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Detect Spammers in Online Social Net...

Zhang, Yi.

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Detect Spammers in Online Social Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Detect Spammers in Online Social Networks./
作者:	Zhang, Yi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
面頁冊數:	75 p.
附註:	Source: Masters Abstracts International, Volume: 54-03.
Contained By:	Masters Abstracts International54-03(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1585028
ISBN:	9781321606959

Detect Spammers in Online Social Networks.
Zhang, Yi.

Detect Spammers in Online Social Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 75 p.

Source: Masters Abstracts International, Volume: 54-03.

Thesis (M.Sc.)--University of Windsor (Canada), 2015.

Fake followers in online social networks (OSNs) are the accounts that are created to boost the rank of some targets. These spammers can be generated by programs or human beings, making them hard to identify. In this thesis, we propose a novel spammer detection method by detecting near-duplicate accounts who share most of the followers.

ISBN: 9781321606959Subjects--Topical Terms:

523869
Computer science.

Detect Spammers in Online Social Networks.
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520 $a Fake followers in online social networks (OSNs) are the accounts that are created to boost the rank of some targets. These spammers can be generated by programs or human beings, making them hard to identify. In this thesis, we propose a novel spammer detection method by detecting near-duplicate accounts who share most of the followers.
520 $a It is hard to discover such near-duplicates on large social networks that provide limited remote access. We identify the near-duplicates and the corresponding spammers by estimating the Jaccard similarity using star sampling, a combination of uniform random sampling and breadth-first crawling. Then we applied our methods in Sina Weibo and Twitter. For Weibo, we find 395 near-duplicates, 12 millions suspected spammers and 741 millions spam links. In Twitter, we find 129 near-duplicates, 4.93 million suspected spammers and 2.608 billion spam links. Moreover, we cluster the near-duplicates and the corresponding spammers, and analyze the properties of each group.
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