Abstract
We present a method for determining whether a Twitter account exhibits automated behavior in publishing status updates known as tweets. The approach uses only the publicly available timestamp information associated with each tweet. After evaluating its effectiveness, we use it to analyze the Twitter landscape, finding that 16% of active accounts exhibit a high degree of automation. We also find that 11% of accounts that appear to publish exclusively through the browser are in fact automated accounts that spoof the source of the updates.
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Zhang, C.M., Paxson, V. (2011). Detecting and Analyzing Automated Activity on Twitter. In: Spring, N., Riley, G.F. (eds) Passive and Active Measurement. PAM 2011. Lecture Notes in Computer Science, vol 6579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19260-9_11
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DOI: https://doi.org/10.1007/978-3-642-19260-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19259-3
Online ISBN: 978-3-642-19260-9
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