Comments - The Real Facebook Controversy - Data Science Central2021-03-04T14:49:40Zhttps://www.datasciencecentral.com/profiles/comment/feed?attachedTo=6448529%3ABlogPost%3A720734&xn_auth=noI saw this comment in my emai…tag:www.datasciencecentral.com,2018-05-10:6448529:Comment:7212202018-05-10T21:00:50.518ZPeter Brucehttps://www.datasciencecentral.com/profile/PeterBruce
<p>I saw this comment in my email:</p>
<p><span>Can someone explain the application of Benford's Law? What does "conforming data set" mean and how are the fake accounts and the significant digits related? Thanks!</span></p>
<p><span>Response: A conforming data set is one whose frequency distribution of first significant digit follows the Benford Law distribution. Actually, "conforming sources or types of data" might be a better way to put it. For example, river lengths follow the law - all…</span></p>
<p>I saw this comment in my email:</p>
<p><span>Can someone explain the application of Benford's Law? What does "conforming data set" mean and how are the fake accounts and the significant digits related? Thanks!</span></p>
<p><span>Response: A conforming data set is one whose frequency distribution of first significant digit follows the Benford Law distribution. Actually, "conforming sources or types of data" might be a better way to put it. For example, river lengths follow the law - all rivers, in general. Golbeck found that, in general, the "friend count" distribution on FB and Twitter follows the law as well - with the exception of a set of accounts that all turned out to be the Russian trolls. In other words, the fact that these were fake troll accounts, and not organically-created accounts, resulted in them not following Benford's law.</span></p>
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