Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to determine total audience ratings. An example apparatus includes metrics generator circuitry to generate a first audience size for media accessed by first devices of a first media platform at a first level of aggregation, the first level of aggregation corresponding to the media accessed on a first television network and on a first website, generate a second audience size for the media accessed by the first devices of the first media platform at a second level of aggregation, the second level of aggregation corresponding to the media accessed on the first television network and accessed on the first website and a second website, comparator circuitry to compare the first audience size to the second audience size, adjustor circuitry to reduce the first audience size based on the second audience size, and audience determination circuitry to determine a total audience size.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to determine total audience ratings. An example apparatus includes metrics generator circuitry to generate a first audience size for media accessed by first devices of a first media platform at a first level of aggregation, the first level of aggregation corresponding to the media accessed on a first television network and on a first website, generate a second audience size for the media accessed by the first devices of the first media platform at a second level of aggregation, the second level of aggregation corresponding to the media accessed on the first television network and accessed on the first website and a second website, comparator circuitry to compare the first audience size to the second audience size, adjustor circuitry to reduce the first audience size based on the second audience size, and audience determination circuitry to determine a total audience size.

Abstract: Example methods and apparatus to onboard return path data providers for audience measurement are disclosed herein. Example apparatus disclosed herein to predict return path data quality include a classification engine to compute a first data set of model features from validation tuning data reported from media metering devices and a second data set of model features from return path data reported from return path data devices. The example apparatus also include a prediction engine to train a machine learning algorithm based on the first data set, apply the trained machine learning algorithm to the second data set to predict quality of the return path data reported from the return path data devices, and determine an onboarding status for a return path data provider based on an aggregate predicted quality of the return path data reported from the return path data devices.

Abstract: Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes processor circuitry to execute computer readable instructions to at least: identify a candidate household from a plurality of second households to associate with a first household based on an analysis of a first duration of time first media was presented by a first media presentation device and a second duration of time second media was presented by second media presentation devices; match different ones of first panelists of the first household with matching ones of second panelists of the candidate household; and impute respective portions of the first duration of time to the different ones of the first panelists based on portions of the second duration of time for which the matching ones of the second panelists of the candidate household were exposed to the second media.

Abstract: Example methods, apparatus, systems and articles of manufacture to implement an addressable measurement framework are disclosed. Example apparatus disclosed herein perform a common homes analysis of provider data and panel data to determine a coverage footprint associated with the provider data, the provider data including at least one of return path data reported by a plurality of set-top boxes or automatic content recognition data reported by a plurality of smart media devices, and the panel data reported by media device meters. Disclosed example apparatus also weight a portion of the provider data based on the common homes analysis, weight a portion of the panel data based on the common homes analysis, and calculate an addressable advertisement rating based on the weighted portion of the provider data and the weighted portion of the panel data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for editing tuning data collected via automated content recognition. Examples include determining whether a time conflict exists between first tuning data corresponding to a first tuning event and second tuning data corresponding to a second tuning event. Examples also include that, in response to determining that the time conflict exists, creating a third tuning event based on the first tuning data, the second tuning data, and one or more criteria. Examples also include that modifying at least one of the first tuning event or the second tuning event based on the third tuning event. Examples also include that crediting a media presentation by the presentation device based on edited tuning data, the edited tuning data including the first modified tuning event, the second modified tuning event, and the third tuning event.

Abstract: Methods, apparatus, systems, and articles of manufacture for reconciliation of commercial measurement ratings for non-return path data media devices are disclosed. Example apparatus disclosed herein are to estimate unreported addressable impressions for a plurality of unreported households for an addressable advertisement based on an impressions adjustment ratio of served reportable addressable impressions to exposed reported addressable impressions included in impressions data associated with reported households. Disclosed example apparatus are further to calculate at least one of reach or frequency for the addressable advertisement to account for non-reporting devices, the at least one of the reach or the frequency determined based on the exposed reported addressable impressions, the estimated unreported addressable impressions, and the impressions adjustment ratio.

Abstract: Example apparatus disclosed herein include a return path data classifier to classify a first viewing period associated with segments of return path data received from a set top box into tuning classifications based on the segments of the return path data; calculate a total reported tuning duration for the first viewing period when the first viewing period is classified as live or playback tuning; and compare the total reported tuning duration to a duration threshold to determine whether the segments of return path data associated with the first viewing period are valid. The example apparatus also includes a return path data rectifier to rectify missing tuning data associated with a second viewing period based on tuning data included in the segments of return path data associated with the first viewing period when the segments of the return path data associated with the first viewing period are determined to be valid.

Abstract: Example methods, apparatus, systems and articles of manufacture to implement an addressable measurement framework are disclosed. Example apparatus disclosed herein perform a common homes analysis of provider data and panel data to determine a coverage footprint associated with the provider data, the provider data including at least one of return path data reported by a plurality of set-top boxes or automatic content recognition data reported by a plurality of smart media devices, and the panel data reported by media device meters. Disclosed example apparatus also weight a portion of the provider data based on the common homes analysis, weight a portion of the panel data based on the common homes analysis, and calculate an addressable advertisement rating based on the weighted portion of the provider data and the weighted portion of the panel data.

Abstract: An example apparatus includes an advertisement determiner to identify a first plurality of respondents that received an addressable advertisement and a second plurality of respondents that received a linear advertisement based on combined program tuning data and reference advertisement data; a calculator to calculate a first average commercial minute rating for the addressable advertisement based on first duration weighted impressions associated with the first plurality of respondents and a second average commercial minute rating for the linear advertisement based on second duration weighted impressions associated with the second plurality of respondents; and a communication interface to transmit the first average commercial minute rating and the second average commercial minute rating for crediting the addressable advertisement and the linear advertisement with audience viewership metrics.

