Webster's chapters
include perspectives from the field of advertising which makes it interesting
to read. He also discusses recent modern platforms like Facebook, Twitter and
YouTube, making the readings applicable and relate-able to audience measurement today.
Amongst the many
audience measurement tools that exist on social media, some give head-counts,
some shows trends, while others rank. I have recently encountered data mining
technology that I would consider as audience measurement. Data mining collects
users' conversation being generated around a phenomenon, news, etc. Instead of
offering a simple number of click-through, a ranking of the most popular events
or trends, it gives the researcher a collection of all the things that have
been said around an event from a certain period. For example, if there is an
earthquake, people will tweet about it. Because so many people are tweeting
about it, audience measurement tools mentioned in the book will tell you that a
trendy topic that is being discussed right now on social media is about the
earthquake. However, data mining can help you see every conversation that has
been made under that topic. I think this is more helpful than rankings and
head-counts when one is trying to understand the audience.
Also, the topic of
personalized recommendation and data collection brings the issue of privacy.
Previously users, especially older generations, showed strong dislike for
personalized recommendation due to the invasion of privacy. It seems that such
strong feelings toward it has been somewhat mollified as time as gone by. Free
data collection has become a norm but people are not aware that their
information is being collected let alone being provided willingly by
themselves. For example, Facebook's algorithm called "edge-rank"
which goes into your newsfeed, sees all of your friends, ties, etc. to make a
personalized recommendation should give people the option to opt out.
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