Mining pharmaceutical spam from Twitter

Shekar, Chandra; Wakade, Shruti; Liszka, Kathy J.; Chan, Chien-Chung

doi:10.1109/isda.2010.5687162

Cited by 17 publications

(10 citation statements)

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“…In [15], Shekar et al studied pharmaceutical spammers on Twitter, where the study shows improved results by using two lists of words instead of one list. The first list is the name of the product, and the second list is the words associated with the products, for example organic, tablet, refill, etc.…”

Section: Spammers In Social Mediamentioning

confidence: 99%

Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Abozinadah¹,

Jones²

2016

IJDKP

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Section: Spammers In Social Mediamentioning

confidence: 99%

Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Abozinadah¹,

Jones²

2016

IJDKP

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“…Naïve Bayes is an easy technique to apply for text classification. Examples for situations where it has been used are recommendation systems [53], topic detection [58], finding trending topics [33], spam detection [41], and summarizing social media-blogs [57]. Another way to improve Naïve Bayes classification process is to use unlabelled documents for training in order to get high correlation between a word w and a class L [13].…”

Section: Naïve Bayesmentioning

confidence: 99%

Classifying Short Text in Social Media: Twitter as Case Study

Kateb¹,

Kalita²

2015

IJCA

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With the huge growth of social media, especially with 500 million Twitter messages being posted per day, analyzing these messages has caught intense interest of researchers. Topics of interest include micro-blog summarization, breaking news detection, opinion mining and discovering trending topics. In information extraction, researchers face challenges in applying data mining techniques due to the short length of tweets as opposed to normal text with longer length documents. Short messages lead to less accurate results. This has motivated investigation of efficient algorithms to overcome problems that arise due to the short and often informal text of tweets. Another challenge that researchers face is stream data, which refers to the huge and dynamic flow of text generated continuously from social media. In this paper, we discuss the possibility of implementing successful solutions that can be used to overcome the inconclusiveness of short texts. In addition, we discuss methods that overcome stream data problems.

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“…Yardi et al (2010) studied the behavior of a small group of spammers, finding that they exhibit very different behavior from non-spammers in terms of posting tweets, replying tweets, followers, and friends. Shekar et al (2010) presented a strategy to filter the pharmaceutical spam on Twitter by specific keywords. The Monarch project at Berkeley Thomas et al, (2011a) used a real-time system to identify link spam in Twitter messages.…”

Section: Previous Workmentioning

confidence: 99%

“…While most existing approaches (Benevenuto, Magno, Rodrigues, & Almeida, 2010;Lee, Caverlee, & Webb, 2010;Shekar, Wakade, Liszka, & Chan, 2010;Stringhini, Kruegel, & Vigna, 2010;Thomas, Grier, Ma, Paxson, & Song, 2011a, Thomas, Grier, Song, & Paxson, 2011bWang, 2010;Yardi, Romero, Schoenebeck, & Boyd, 2010) focus on detecting Twitter criminal accounts individually, our approach for the spam problem focuses on the detection of tweets containing spam instead of detecting spam accounts. The detection of spam tweets itself can be useful for filtering spam on real time search (Benevenuto et al, 2010), whereas the detection of spammers is related with the detection of existent spam accounts.…”

Section: Introductionmentioning

confidence: 99%

Detecting malicious tweets in trending topics using a statistical analysis of language

Martínez-Romo

Araujo

2013

Expert Systems with Applications

166

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a b s t r a c tTwitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody's lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.

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Mining pharmaceutical spam from Twitter

Cited by 17 publications

References 0 publications

Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Classifying Short Text in Social Media: Twitter as Case Study

Detecting malicious tweets in trending topics using a statistical analysis of language

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