Abstract: This disclosure describes techniques for improving user experience when servicing on-demand requests by a consumer for a hyper-specific media content in a network environment. The hyper-specific media content may include specified audio, video, etc. that relates to hyper-specific criteria (e.g., specific subject, location, and time condition). In an embodiment, the hyper-specific criteria in a consumer request are compared with stored hyper-specific criteria in a database. A prediction model is then trained on feature parameters of matching stored hyper-specific criteria to generate a trending level classification that can be associated with the hyper-specific criteria in the consumer request. The trending level classification indicates level of interest of third parties and may be used as a basis for content management.

Abstract: This disclosure describes techniques for servicing on-demand requests by a consumer for a hyper-specific media content in a network environment. The hyper-specific media content may include specified audio, video, other media, or multi-media combination content that relates to hyper-specific criteria. The hyper-specific criteria cover a specifically requested subject and/or object at a specific location, for a specific time, and for a specific future context or event sufficient for a third-party to author media content to capture that context or event. In an example embodiment, the hyper-specific criteria may be used as an authoring qualification to identify specific content creator devices that may capture the particular or future events.