Fusing online media monitoring data with secondary online data feeds to generate ratings data for online media exposure
Methods, apparatus, systems, storage media, etc., to fuse online media monitoring data with secondary online data feeds to generate ratings data for online media exposure are disclosed. Example methods disclosed herein to determine ratings data for online media exposure include accessing timestamps included in monitoring data obtained by monitoring exposure to online media. Such example methods also include searching a secondary online data feed using the timestamps included in the monitoring data for data entries associated with exposure to the online media. Such example methods further include fusing the data entries from the secondary online data feed with the monitoring data to generate the ratings data for online media exposure.
1. A method to determine ratings data for online media exposure, the method comprising:
accessing, by executing an instruction with a processor, monitoring data from at least one of monitoring applications executed by respective ones of a group of online computing platforms or monitoring devices in communication with the respective ones of the group of online computing platforms, the at least one of the monitoring applications or the monitoring devices to monitor online media exposure associated with the group of online computing platforms, the monitoring data including timestamps and instances of a first media identifier associated with respective ones of the timestamps, the respective ones of the timestamps indicating when first streaming media identified by the first media identifier was at least one of presented or accessed via the group of online computing platforms;
selecting first data entries from a first online data feed having entry times corresponding to the respective ones of the timestamps associated with the first media identifier in the monitoring data;
selecting second data entries from the first data entries based on the first media identifier;
combining the monitoring data and the second data entries to determine first ratings data characterizing exposure to the first streaming media identified by the first media identifier;
parsing a second online data feed, different from the first online data feed, based on at least one keyword or phrase to identify one or more third data entries corresponding to one or more news or weather alerts associated with the at least one keyword or phrase; and
presenting the one or more third data entries aligned in time with the first ratings data to depict whether the one or more news or weather alerts corresponding to the one or more third data entries are correlated with changes in a time varying audience of the first streaming media.
2. The method as defined in claim 1 , wherein the first online data feed includes a social media feed, the selecting of the second data entries includes selecting the first data entries having content corresponding to the first streaming media identified by the first media identifier to be the second data entries, and the combining of the monitoring data and the second data entries includes:
using the monitoring data to determine the time varying audience of the first streaming media;
using the second data entries to determine a time varying social impact of the first streaming media; and
aligning values of the time varying audience with corresponding values of the time varying social impact based on the timestamps included in the monitoring data.
3. The method as defined in claim 2 , wherein the using of the second data entries to determine the time varying social impact of the first streaming media includes:
determining, for a first one of the timestamps associated with the first media identifier, a first number of distinct social media users that authored a first subset of the second data entries corresponding to the first one of the timestamps; and
determining, for a second one of the timestamps associated with the first media identifier, a second number of distinct social media users that authored a second subset of the second data entries corresponding to the second one of the timestamps.
4. The method as defined in claim 3 , wherein the using of the second data entries to determine the time varying social impact of the first streaming media further includes:
combining metrics characterizing respective reaches of respective ones of the social media users in the first number of distinct social media users to determine a first value characterizing social media reach of the first streaming media at a first time corresponding to the first one of the timestamps associated with the first media identifier; and
combining metrics characterizing respective reaches of respective ones of the social media users in the second number of distinct social media users to determine a second value characterizing the social media reach of the first streaming media at a second time corresponding to the second one of the timestamps associated with the first media identifier.
5. The method as defined in claim 2 , wherein the using of the second data entries to determine the time varying social impact of the first streaming media includes:
combining metrics characterizing feedback to respective ones of a first subset of the second data entries corresponding to a first one of the timestamps to determine a first value characterizing social media response to the first streaming media at a first time associated with the first one of the timestamps; and
combining metrics characterizing feedback to respective ones of a second subset of the second data entries corresponding to a second one of the timestamps to determine a second value characterizing social media response to the first streaming media at a second time associated with the second one of the timestamps.
