Detecting Social Spam Campaigns on Twitter

Chu, Zi; Widjaja, Indra; Wang, Haining

doi:10.1007/978-3-642-31284-7_27

Cited by 126 publications

(83 citation statements)

References 16 publications

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“…Equation 13 corresponds to the incorporation of the paradoxical discounted ratio to PageRank. As it can be seen, for each page p j linking to page p i its PageRank score -PR(p j )-is weighted according to the number of outgoing links from p j and, in this modified version, its paradoxical discounted ratio.…”

Section: A Rationale For "Desensitizing" Prestige Ranking Methods Agamentioning

confidence: 99%

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Gayo-Avello

2013

Information Processing & Management

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Abstract. Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards -desensitizing‖ prestige algorithms against cheating by spammers and other abusive users.

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Section: A Rationale For "Desensitizing" Prestige Ranking Methods Agamentioning

confidence: 99%

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Gayo-Avello

2013

Information Processing & Management

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“…Zi Chu et al in [10] described that previously all spam detection methods check only individual messages or account for the existence of spam. They focused on the detection of spam campaigns that supervise multiple accounts to spread spam on the twitter network.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Based Twitter Spam Detection

Perveen¹,

Missen²,

Rasool³

et al. 2016

ijacsa

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Abstract-Spams are becoming a serious threat for the users of online social networks especially for the ones like of twitter. twitter's structural features make it more volatile to spam attacks. In this paper, we propose a spam detection approach for twitter based on sentimental features. We perform our experiments on a data collection of 29K tweets with 1K tweets for 29 trending topics of 2012 on twitter. We evaluate the usefulness of our approach by using five classifiers i.e. BayesNet, Naive Bayes, Random Forest, Support Vector Machine (SVM) and J48. Naive Bayes, Random Forest, J48 and SVM spam detections performance improved with our all proposed features combination. The results demonstrate that proposed features provide better classification accuracy when combined with content and user-oriented features.

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“…As the popularity of Twitter rapidly grows, spammers have started to distribute spam tweets which can be broadly defined as unwanted tweets that contains malicious URLs in most cases or occasionally malicious texts [4], [5], [20]. According to a study in 2010 [4], roughly 8% of the URLs in tweets are malicious ones that direct users to scams/malware/phishing sites, and about 0.13% of the spam URLs will be clicked.…”

Section: A Why the Social Botnet For Spam Distribution?mentioning

confidence: 99%

On the impact of social botnets for spam distribution and digital-influence manipulation

Zhang

et al. 2013

2013 IEEE Conference on Communications and Network Security (CNS)

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Abstract-Online social networks (OSNs) are increasingly threatened by social bots which are software-controlled OSN accounts that mimic human users with malicious intentions. A social botnet refers to a group of social bots under the control of a single botmaster, which collaborate to conduct malicious behavior, while at the same time mimicking the interactions among normal OSN users to reduce their individual risk of being detected. We demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digitalinfluence manipulation through real experiments on Twitter and also trace-driven simulations. Our results can help understand the potentially detrimental effects of social botnets and help OSNs improve their bot(net) detection systems.

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Detecting Social Spam Campaigns on Twitter

Cited by 126 publications

References 16 publications

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Sentiment Based Twitter Spam Detection

On the impact of social botnets for spam distribution and digital-influence manipulation

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