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Gustav Zhuravlev
Gustav Zhuravlev

TINDER TRAFFIC METHOD.pdf



Sifting through the data, we saw patterns in who was liking and retweeting posts, which candidates were getting the most social media traffic, how much of that traffic came from highly automated accounts, and what sources of political news and information were being used. We constructed a retweeting network that included only connections where a human user retweeted a bot. This network consisted of 15,904 humans and 695 bots. The average human user in this network shared information from a bot five times.




TINDER TRAFFIC METHOD.pdf



Protecting democracy from social media manipulation will require some sort of public policy oversight. Social media companies cannot be expected to regulate themselves. They are in the business of selling information about their users to advertisers and others, information they gather through the conversations that take place on their platforms. Filtering and policing that content will cause their traffic to shrink, their expenses to rise, and their revenues to fall.


Key Takeaway: Set the stage with a story rather than trying to impress investors with big numbers on traffic and usage data. This will grab their attention and help them understand the purpose of your product.


Sites that are being used to commit fraudulent online display advertising transactions using different ad impression boosting techniques, including but not limited to ads stacking, iframe stuffing, and hidden ads. Sites that have high non-human web traffic and with rapid, large, and unexplained changes in traffic. Web analysts should not use this category. 041b061a72


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