Identification of shares containing offensive charge in social media

Karamollaoglu, Hamdullah; Doğru, İbrahim Alper; Utku, Anıl

doi:10.1109/siu.2017.7960286

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…Many studies have been conducted to identify and remove spam and spam accounts [3,15,16,18,19,24,25]. The best precaution to be taken against these threats, which use social media extensively, is to know the ways in which spammers threaten users and to take personal precautions against them [26,27]. Facebook, Twitter, LinkedIn, WhatsApp and Instagram are the most common social networks on the Internet that are used for different purposes [2,4,28,29].…”

Section: Related Studies In Literaturementioning

confidence: 99%

“…Facebook, Twitter, LinkedIn, WhatsApp and Instagram are the most common social networks on the Internet that are used for different purposes [2,4,28,29]. When classifying spam detection methods on social networks, the methods that are most up-to-date and most commonly used are taken into consideration [3,24,25,27,30]. Anomaly spam detection method is based on the behavior the users exhibit [12].…”

Section: Related Studies In Literaturementioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Spam Detection Framework for Social Networks

Çıtlak¹,

Dörterler²,

Doğru³

2023

Politeknik Dergisi

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The widespread use of social networks has caused these platforms to become the target of malicious people. Although social networks have their own spam detection systems, these systems sometimes may not prevent spams in their social networks. Spam contents and messages threaten the security and performance of users of these networks. A spam account detection framework based on three components is proposed in this study. Short link analysis, machine learning and text analysis are the components used together in the proposed framework. First, a dataset was created for this purpose and the attributes of spam accounts were determined. Later, the hyperlinks in the messages in this dataset were analyzed through link analysis component. The machine learning component was modelled through attributes. Moreover, the messages of the social network users were analyzed through text analysis method. A web-based application of the proposed model was put into practice. As a result of the experimental studies carried out thanks to the framework, it was determined that the proposed framework showed a performance of 95.69 %. The success of this article was calculated according to the F-measure and precision evaluation metrics under the influence of sensitive content rate. It is aimed to detect spam accounts on social network and the spam detection policy of these networks is intended to support.

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