The contrast between the two chapters was very interesting, as McQuail explained audience measurement in a time when social media was young and Webster came in to apply those principals to the modern day. I loved when McQuail said in his conclusion that (emphasis added) "With all the developments of research technique, there can never be more than a very approximate estimate of who was (or is being) reached, where, and under what circumstances and in what state of mind," because I actively disagreed but then after reading Webster's thoughts found myself aligning closer with the statement. Initially, I thought that with all the resources for data collection we have today of COURSE we can get more than an approximate estimate of our audience.
Webster made me think more about overgeneralization of data and audience information, especially when you look at how interactive things are today. Popularity and Personalization Bias are the two things that made me rethink my initial argument. When Webster wrote how "predications about social activity can affect the thing they are predicting" (p. 93) I realized just how complex new media audience measurement can be. I used to be a fan of the "most read" lists on news web sites but now see how they can loop through audience herding. Should news sites take those features off to encourage users to find their own news of interest? Personalization Bias seems like just a modern but veiled version of the celebrity endorser, and makes me question the metrics used to show us what is trending.
These chapters have got me thinking a lot more about how deep audience measurement really goes and how less organic it can be versus what i thought it was. Audience fragmentation, overgeneralization and popularity/personalization bias all have me leaning much closer to agreeing that we won't likely be able to obtain more than the approximate audience estimate that McQuail described.
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