Nikan Chavoshi

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Online reviews on products and services can be very useful for customers, but they need to be protected from manipulation. So far, most studies have focused on analyzing online reviews from a single hosting site. How could one leverage information from multiple review hosting sites? This is the key question in our work. In response, we develop a systematic(More)
We develop a warped correlation finder to identify correlated user accounts in social media websites such as Twitter. The key observation is that humans cannot be highly synchronous for a long duration, thus, highly synchronous user accounts are most likely bots. Existing bot detection methods are mostly supervised, which requires a large amount of labeled(More)
Social media sites (e.g. Twitter and Pinterest) allow users to change the name of their accounts. A change in the account name results in a change in the URL of the user’s homepage. We develop an algorithm that extracts such changes from streaming data and discover that a large number of social media accounts are performing synchronous and collaborative URL(More)
Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, DTW, in its original formulation, is extremely inefficient in comparing long sparse time series, which mostly contain zeros and unevenly spaced non-zero observations. Original DTW distance does not take advantage of the sparsity, and(More)
We propose BotWalk, a near-real time adaptive Twitter exploration algorithm to identify bots exhibiting novel behavior. Due to suspension pressure, Twitter bots are constantly changing their behavior to evade detection. Traditional supervised approaches to bot detection are non-adaptive and thus cannot identify novel bot behaviors. We therefore devise an(More)
Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced non-zero observations. Original DTW distance does not take advantage of this sparsity,(More)
Dynamic time warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced nonzero observations. Original DTW distance does not take advantage of this sparsity,(More)
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