User Information Regimes

Reading both McQuail (1997) and Webster (2014) provides a comprehensive picture of audience measurement challenges across time. When McQuail wrote his book, he asserted that "only a small fragment of the total of actual audience behavior can ever be measured, and the rest is extrapolation, estimate or guesswork" (p. 49). However, as a result of technological developments, it is now possible to know more about the actual audience behavior and to get information that is disaggregated at the story level, unlike tools such as circulation statistics and surveys, which provide aggregate and indirect information. This information could be helpful in understanding what is commanding public attention, but as Webster pointed out, "all media measures are biased… it means they can never offer a completely objective picture of reality. Bias is inherent in the process of collecting and reducing data" (p. 86). Also, these measures "don't stand apart from the reality they purport to measure; they reshape it" (p. 93). We should remember this when we rely on what Webster calls "user information regimes." We should also think how we can study the effects of these regimes.