6. A non-transitory computer readable medium comprising machine readable instructions that, when executed, cause a machine to at least:
access monitoring data from at least one of monitoring applications executed by respective ones of a group of online computing platforms or monitoring devices in communication with the respective ones of the group of online computing platforms, the at least one of the monitoring applications or the monitoring devices to monitor online media exposure associated with the group of online computing platforms, the monitoring data including timestamps and instances of a first media identifier associated with respective ones of the timestamps, the respective ones of the timestamps indicating when first streaming media identified by the first media identifier was at least one of presented or accessed via the group of online computing platforms;
select first data entries from a first online data feed having entry times corresponding to the respective ones of the timestamps associated with the first media identifier in the monitoring data;
select second data entries from the first data entries based on the first media identifier;
combine the monitoring data and the second data entries to determine first ratings data characterizing exposure to the first streaming media identified by the first media identifier;
parse a second online data feed, different from the first online data feed, based on at least one keyword or phrase to identify one or more third data entries corresponding to one or more news or weather alerts associated with the at least one keyword or phrase; and
present the one or more third data entries aligned in time with the first ratings data to depict whether the one or more news or weather alerts corresponding to the one or more third data entries are correlated with changes in a time varying audience of the first streaming media.
7. The non-transitory computer readable medium as defined in claim 6 , wherein the first online data feed includes a social media feed, wherein to select the second data entries, the machine readable instructions, when executed, further cause the machine to select the first data entries having content corresponding to the first streaming media identified by the first media identifier to be the second data entries, and wherein to combine the monitoring data and the second data entries, the machine readable instructions, when executed, further cause the machine to:
use the monitoring data to determine the time varying audience of the first streaming media;
use the second data entries to determine a time varying social impact of the first streaming media; and
align values of the time varying audience with corresponding values of the time varying social impact based on the timestamps included in the monitoring data.
8. An apparatus to determine ratings data for online media exposure, the apparatus comprising:
at least one logic circuit;
a secondary feed data searcher implemented by the at least one logic circuit to:
access monitoring data from at least one of monitoring applications executed by respective ones of a group of online computing platforms or monitoring devices in communication with the respective ones of the group of online computing platforms, the at least one of the monitoring applications or the monitoring devices to monitor online media exposure associated with the group of online computing platforms, the monitoring data including timestamps and instances of a first media identifier associated with respective ones of the timestamps, the respective ones of the timestamps indicating when first streaming media identified by the first media identifier was at least one of presented or accessed via the group of online computing platforms;
select first data entries from a first online data feed having entry times corresponding to the respective ones of the timestamps associated with the first media identifier in the monitoring data; and
select second data entries from the first data entries based on the first media identifier; and
a ratings data generator implemented by the at least one logic circuit to:
combine the monitoring data and the second data entries to determine first ratings data characterizing exposure to the first streaming media identified by the first media identifier;
parse a second online data feed, different from the first online data feed, based on at least one keyword or phrase to identify one or more third data entries corresponding to one or more news or weather alerts associated with the at least one keyword or phrase; and
present the one or more third data entries aligned in time with the first ratings data to depict whether the one or more news or weather alerts corresponding to the one or more third data entries are correlated with changes in a time varying audience of the first streaming media.
9. The apparatus as defined in claim 8 , wherein the first online data feed includes a social media feed, the secondary feed data searcher is to select the second data entries by selecting the first data entries having content corresponding to the first streaming media identified by the first media identifier to be the second data entries, and the ratings data generator is to:
use the monitoring data to determine the time varying audience of the first streaming media;
use the second data entries to determine a time varying social impact of the first streaming media; and
align values of the time varying audience with corresponding values of the time varying social impact based on the timestamps included in the monitoring data.
10. The apparatus as defined in claim 9 , wherein the ratings data generator is further to:
determine, for a first one of the timestamps associated with the first media identifier, a first number of distinct social media users that authored a first subset of the second data entries corresponding to the first one of the timestamps; and
determine, for a second one of the timestamps associated with the first media identifier, a second number of distinct social media users that authored a second subset of the second data entries corresponding to the second one of the timestamps.
