SPAR: A system to detect spam in Arabic opinions

Wahsheh, Heider A.; Al-Kabi, Mohammed N.; Alsmadi, Izzat

doi:10.1109/aeect.2013.6716442

Cited by 15 publications

(11 citation statements)

References 10 publications

(15 reference statements)

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“…Arabic opinion spam detection is still under researched. Only two studies were found: [40], [41], and both on business reviews that use machine learning classifiers on annotated corpora.…”

Section: Opinion Spam Detectionmentioning

confidence: 99%

Subjectivity and sentiment analysis of Arabic: Trends and challenges

Al-Twairesh

Al‐Khalifa

Al-Salman

2014

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)

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Recently, subjectivity and sentiment analysis of Arabic has received much attention from the research community. In the past two years, an enormous number of references in the field have emerged compared to what has been published in previous years. In this paper, we present an updated survey of the emerging research on subjectivity and sentiment analysis of Arabic. We also highlight the challenges and future research directions in this field.

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“…Arabic opinion spam detection is still under researched. Only two studies were found: [40], [41], and both on business reviews that use machine learning classifiers on annotated corpora.…”

Section: Opinion Spam Detectionmentioning

confidence: 99%

Subjectivity and sentiment analysis of Arabic: Trends and challenges

Al-Twairesh

Al‐Khalifa

Al-Salman

2014

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA)

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“…The detection of spam in Arabic opinion reviews is a relatively new research field then, the bibliography is scarce in this area. In Wahsheh, Al‐Kabi, and Alsmadi (), the authors present one of the first systems to detect spam in Arabic opinions. The system named SPAR uses features as spam URLs (a blacklist with Arabic content/link spam web pages; Wahsheh, Al‐kabi, & Alsmadi, ), five or more consecutive numbers, and presence of the “@” symbol with letters around (e‐mails address) for the classification of opinions like spam or not spam.…”

Section: Deception Detectionmentioning

confidence: 99%

A survey on author profiling, deception, and irony detection for the Arabic language

Rosso

Rangel

Farías

et al. 2018

Language and Linguist. Compass

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The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.

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“…For instance [5] built a framework to detect spam in Arabic opinions of the user feedback and comments on the web content or news. The framework has two categories and subcategories.…”

Section: B Spam Detection and Machine Learningmentioning

confidence: 99%

“…These attempts have failed because spam detection tools trained in the English language are being implemented Manuscript received March 12, 2015; revised June 9, 2015. The authors are with Computer Science Department, George Mason University, Fairfax, VA 22030 USA (e-mail: eabozina@gmu.edu, ambaziir@gmu.edu, jjonesu@gmu.edu).on Arabic spam [4], [5]. Spammers are exploiting this loophole to launch successful spam campaigns.…”

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confidence: 99%

Detection of Abusive Accounts with Arabic Tweets

Abozinadah¹,

Mbaziira²,

Jones³

2015

IJKE

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Abstract-Twitter is one of the most popular sources for disseminating news and propaganda in the Arab region. Spammers are now creating abusive accounts to distribute adult content in Arabic tweets, which is prohibited by Arabic norms and cultures. Arab governments are facing a massive challenge to detect these accounts. This paper evaluates different machine learning algorithms for detecting abusive accounts with Arabic tweets, using Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (J48) classifiers. We are not aware of another existing data set of abusive accounts with Arabic tweets, and this is the first study to investigate this issue. The data set for this analysis was collected based on the top five Arabic swearing words. The results show that the Naïve Bayes (NB) classifier with 10 tweets and 100 features has the best performance with 90% accuracy rate.Index Terms-Arabic text classification, machine learning, pornographic spam, social network abuse. I. INTRODUCTIONTwitter is a micro blogger provider where users compose messages of not more than 140 characters. These messages are called tweets, and may contain text, pictures, videos or hyperlinks. The usernames in Twitter start with a prefix (@). Twitter users create their social networks through followers and following relationships. Tweets will be posted on the user and the followers' timelines and can be found by Twitter's search engine. The tweets can be forwarded to the user's followers by clicking -Retweet‖. At the same time, the tweet can be replayed by including the username prefixed by @ in the tweet. The tweets' topics can be indexed using hashtags for each topic. All hashtags in Twitter are preceded with the hash (#) symbol and can also be searched through Twitter's search engine.Since the 2011 Arab spring, the number of Twitter users in Arab nations has been escalating. Twitter has registered five million active users in Arab countries, who send on average 17 million tweets a day. Twitter, like other social media, is a popular medium for disseminating news and propaganda Consequently, spammers are exploiting Twitter's popularity in the Middle East to disseminate malicious content. These mal-actors have opened up Twitter accounts to launch spamming campaigns targeting Arabic speakers within the 22 nations in the Middle East. Some of the Arab nations have attempted, but failed, to censor Internet traffic to block malicious URLs and contents from abusive social media accounts. These attempts have failed because spam detection tools trained in the English language are being implemented Manuscript received March 12, 2015; revised June 9, 2015. The authors are with Computer Science Department, George Mason University, Fairfax, VA 22030 USA (e-mail: eabozina@gmu.edu, ambaziir@gmu.edu, jjonesu@gmu.edu).on Arabic spam [4], [5]. Spammers are exploiting this loophole to launch successful spam campaigns.In the meantime, the number of abusive accounts has been increasing over time by exploiting the simplicity of using emails as a verificati...

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SPAR: A system to detect spam in Arabic opinions

Cited by 15 publications

References 10 publications

Subjectivity and sentiment analysis of Arabic: Trends and challenges

Subjectivity and sentiment analysis of Arabic: Trends and challenges

A survey on author profiling, deception, and irony detection for the Arabic language

Detection of Abusive Accounts with Arabic Tweets

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