In her research study,\u00a0Words that Have Changed History, Or Modeling the Dynamics of Linguistic Changes,<\/em> Marciej Eder explores how the frequency and acceleration of language has changed over time. She does this through a form of graphical trend lines that helps to model the ways in which the researchers have measured language changing over time. Although this is fascinating, I do believe there is a fundamental error when collecting this type of data. Eder writes, “More important, however, is the fact that the scores are not even:\u00a0the signal becomes stronger in some periods, clearly indicating an acceleration of the language change.” In order to identify these changes, the researchers rely on their own judgement when relating historical events to the acceleration of language. Biased data is flawed and unreliable because that allows for researchers and other experimenters to frame data and other elements of an experiment to their own liking. In addition, I also believe that the researchers require more specificity when testing language change over time. As NL states in their post, “this becomes very relevant because of the ambiguity of the word selection. The study could have chose completely different words and may have formed a different conclusion.” The trend lines created were based off of the frequency of mostly “common function words”, causing a lot of unclear data.<\/p>\n","protected":false},"excerpt":{"rendered":"

In her research study, Words that Have Changed History, Or Modeling the Dynamics of Linguistic Changes, Marciej Eder explores how the frequency and acceleration of language has changed over time. She does this through a form of graphical trend lines that helps to model the ways in which the researchers have measured language changing over time. … <\/p>\n

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