users, of the percent of that user's total responses that have been tagged as “negative content” (the number of “negative content” tags since that user joined the number of total responses since that user joined);
the “don't know” Factor: the relative value (compared to all other users) of how many times he/she clicked the “don't know” button in in a certain time period; and
the number of warnings: number of warnings a given user has.
3 . The method of claim 1 , wherein the activity period is 2 days.
4 . The method of claim 1 , wherein the TPF period is since a user has joined.
5 . The method of claim 1 , wherein the positive feedback can comprise at least one of LIKES or INVESTING.
6 . The method of claim 1 , wherein the accuracy period is 15 days.
7 . The method of claim 1 , wherein the relative value for the NEGATIVE CONTENT is determined as the number of positive feedbacks during the accuracy period/the number of total responses in the accuracy period.
8 . The method of claim 1 , wherein the relative value for the NEGATIVE CONTENT is determined as the number of “negative content” tags during a negative content period/the, number of total responses during the negative content period.
9 . The method of claim 1 , wherein the number of warnings is broken down by category.
10 . The method of claim 9 , wherein the categories can include CATEGORY 1 OFFENSES, which is any type of content or act that deliberately offends people, institutions, beliefs or that would be categorized as misconduct or unethical.
11 . The method of claim 9 , wherein the categories can include CATEGORY 2 OFFENSES, which is any type of content considered of bad quality, gibberish, copy-paste content, or unrelated content.
12 . A system comprising;
at least one hardware processor; and
one or more software modules that are configured to, when executed by the at least one hardware processor,
generate a que of users willing to respond to queries;
receive a query and a required number of responses from an application; and
assign from the que a plurality of users to respond to the query, wherein the number of the plurality of users is based on the required number of responses and wherein the users are assigned based on a score that is indicative of a quality determination of prior responses for each of the plurality of users, wherein the score is based at least on positive feedback including investing, wherein investing comprises:
receiving a selection of a response that the user desires to invest in,
receiving a selection of the desired number of reviews the user would like as part of the investment,
initiating an investigation during which the desired number of reviews of the selected response will be performed; and
based on the reviews, adjust the score or leave the score the same.
13 . The system of claim 12 , wherein the score is determined using the following equation:
SCORE=[{(ACTIVITY*0.25)+(TPF*0.25)+(ACCURACY*0.25)+((1—“NEGATIVE CONTENT” factor)*0.25)}*5]−[(# of warnings)/2]−(“don't know” factor * 3),
Where:
ACTIVITY: the relative value, compared to all other users, of a user's activity in an activity period;
TPF: the relative value, compared to all other users, of how much overall positive feedback during a TPF period;
ACCURACY: the relative value, compared to all other users, of what percent of positive feedback the user has overall for an accuracy period;
NEGATIVE CONTENT factor: the relative value, compared to all other users, of the percent of that user's total responses that have been tagged as “negative content” (the number of “negative content” tags since that user joined/the number of total responses since that user joined);
the “don't know” factor: the relative value (compared to all other users) of how many times he/she clicked the “don't know” button in in a certain time period; and
the number of warnings: number of warnings a given user has.
13 . The system of claim 12 , wherein the activity period is 2 days.
14 . The system of claim 12 , wherein the TPF period is since a user has joined.
15 . The system of claim 12 , wherein the positive feedback can comprise at least one of LIKES or INVESTING.
16 . The system of claim 12 , wherein the accuracy period is 15 days.
17 . The system of claim 12 , wherein the relative value for the NEGATIVE CONTENT is determined as the number of positive feedbacks during the accuracy period/the number of total responses in the accuracy period.
18 . The system of claim 12 , wherein the relative value for the NEGATIVE CONTENT is determined as the number of “negative content” tags during a negative content period/the number of total responses during the negative content period.
19 . The system of claim 12 , wherein the number of warnings is broken down by category.
20 . The system of claim 19 , wherein the categories can include CATEGORY 1 OFFENSES, which is any type of content or act that deliberately offends people, institutions, beliefs or that would be categorized as misconduct or unethical.
21 . The system of claim 19 , wherein the categories can include CATEGORY 2 OFFENSES, which is any type of content considered of had quality, gibberish, copy-paste content, or unrelated content.
22 . A non-transitory computer-readable medium having instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to:
generate a que of users willing to respond to queries;
receive a query and a required number of responses from an application; and
assign from the que a plurality of users to respond to the query, wherein the number of the plurality of users is based on the required number of responses and wherein the users are assigned based on a score that is indicative of a quality determination of prior responses for each of the plurality of users, wherein the score is based at least on positive feedback including investing, wherein investing comprises:
receiving a selection of a response that the user desires to invest in, receiving a selection of the desired number of reviews the user would like as part of the investment,
initiating an investigation during which the desired number of reviews of the selected response will be performed; and
based on the reviews, adjust the score or leave the score the same.
23 . The non-transitory computer-readable medium having instructions stored therein of claim 22 , wherein the score is determined using the following equation:
SCORE=[{(ACTIVITY*0.25)+(TPF*0.25)+(ACCURACY*0.25)+((1—“NEGATIVE CONTENT” factor)*0.25)}*5]−[(# of warnings)/2]−(“don't know” factor * 3),
Where:
ACTIVITY: the relative value, compared to all other users, of a user's activity in an activity period;
TPF: the relative value, compared to all other users, of how much overall positive feedback during a TPF period;
ACCURACY: the relative value, compared to all other users, of what percent of positive feedback the user has overall for an accuracy period;
NEGATIVE CONTENT factor: the relative value, compared to all other users, of the percent of that user's total responses that have been tagged as “negative content” (the number of “negative content” tags since that user joined/the number of total responses since that user joined);
the “don't know” factor: the relative value (compared to all other users) of how many times he/she clicked the “don't know” button in in a certain time period; and
the number of warnings: number of warnings a given user has.
24 . The non-transitory computer-readable n odium having instructions stored therein of claim 21 , wherein the activity period is 2 days.
25 . The non-transitory computer-readable medium having instructions stored therein claim 22 , wherein the TPF period is since a user has joined.
26 . The non-transitory computer-readable medium having instructions stored therein of claim 22 , wherein the positive feedback can comprise at least one of LIKES or INVESTING.
27 . The non-transitory computer-readable medium having instructions stored therein of claim 21 , wherein the accuracy period is 15 days.
28 . The non-transitory computer-readable medium having instructions stored therein of claim 22 , wherein the relative value for the NEGATIVE CONTENT is determined as the number of positive feedbacks during the accuracy period/the number of total responses in the accuracy period.
29 . The non-transitory computer-readable medium having instructions stored therein of claim 22 , wherein the relative value for the NEGATIVE CONTENT is determined as the number of “negative content” tags during a negative content period/the number of total responses during the negative content period.
30 . The non-transitory computer-readable medium having instructions stored therein of claim 22 , wherein the number of warnings is broken down by category.
31 . The non-transitory computer-readable medium having instructions stored therein of claim 30 , wherein the categories can include CATEGORY 1 OFFENSES, which is any type of content or act that deliberately offends people, institutions, beliefs or that would be categorized as misconduct or unethical.
32 . The non-transitory computer-readable medium having instructions stored therein of claim 30 , wherein the categories can include CATEGORY 2 OFFENSES, which is any type of content considered of bad quality, gibberish, copy-paste content, or unrelated content.