Methods and apparatus to detect spillover are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to: identify a quantity of first durations of loudness in an audio signal of media; calculate a ratio of the quantity of the first durations of loudness to a quantity of second durations of loudness in the audio signal of the media, the quantity of the second durations of loudness including the quantity of the first durations of loudness; and in response to a detection of the audio signal being spillover, store data denoting the media as un-usable to credit a media exposure when the ratio does not satisfy a loudness ratio threshold, the storing of the data to improve an accuracy of media exposure credits by not crediting spillover media.

Methods and apparatus to model on/off states of media presentation devices based on return path data are disclosed. An apparatus includes a memory and processor circuitry to execute instructions stored in the memory to: generate a first probability distribution indicative of actual durations of panel tuning segments, the panel tuning segments corresponding to time periods during which panelists were exposed to first media; generate a second probability distribution indicative of modelled durations of modelled tuning segments, the modelled tuning segments corresponding to modified lengths of the panel tuning segments; and estimate a set-on time for a media set associated with an RPD device based on RPD tuning information and the first and second probability distributions, the RPD tuning information reported from the RPD device, the RPD tuning information indicative of a reported RPD tuning segment during which the RPD device was accessing second media.

An example to predict demographics for impressions includes a prediction manager to: determine that first demographic data corresponding to a first database proprietor subscriber does not match second demographic data corresponding to a media delivery device, both the first and second demographic data corresponding to an impression; obtain third demographic data corresponding to an Internet protocol address, the third demographic data obtained from a second database proprietor; and generate matched demographic data based on comparing the third demographic data to the first demographic data; and a modeler to generate a prediction model based on the matched demographic data, the prediction model to predict fourth demographic data for the impression.

Methods, apparatus, systems and articles of manufacture to identify and triage digital ad ratings data quality issues are disclosed. An example apparatus includes score calculation circuitry to: generate one or more aggregate factor scores based on aggregate data from a first impression data point; generate one or more daily factor scores based on daily data from the from impression data point; normalize the one or more aggregate factor scores based on aggregate factor scores of at least a second data impression point; normalize the one or more daily factor scores based on daily factor scores of at least a second data impression point; calculate a final weight score for the first impression data point using the aggregate factor scores and the daily factor scores for the first impression data point; and flag the final weight score if it does not satisfy a threshold score.

In one aspect, a method includes while a first content-presentation device is performing a content-replacement operation in which the first content-presentation device outputs a replacement advertisement segment in place of an advertisement segment, determining, by a computing system, that the advertisement segment is not an expected modifiable advertisement segment. The method also includes in response to determining that the advertisement segment is not the expected modifiable advertisement segment, determining, by the computing system, that the advertisement segment is ineligible for replacement The method also includes in response to determining that the advertisement segment is ineligible for replacement, causing, by the computing system, at least one content-presentation device to perform, at a subsequent content-replacement opportunity, a corrective content-replacement operation in which the at least one content-presentation device outputs the advertisement segment in place of a subsequent modifiable advertisement segment that the at least one content-presentation device is scheduled to receive.

Systems, devices, media, and methods are presented for identifying emerging viewing patterns for visual media such as still images and videos. Emerging viewing patterns are identified by identifying visual tags for visual media viewed by users, selecting a subset of the tags by applying a taxonomy-based filter, generating pattern candidates from the subset, evaluating consumption metrics for each of the generated patterns, and ranking the generated pattern candidates responsive to the consumption metrics to identify emerging viewing patterns for the users.

The present disclosure relates to reducing or preventing ad fatigue in a user by determining the preference of a user to an ad, in particular user preference for parameters such as video track, audio track, dialogue or tone. The disclosure also relates to the provision of a timer that prevents an ad being shown repeatedly within a predetermined time frame.

Disclosed examples determine a duplicated audience size representative of panelists exposed to television media and digital media; determine a panel duplication reach based on the duplicated audience size and a panelist population; determine a did-not-view reach based on a television audience size, a digital audience size, the duplicated audience size, and the panelist population; obtain an overlap multiplier as a ratio of (1) a product of the panel duplication reach and the did-not-view reach and (2) a product of a television panel reach and a digital panel reach; determine a duplication factor for a media item based on a television audience reach, a digital audience reach, and the overlap multiplier; and determine a total audience for the media item based on the television audience reach, the digital audience reach, and the duplication factor.

Aspects of the subject disclosure may include, for example, obtaining one or more signals, the one or more signals being based upon brain activity of a viewer while the viewer is viewing media content; predicting, based upon the one or more signals, a first predicted desired viewport of the viewer; obtaining head movement data associated with the media content; predicting, based upon the head movement data, a second predicted desired viewport of the viewer; comparing the first predicted desired viewport to the second predicted desired viewport, resulting in a comparison; and determining, based upon the comparison, to use the first predicted desired viewport to facilitate obtaining a first subsequent portion of the media content or to use the second predicted desired viewport to facilitate obtaining a second subsequent portion of the media content. Other embodiments are disclosed.

Methods and apparatus to estimate demographics of a household are disclosed. An example method to determine demographics for non-panelist households includes calculating a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first tuning event of a household and a second demographic distribution of a second tuning event of the household. The household is a non-panelist household. The example method also includes, based on the first demographic constraint average, determining a first likelihood of the household being associated with a first demographic constraint. The example method also includes, based on the second demographic constraint average, determining a second likelihood of the household being associated with a second demographic constraint. The example method also includes estimating a household characteristic of the household based on the first likelihood and the second likelihood.