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How antifraud works. The story of Russian bots, auction thieves and stolen billions.
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<blockquote data-quote="Carders" data-source="post: 642" data-attributes="member: 17"><p>A beautiful example of using Benford's law to detect deception is provided by the recent work of Jennifer Golbeck, a well-known expert in the field of social network analysis. She showed that it can be used to expose bots — fake accounts on Facebook or Twitter.</p><p></p><p><img src="https://xakep.ru/wp-content/uploads/2016/01/1453177947_1b1d_golbeck.png" alt="Jennifer Golbeck" class="fr-fic fr-dii fr-draggable " style="" /></p><p></p><p><em>Jennifer Golbeck</em></p><p></p><p>Golbeck began by studying data sets on subsets of users on five major social networks: Facebook, Twitter, Google+, Pinterest и LiveJournal. In most cases, user data was extracted using the software interface of the corresponding social network. The exceptions were Google+ and LiveJournal. Information about their users was borrowed from the Stanford Network Analysis Project.</p><p></p><p>First, the researcher checked the number of links between accounts in each social network. As expected, these values coincided with the indicators predicted by Benford's law. An exception is Pinterest: when creating an account, the service adds five links automatically, and this spoils all statistics.</p><p></p><p>Golbeck then began analyzing individual accounts. She selected those that have at least a hundred social connections. It turned out that the distribution of the first significant digits of the number of "friends" in the accounts to which these connections lead almost always fits into Benford's law. For example, in the Twitter data set, a significant deviation was observed only in 1% of cases.</p><p></p><p>And what is this percentage? Golbeck checked 170 Twitter accounts that do not comply with the Benford law, and found that only two of them are not suspicious. The vast majority of the rest turned out to be Russian bots. These accounts are very similar to each other: the user's photo is clearly borrowed from the photo bank, the tweets themselves are meaningless fragments of book quotes, and friends are other bots. They disguise themselves as ordinary people, but Benford's law easily reveals their artificiality.</p><p></p><h4>Outro</h4><p>In one short article, it is impossible to list (and even more so explain) all the methods for detecting anomalies that are useful in hunting for online scammers. But such a goal is not worth it — this is not an " Anti-fraud for dummies "(such a book, by the way, exists). If you want to dive deeper into the topic, then the best way is to read academic publications.</p><p></p><p><a href="https://scholar.google.ru/" target="_blank">Scholar.google.com</a> it will help you find them. And then-himself.</p><p></p><p>(c) <a href="https://xakep.ru/2016/01/19/how-antifraud-works/" target="_blank">https://xakep.ru/2016/01/19/how-antifraud-works/</a></p></blockquote><p></p>
[QUOTE="Carders, post: 642, member: 17"] A beautiful example of using Benford's law to detect deception is provided by the recent work of Jennifer Golbeck, a well-known expert in the field of social network analysis. She showed that it can be used to expose bots — fake accounts on Facebook or Twitter. [IMG alt="Jennifer Golbeck"]https://xakep.ru/wp-content/uploads/2016/01/1453177947_1b1d_golbeck.png[/IMG] [I]Jennifer Golbeck[/I] Golbeck began by studying data sets on subsets of users on five major social networks: Facebook, Twitter, Google+, Pinterest и LiveJournal. In most cases, user data was extracted using the software interface of the corresponding social network. The exceptions were Google+ and LiveJournal. Information about their users was borrowed from the Stanford Network Analysis Project. First, the researcher checked the number of links between accounts in each social network. As expected, these values coincided with the indicators predicted by Benford's law. An exception is Pinterest: when creating an account, the service adds five links automatically, and this spoils all statistics. Golbeck then began analyzing individual accounts. She selected those that have at least a hundred social connections. It turned out that the distribution of the first significant digits of the number of "friends" in the accounts to which these connections lead almost always fits into Benford's law. For example, in the Twitter data set, a significant deviation was observed only in 1% of cases. And what is this percentage? Golbeck checked 170 Twitter accounts that do not comply with the Benford law, and found that only two of them are not suspicious. The vast majority of the rest turned out to be Russian bots. These accounts are very similar to each other: the user's photo is clearly borrowed from the photo bank, the tweets themselves are meaningless fragments of book quotes, and friends are other bots. They disguise themselves as ordinary people, but Benford's law easily reveals their artificiality. [HEADING=3]Outro[/HEADING] In one short article, it is impossible to list (and even more so explain) all the methods for detecting anomalies that are useful in hunting for online scammers. But such a goal is not worth it — this is not an " Anti-fraud for dummies "(such a book, by the way, exists). If you want to dive deeper into the topic, then the best way is to read academic publications. [URL='https://scholar.google.ru/']Scholar.google.com[/URL] it will help you find them. And then-himself. (c) [URL]https://xakep.ru/2016/01/19/how-antifraud-works/[/URL] [/QUOTE]
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