Abstract: Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes processor circuitry to execute computer readable instructions to at least: identify a candidate household from a plurality of second households to associate with a first household based on an analysis of a first duration of time first media was presented by a first media presentation device and a second duration of time second media was presented by second media presentation devices; match different ones of first panelists of the first household with matching ones of second panelists of the candidate household; and impute respective portions of the first duration of time to the different ones of the first panelists based on portions of the second duration of time for which the matching ones of the second panelists of the candidate household were exposed to the second media.

Abstract: Example apparatus disclosed herein include a return path data classifier to classify a first viewing period associated with segments of return path data received from a set top box into tuning classifications based on the segments of the return path data; calculate a total reported tuning duration for the first viewing period when the first viewing period is classified as live or playback tuning; and compare the total reported tuning duration to a duration threshold to determine whether the segments of return path data associated with the first viewing period are valid. The example apparatus also includes a return path data rectifier to rectify missing tuning data associated with a second viewing period based on tuning data included in the segments of return path data associated with the first viewing period when the segments of the return path data associated with the first viewing period are determined to be valid.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying, by executing an instruction with a processor, in the return path data, first tuning data corresponding to a first group of set top boxes, the first group of set top boxes classified as associated with machine events, determining, by executing an instruction with a processor, a ratio between first tuning events in the return path data and second tuning events in the return path data, the first tuning events attributed to the first group of the set top boxes, the second tuning events attributed to a second group of the set top boxes classified at not associated with machine events, and in response to the ratio satisfying a threshold during a time interval, removing second tuning data associated with the time interval from the first tuning data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for editing tuning data collected via automated content recognition. Examples include determining whether a time conflict exists between first tuning data corresponding to a first tuning event and second tuning data corresponding to a second tuning event. Examples also include that, in response to determining that the time conflict exists, creating a third tuning event based on the first tuning data, the second tuning data, and one or more criteria. Examples also include that modifying at least one of the first tuning event or the second tuning event based on the third tuning event. Examples also include that crediting a media presentation by the presentation device based on edited tuning data, the edited tuning data including the first modified tuning event, the second modified tuning event, and the third tuning event.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed. An apparatus includes at least one memory, instructions, and processor circuitry to execute the instructions. The processor circuitry executes the instructions to deduplicate at least one of panel data, automatic content recognition data, or return path data to generate a deduplicated data set. The processor circuitry executes the instructions to generate a graph that connects devices identified in the deduplicated data set, the devices connected based on at least one of a shared intemet protocol addresses, a shared location identifier, or a common device identifier assigned by a common device identification algorithm. The processor circuitry executes the instructions to remove one or more connections based on an inconsistency between the one or more connections and personally identifiable information.

Abstract: Example methods, apparatus, systems and articles of manufacture to implement under-addressable advertisement measurement are disclosed. Example apparatus disclosed herein determine a tuned under-addressable advertisement duration and a total tuned advertisement duration for a first television broadcast at a first monitored site, the tuned under-addressable advertisement duration and the total tuned advertisement duration determined based on (i) return-path tuning data associated with the first monitored site, (ii) linear advertisement log data associated with a broadcast area including the first monitored site, and (iii) addressable advertisement log data associated with the first monitored site.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying, by executing an instruction with a processor, in the return path data, first tuning data corresponding to a first group of set top boxes, the first group of set top boxes classified as associated with machine events, determining, by executing an instruction with a processor, a ratio between first tuning events in the return path data and second tuning events in the return path data, the first tuning events attributed to the first group of the set top boxes, the second tuning events attributed to a second group of the set top boxes classified at not associated with machine events, and in response to the ratio satisfying a threshold during a time interval, removing second tuning data associated with the time interval from the first tuning data.

Abstract: Example methods, apparatus, systems and articles of manufacture to implement an addressable measurement framework are disclosed. Example apparatus disclosed herein perform a common homes analysis of provider data and panel data to determine a coverage footprint associated with the provider data, the provider data including at least one of return path data reported by a plurality of set-top boxes or automatic content recognition data reported by a plurality of smart media devices, and the panel data reported by media device meters. Disclosed example apparatus also weight a portion of the provider data based on the common homes analysis, weight a portion of the panel data based on the common homes analysis, and calculate an addressable advertisement rating based on the weighted portion of the provider data and the weighted portion of the panel data.

Abstract: Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes processor circuitry to execute computer readable instructions to at least: identify a candidate household from a plurality of second households to associate with a first household based on an analysis of a first duration of time first media was presented by a first media presentation device and a second duration of time second media was presented by second media presentation devices; match different ones of first panelists of the first household with matching ones of second panelists of the candidate household; and impute respective portions of the first duration of time to the different ones of the first panelists based on portions of the second duration of time for which the matching ones of the second panelists of the candidate household were exposed to the second media.

Abstract: Methods, apparatus, systems and articles of manufacture to determine whether a smart device is in-tab are disclosed. An example apparatus includes memory; instructions in the apparatus; and processor circuitry to execute the instructions to: provide smart television data from a smart television as an input to a model to generate an output, the smart television data being included in population data from a population of smart televisions; determine a tab status of the smart television based on the output; in response to the tab status of the smart television being out-of-tab, remove the smart television data from the population data; and credit media based on the population data.