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Spammers are highly active in OSNs. Uncovering spammers has become one of the most challenging problems in OSNs. Classification-based supervised approaches are the most commonly used method for detecting spammers. Classification-based systems suffer from limitations of \u201cdata labelling\u201d, \u201cspam drift\u201d, \u201cimbalanced datasets\u201d and \u201cdata fabrication\u201d. These limitations effect the accuracy of a classifier\u2019s detection. An unsupervised approach does not require labelled datasets. We aim to address the limitation of data labelling and spam drifting through an unsupervised approach.We present a pure unsupervised approach for spammer detection based on the peer acceptance of a user in a social network to distinguish spammers from genuine users. The peer acceptance of a user to another user is calculated based on common shared interests over multiple shared topics between the two users. The main contribution of this paper is the introduction of a pure unsupervised spammer detection approach based on users\u2019 peer acceptance. Our approach does not require labelled training datasets. While it does not better the accuracy of supervised classification-based approaches, our approach has become a successful alternative for traditional classifiers for spam detection by achieving an accuracy of 96.9%.<\/jats:p>","DOI":"10.1186\/s40537-021-00552-5","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T13:03:58Z","timestamp":1641819838000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["An unsupervised method for social network spammer detection based on user information interests"],"prefix":"10.1186","volume":"9","author":[{"given":"Darshika","family":"Koggalahewa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1137-0272","authenticated-orcid":false,"given":"Yue","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Ernest","family":"Foo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"552_CR1","volume-title":"A reminder about spammy behaviour and platform manipulation on twitter","author":"K Hinesley","year":"2020","unstructured":"Hinesley K. 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