Detecting spammers on social networks

Zheng, Xianghan; Zeng, Zhipeng; Chen, Zheyi; Yu, Yuanlong; Rong, Chunming

doi:10.1016/j.neucom.2015.02.047

Cited by 204 publications

(96 citation statements)

References 13 publications

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“…We started by reviewing of the existing systematic literature [10]; [12]; [11]; [71]; [69]; [26]; we concentrated on developing a protocol for a systematic mapping study that has addressed questions that are related to the spam detection framework on 3 different social networks platform [77], [76], [2][8] [9]. In the following sections, we will detail each process that we use.…”

Section: Research Methods 31 Protocol Developmentmentioning

confidence: 99%

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A Mapping Study to Investigate Spam Detection on Social Networks

Kamoru¹,

Jaafar²,

Murad³

2017

IJAIS

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Social networks such as Facebook, Twitter and SinaWeibo have become increasingly important for reaching millions of user globally. Consequently, spammers are increasing using such networks for propagating spam. Existing research on filtering techniques such as collaborative filters and behavioral analysis filters are able to significantly reduce spam. In recent years, online social networks have become the most important medium of communication among individual and organization to interact. Unfortunately, driven by the desire to communicate, fraudster or spammers have produced deceptive spam or unsolicited commercial email(UCE). The fraudsters' or spammer activities mislead potential users and victims reshaping their individual life and general communication on social network platform.The aim of this study is to understand, classify and analyze existing research in spam detection on social networks, focusing on approaches and elements that are used to evaluate the general framework of spam detection and its architectural framework from the users perspective, service provider and security analyst 's point of view. This paper presents a systematic mapping study of several spam detection techniques and approaches on social networks that were proposed to measure to evaluate the general framework of spam detection on social networks. We found 17 proposals that could be applied to evaluate spam detection on social networks, while 14 proposals could be applied to evaluate the users, service providers and practitioners. Various elements of spam detection on social networks that were measured are reviewed and discussed. Only a few of the proposed spam detection on social networks are soundly defined. The quality assessment of the primary studies detected many limitations and suggested guidelines for possibilities for improving and increasing the acceptance of spam detection on social networks. However, it remains a challenge to characterize and evaluate a spam detection and framework on social networks quantitatively. For this fact, much effort must be made to achieve a better spam detection approach in the future that will be devoid of problem anomaly detection, fault detection, malware detection and intrusion detection General Terms

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Section: Research Methods 31 Protocol Developmentmentioning

confidence: 99%

“…With the recent survey, it shows that social spam is about 355% [11],there are many problem of spam detection and spam filtering are ineffective with lots of content and behavior feature. Millions of users and waste invaluable resources and have been burden to email system [12].…”

Section: Introductionmentioning

confidence: 99%

A Mapping Study to Investigate Spam Detection on Social Networks

Kamoru¹,

Jaafar²,

Murad³

2017

IJAIS

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“…They have developed a complete system to generate summaries in real time from the incoming stream of tweets. In [18], Xianghan Zheng et. al.…”

Section: Related Workmentioning

confidence: 99%

Summarization Approach From Microblog During Disaster Events

Kawade¹,

Pise²,

Kulkarni³

2017

IJCA

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During bulk convergence events such as natural disasters, microblogging platforms like Twitter are broadly used by affected people to post situational awareness messages. As soon as natural disaster events happen, users are willing to know more about them. Twitter is a great source that can be exploited for obtaining such fine-grained arranged information for fresh natural disaster events. These crisisrelated messages disperse among multiple categories like infrastructure damage, information about bomb blast, missing, injured, and dead people etc. The challenge here is to create summary from disaster related tweets and filter the short spam url containing tweets.

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“…If a users' followers count is less than 10 and follower/friends ratio is below 0.1, then this user is labeled as spam user and so that user was not included in the dataset. The values for these parameters are assigned according to many spam user detection literature and detailed information can be found in Benevenuto et al (2010) and Zheng et al (2015).…”

Section: Data Cleaningmentioning

confidence: 99%

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

Dokuz

Çelik

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20%.

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Detecting spammers on social networks

Cited by 204 publications

References 13 publications

A Mapping Study to Investigate Spam Detection on Social Networks

A Mapping Study to Investigate Spam Detection on Social Networks

Summarization Approach From Microblog During Disaster Events

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

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