11. The apparatus as defined in claim 10 , wherein the ratings data generator is further to:
combine metrics characterizing respective reaches of respective ones of the social media users in the first number of distinct social media users to determine a first value characterizing social media reach of the first streaming media at a first time corresponding to the first one of the timestamps associated with the first media identifier; and
combine metrics characterizing respective reaches of respective ones of the social media users in the second number of distinct social media users to determine a second value characterizing the social media reach of the first streaming media at a second time corresponding to the second one of the timestamps associated with the first media identifier.
12. The non-transitory computer readable medium as defined in claim 7 , wherein to use the second data entries to determine the time varying social impact of the first streaming media, the machine readable instructions, when executed, further cause the machine to:
determine, for a first one of the timestamps associated with the first media identifier, a first number of distinct social media users that authored a first subset of the second data entries corresponding to the first one of the timestamps; and
determine, for a second one of the timestamps associated with the first media identifier, a second number of distinct social media users that authored a second subset of the second data entries corresponding to the second one of the timestamps.
13. The non-transitory computer readable medium as defined in claim 12 , wherein to use the second data entries to determine the time varying social impact of the first streaming media, the machine readable instructions, when executed, further cause the machine to:
combine metrics characterizing respective reaches of respective ones of the social media users in the first number of distinct social media users to determine a first value characterizing social media reach of the first streaming media at a first time corresponding to the first one of the timestamps associated with the first media identifier; and
combine metrics characterizing respective reaches of respective ones of the social media users in the second number of distinct social media users to determine a second value characterizing the social media reach of the first streaming media at a second time corresponding to the second one of the timestamps associated with the first media identifier.
14. The non-transitory computer readable medium as defined in claim 7 , wherein to use the second data entries to determine the time varying social impact of the first streaming media, the machine readable instructions, when executed, further cause the machine to:
combine metrics characterizing feedback to respective ones of a first subset of the second data entries corresponding to a first one of the timestamps to determine a first value characterizing social media response to the first streaming media at a first time associated with the first one of the timestamps; and
combine metrics characterizing feedback to respective ones of a second subset of the second data entries corresponding to a second one of the timestamps to determine a second value characterizing social media response to the first streaming media at a second time associated with the second one of the timestamps.
15. The non-transitory computer readable medium as defined in claim 7 , wherein to use the monitoring data to determine the time varying audience of the first streaming media, the machine readable instructions, when executed, further cause the machine to use the instances of the first media identifier and the timestamps included in the monitoring data to determine a time varying number of the online computing platforms that at least one of accessed or presented the first streaming media over a first period of time.
16. The apparatus as defined in claim 9 , wherein the ratings data generator is further to:
combine metrics characterizing feedback to respective ones of a first subset of the second data entries corresponding to a first one of the timestamps to determine a first value characterizing social media response to the first streaming media at a first time associated with the first one of the timestamps; and
combine metrics characterizing feedback to respective ones of a second subset of the second data entries corresponding to a second one of the timestamps to determine a second value characterizing social media response to the first streaming media at a second time associated with the second one of the timestamps.
17. The apparatus as defined in claim 9 , wherein the ratings data generator is further to use the instances of the first media identifier and the timestamps included in the monitoring data to determine a time varying number of the online computing platforms that at least one of accessed or presented the first streaming media over a first period of time.
18. The method as defined in claim 1 , further including:
receiving at least a portion of the monitoring data electronically via a network; and
receiving the first data entries of the first online data feed electronically via the network from a server.
19. The non-transitory computer readable medium as defined in claim 6 , wherein the machine readable instructions, when executed, further cause the machine to:
receive at least a portion of the monitoring data electronically via a network; and
receive the first data entries of the first online data feed electronically via the network from a server.
20. The apparatus as defined in claim 8 , further including a data receiver to:
receive at least a portion of the monitoring data electronically via a network; and
receive the first data entries of the first online data feed electronically via the network from a server.