Row 7260

Row ID: 7260 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 7260 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

[https://github.com/tomwillcode/Detecting\_Fake\_Accounts](https://github.com/tomwillcode/Detecting_Fake_Accounts)

Accounts impersonating other people (name, photos) are a common thing on social networks these days. In this repo we see a method for detecting these fake accounts with a human out of the loop (for the most part). the method works like this:

1. Map every user to a "unique name identifer" (UNI) so that any unneccessary characters are removed: "Jeff Bezos" -> 'jeffbezos', and 'Real Jeff Bezos' -> 'jeffbezos', and 'jeff\_bezos' -> 'jeffbezos' 2. Merge verified accounts with non-verified accounts on the UNI (inner join). 3. Compare bio, usernames etc., with NLI or another form of NLP to detect evidence for fraud, or conversely good natured tributes 4. Compare pictures using Computer Vision in this case using the DeepFace library

FieldValue
text [https://github.com/tomwillcode/Detecting\_Fake\_Accounts](https://github.com/tomwillcode/Detecting_Fake_Accounts) Accounts impersonating other people (name, photos) are a common thing on social networks these days. In this repo we see a method for detecting these fake accounts with a human out of the loop (for the most part). the method works like this: 1. Map every user to a "unique name identifer" (UNI) so that any unneccessary characters are removed: "Jeff Bezos" -> 'jeffbezos', and '…
label r/datascience
dataType post
communityName r/datascience
datetime 2024-05-15
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Raw Record

{
  "text": "[https://github.com/tomwillcode/Detecting\\_Fake\\_Accounts](https://github.com/tomwillcode/Detecting_Fake_Accounts)\n\nAccounts impersonating other people (name, photos) are a common thing on social networks these days. In this repo we see a method for detecting these fake accounts with a human out of the loop (for the most part).  \n  \nthe method works like this:\n\n1. Map every user to a \"unique name identifer\" (UNI) so that any unneccessary characters are removed: \"Jeff Bezos\" -> 'jeffbezos', and 'Real Jeff Bezos' -> 'jeffbezos', and 'jeff\\_bezos' -> 'jeffbezos'\n2. Merge verified accounts with non-verified accounts on the UNI (inner join).\n3. Compare bio, usernames etc., with NLI or another form of NLP to detect evidence for fraud, or conversely good natured tributes\n4. Compare pictures using Computer Vision in this case using the DeepFace library",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-15",
  "username_encoded": "Z0FBQUFBQm5LakwzT3pFZmlYTWVQTTl0TWNIMTBMTGJaakZfZkdrdmwzcjJ5NlhzMU02Sk1iclpjUEM3ZjZCV3I4ZmJEVEhBS3JKS3pCNGtHZF9ad1FNYUM2VDN1UEM0N2JPMmQ0RUdlNFZXVTJBR0VWbFltaFE9",
  "url_encoded": "Z0FBQUFBQm5Lak9INlVObGxOVUtxYl9Jc3E5cFhtQndrWWRrMElxQjhlUTd0QWZKaHEyZ0c2SkZBM1V1YmN2X1lqM3U4X0sybloxaFVZc3BqVlFkLTJsYlkyYW9iMU9XalFDcm1GWDdUNDdsaFV3MWZRMVpJWWt2VUtmZkl4UDdIZVNMRENJUDZoWl9fSzlsVUdDMHlON0FZRHN4Q01LVEFmY1V1WjNkVUtBMUdyMWdNdGNDNUV1ODhrMkI1VGRLNXFsbzh2TFQwNnpEYjJjbHBBcEFXMnNqS2JCUHlUSVVJdz09"
}
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