41–50 of 54 posts
Earlier quoted context omitted.
There's a big difference if 25% of your plates are accidentally broken in shipping vs being smashed with a hammer in the warehouse.
Your 'big difference' is _intent_, a flighty, volatile emotion that no rational person will ever care to account for.
Many people are asking why it's a problem, why don't advertisers just pay less, etc. The adbots are making money off of this, otherwise they wouldn't do it, and that money has to come from somewhere . Meanwhile, they don't contribute anything of value, so someone 's losing money to the adbots. The burden of the loss will actually be distributed between the ad buyers and ad space sellers depending on their elasticity.…
I think a clearer way to put it is this: The 23% number was measured across networks displaying ads on many sites. Some of those sites employ botnets to generate fake views, giving them much more than 23% fraudulent views, while other sites that don't do such things have 0% fraudulent views. The amount the advertiser is willing to pay is presumably set by their experience across the whole network, so if they were wil…
Advertisers, for example, should demand transparency from website operators about where their traffic comes from. Ads are usually served from ad networks' servers so they should already have this information.
I'm confused about the motivation here. Are the companies serving these ads running the bots, or paying others surreptitiously to run them? Why not just directly fake numbers if you're going to cheat customers? I feel I must be missing something...
1) Make shitty blog 2) Put some ads on it 3) Buy some traffic 4) Buy some click fraud traffic 5) Profit.
eg. If buzzfeed wants to make a little extra rev this month all they have to do is buy some click fraud traffic.
I'm surprised that no-one is talking about accuracy here. How, exactly, do they determine who is and isn't a bot? > For the study, researchers put software into the actual ads of 36 members of the advertising association between Aug. 1 and Oct. 1. The software allowed them to tell if viewers of the ads were people or bots.
As a product and company, they remind me of http://spider.io who did a similar thing and were acquired by Google. I was interested in using the technology to fight comment spam on blogs and forums, but it was really tailored for detecting fraudulent clicks and impressions on adverts and Google were quick to acquire.
Some of the spider.io blog is still accessible and shares some of the approaches they used: http://www.spider.io/blog/
http://dhowe.github.io/AdNauseam/
http://www.informationweek.com/software/productivity-collabo...
I'm surprised that no-one is talking about accuracy here. How, exactly, do they determine who is and isn't a bot? > For the study, researchers put software into the actual ads of 36 members of the advertising association between Aug. 1 and Oct. 1. The software allowed them to tell if viewers of the ads were people or bots.
They do that with the intent of making ad purchases, so it's actually desirable bot traffic.