10. A method for dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users, method comprising: defining a user characteristic, the user characteristic being an estimation that a user is a human user, wherein: when the user corresponds to the user characteristic, the user is estimated to be a human user, and when the user does not correspond to the user characteristic, the user is estimated to be a robot user; identifying a model that enhances the user characteristic definer by detecting when users alter activity to cause the ticket management system to falsely characterize the users, the model including at least one machine-learning algorithm, identifying, using the at least one machine-learning algorithm, an initial set of attributes based on the user characteristic, each attribute of the initial set of attributes being used to determine whether users correspond to the user characteristic, and at least one attribute of the initial set of attributes characterizing an engagement based on at least one of: interactions with the ticket management system, interactions with a social media system, membership to one or more groups, past event attendances, and information in user profiles; determining, for each user in a first set of users, a first value for each attribute in the initial set of attributes, the first value being indicative of an extent of the engagement; identifying a first group and a second group from amongst the first set of users based on the determined first values; identifying a collection of users previously determined to correspond to the user characteristic; automatically detecting whether a rate of increase of users in the collection of users meets or exceeds a threshold, the meeting or exceeding the threshold indicating a likelihood that one or more users have altered activity to cause the ticket management system to falsely characterize the one or more users as a human user; in response to detecting that the rate of increase meets or exceeds the threshold, automatically determining to modify the initial set of attributes, each attribute in the modified set of attributes being used to determine whether users correspond to the user characteristic; determining, for each user in a second set of users, a second value for each attribute in the modified set of attributes, the second value being indicative of an extent to which a user corresponds to the user characteristic; continuously detecting, using the at least one learning algorithm, whether or not at least one user has altered activity to cause the ticket management system to falsely characterize the at least one user; identifying a third group and a fourth group from amongst the second set of users based on the determined second values, wherein each user in the third group corresponds to the defined user characteristic, wherein each user in the fourth group does not correspond to the user characteristic, and wherein users in the second set of users are different from users in the first set of users; determining that ticket offerings are to be biased to favor users in the third group over users in the fourth group; generating a digital presentation for each user in the second set of users based on the determination that ticket offerings are to be biased to favor users in the third group over users in the fourth group, the digital presentation being different for users in the third group than for users in the fourth group; and transmitting the digital presentation to each user in the second set of users, the digital presentation causing an interface to be displayed when received at a user device associated with each user in the second set of users, the interface being configured to receive ticket requests when the interface is displayed at the user device associated with each user in the third group, and the interface being configured to not receive ticket requests when the interface is displayed at the user device associated with each user in the fourth group.
10. A method for dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users, method comprising: defining a user characteristic, the user characteristic being an estimation that a user is a human user, wherein: when the user corresponds to the user characteristic, the user is estimated to be a human user, and when the user does not correspond to the user characteristic, the user is estimated to be a robot user; identifying a model that enhances the user characteristic definer by detecting when users alter activity to cause the ticket management system to falsely characterize the users, the model including at least one machine-learning algorithm, identifying, using the at least one machine-learning algorithm, an initial set of attributes based on the user characteristic, each attribute of the initial set of attributes being used to determine whether users correspond to the user characteristic, and at least one attribute of the initial set of attributes characterizing an engagement based on at least one of: interactions with the ticket management system, interactions with a social media system, membership to one or more groups, past event attendances, and information in user profiles; determining, for each user in a first set of users, a first value for each attribute in the initial set of attributes, the first value being indicative of an extent of the engagement; identifying a first group and a second group from amongst the first set of users based on the determined first values; identifying a collection of users previously determined to correspond to the user characteristic; automatically detecting whether a rate of increase of users in the collection of users meets or exceeds a threshold, the meeting or exceeding the threshold indicating a likelihood that one or more users have altered activity to cause the ticket management system to falsely characterize the one or more users as a human user; in response to detecting that the rate of increase meets or exceeds the threshold, automatically determining to modify the initial set of attributes, each attribute in the modified set of attributes being used to determine whether users correspond to the user characteristic; determining, for each user in a second set of users, a second value for each attribute in the modified set of attributes, the second value being indicative of an extent to which a user corresponds to the user characteristic; continuously detecting, using the at least one learning algorithm, whether or not at least one user has altered activity to cause the ticket management system to falsely characterize the at least one user; identifying a third group and a fourth group from amongst the second set of users based on the determined second values, wherein each user in the third group corresponds to the defined user characteristic, wherein each user in the fourth group does not correspond to the user characteristic, and wherein users in the second set of users are different from users in the first set of users; determining that ticket offerings are to be biased to favor users in the third group over users in the fourth group; generating a digital presentation for each user in the second set of users based on the determination that ticket offerings are to be biased to favor users in the third group over users in the fourth group, the digital presentation being different for users in the third group than for users in the fourth group; and transmitting the digital presentation to each user in the second set of users, the digital presentation causing an interface to be displayed when received at a user device associated with each user in the second set of users, the interface being configured to receive ticket requests when the interface is displayed at the user device associated with each user in the third group, and the interface being configured to not receive ticket requests when the interface is displayed at the user device associated with each user in the fourth group. 17. The method dynamically selecting attributes indicative of human users, evaluating users and selectively presenting offers to estimated human users as recited in claim 10 , wherein the attributes are indicative as to whether the users are fans of a performing entity.