Short version: you don't just want to flood the number of reports, you want to make it so that someone looking at the data from those reports is incapable of telling what's real data and what's fake data, so they either have to go through the entire dataset, painfully sifting out the real data to get anything from it, or they toss the entire dataset.
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If I were running something like this, I'd have at least four levels of screening to filter out 'fake results', probably more as time went on.
Level 1 screening: famous, notable, or obviously fake names.
Level 2 screening: known VPN providers / multiple reports from the same IP address in a short period of time.
Level 3 screening: If they require an address to be put in, don't just put in '1600 Pennsylvania Ave, DC' or use the same address every time.
Level 4 screening: looking for words in the responses/addresses/names that indicate someone is against what this database is set up to do, including excluding records with phrases like 'pro-choice', 'right to choose', 'my body, my choice', any and every variation of a swearword, and a large number of exclamation marks or non-alphanumerics in the text.
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timeasmymeasure
no subject
Date: 2021-08-26 04:58 am (UTC)--
If I were running something like this, I'd have at least four levels of screening to filter out 'fake results', probably more as time went on.
Level 1 screening: famous, notable, or obviously fake names.
Level 2 screening: known VPN providers / multiple reports from the same IP address in a short period of time.
Level 3 screening: If they require an address to be put in, don't just put in '1600 Pennsylvania Ave, DC' or use the same address every time.
Level 4 screening: looking for words in the responses/addresses/names that indicate someone is against what this database is set up to do, including excluding records with phrases like 'pro-choice', 'right to choose', 'my body, my choice', any and every variation of a swearword, and a large number of exclamation marks or non-alphanumerics in the text.