7 ms·

Heh, cool stats lesson. Always learn something new on HN. The only thing I didn't catch was how they were to figure out which of the accounts in the sample wer

by jqm 12y ago

Heh, cool stats lesson. Always learn something new on HN. The only thing I didn't catch was how they were to figure out which of the accounts in the sample were fake.

dredmorbius 12y ago
You might have missed: "scoring of accounts based on some sort of activity profile." Fake accounts are almost certainly going to have characteristics which distinguish them from legitimate ones, and likely a quantitative, manual, or combined assessment might determine that. Points to consider: ⚫ Photo images. Search for duplicates or analyze for manipulation. ⚫ Correlation with other data sources. Real names, Social Security, marketing, and numerous databases exist which tend to point to more legitimate profiles. ⚫ Direct contact. Set up events, meet-ups, special purchase offers, etc., or otherwise try to elicit direct action. Even statistically differential response rates are useful. ⚫ Social graph. Real people _interact_ with other real people, and there's a web of trust or certification which can be imputed or determined. ⚫ Network profiles. Identifying large numbers of accounts with similar or suspicious profiles _and_ originating from the same or adjacent network addresses (CIDR/BGP block), or from known nonresidential space that's _not_ a generally-used proxy, would be a strong indicator. If proxy use continues to climb, that's going to be less useful (and I suspect this will be the case). Tests along these or similar lines could be used to generate more general scoring algorithms for identifying suspect accounts.
troels 12y ago
With a small enough volume, you could do manual inspection. I'm guessing most fake accounts are easy to spot by an actual human, putting an effort into it.
- dredmorbius 12y ago
  Pretty much that. 100 accounts could be reviewed by one person pretty easily in a day or few, 1,000 by a small team, specifically trained and applying consistent standards, with cross-checks. Once you've done the manual assessments, deriving heuristics for broader estimates are also possible. The key is having training data of known classified accounts.

by jqm 12y ago

Heh, cool stats lesson. Always learn something new on HN. The only thing I didn't catch was how they were to figure out which of the accounts in the sample were fake.

dredmorbius 12y ago

You might have missed: "scoring of accounts based on some sort of activity profile." Fake accounts are almost certainly going to have characteristics which distinguish them from legitimate ones, and likely a quantitative, manual, or combined assessment might determine that. Points to consider: ⚫ Photo images. Search for duplicates or analyze for manipulation. ⚫ Correlation with other data sources. Real names, Social Security, marketing, and numerous databases exist which tend to point to more legitimate profiles. ⚫ Direct contact. Set up events, meet-ups, special purchase offers, etc., or otherwise try to elicit direct action. Even statistically differential response rates are useful. ⚫ Social graph. Real people _interact_ with other real people, and there's a web of trust or certification which can be imputed or determined. ⚫ Network profiles. Identifying large numbers of accounts with similar or suspicious profiles _and_ originating from the same or adjacent network addresses (CIDR/BGP block), or from known nonresidential space that's _not_ a generally-used proxy, would be a strong indicator. If proxy use continues to climb, that's going to be less useful (and I suspect this will be the case). Tests along these or similar lines could be used to generate more general scoring algorithms for identifying suspect accounts.

troels 12y ago

With a small enough volume, you could do manual inspection. I'm guessing most fake accounts are easy to spot by an actual human, putting an effort into it.

dredmorbius 12y ago
Pretty much that. 100 accounts could be reviewed by one person pretty easily in a day or few, 1,000 by a small team, specifically trained and applying consistent standards, with cross-checks. Once you've done the manual assessments, deriving heuristics for broader estimates are also possible. The key is having training data of known classified accounts.