Detecting Arabic spammers and content polluters on Twitter

El-Mawass, Nour; Alaboodi, Saad Saleh

doi:10.1109/icdipc.2016.7470791

Cited by 25 publications

(20 citation statements)

References 12 publications

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“…Bots on English Twitter have been used to promote jihadist propaganda [6,7], spread fake news [43], and infiltrate political discussions [9]. Bots have also been used for spamming in both Arabic [22] and English [54] Twitter networks. Other nefarious roles of bots that have been explored on English Twitter include manipulating the stock market and stealing personal data [25].…”

Section: Discussionmentioning

confidence: 99%

“…A different but related research problem is the detection of spam content which sometimes involves bots. In [22], El-Mawass et al reported that about 74% of tweets in Saudi trending hashtags are spam. They suggested that bots are sometimes used to increase the reach of spam content by coordinated liking and retweeting of spam tweets.…”

Section: Malicious Use Of Bots In Social Mediamentioning

confidence: 99%

“…A study by Abokhodair et al [1] analyzed a Twitter botnet that was active during the Syrian civil war to understand how it might have influenced related discussions. El-Mawass et al [22] estimated that around 75% of Saudi trending hashtags on Twitter contained spam content, some of which was created by automated spammers.…”

Section: Introductionmentioning

confidence: 99%

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Hateful People or Hateful Bots?

Albadi

Kurdi

Mishra

2019

Proc. ACM Hum.-Comput. Interact.

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Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.

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Section: Discussionmentioning

confidence: 99%

Section: Malicious Use Of Bots In Social Mediamentioning

confidence: 99%

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Hateful People or Hateful Bots?

Albadi

Kurdi

Mishra

2019

Proc. ACM Hum.-Comput. Interact.

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“…In our previous investigation, we found that about three quarters of the tweets with trending hashtags in Saudi Arabia were spam messages. This estimation is backed by independent reports that place Saudi Arabia as the second most common "global target for online spam and other forms of cyber-violation" and as the "most spammed country in the world for three years in a row with an 83.3% spam rate" [2]. A deeper analysis showed an even higher percentage of automatically generated tweets.…”

Section: Introductionmentioning

confidence: 95%

Predicting Rogue Content and Arabic Spammers on Twitter

Alharbi

Aljaedi

2019

Future Internet

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Twitter is one of the most popular online social networks for spreading propaganda and words in the Arab region. Spammers are now creating rogue accounts to distribute adult content through Arabic tweets that Arabic norms and cultures prohibit. Arab governments are facing a huge challenge in the detection of these accounts. Researchers have extensively studied English spam on online social networks, while to date, social network spam in other languages has been completely ignored. In our previous study, we estimated that rogue and spam content accounted for approximately three quarters of all content with Arabic trending hashtags in Saudi Arabia. This alarming rate, supported by autonomous concurrent estimates, highlights the urgent need to develop adaptive spam detection methods. In this work, we collected a pure data set from spam accounts producing Arabic tweets. We applied lightweight feature engineering based on rogue content and user profiles. The 47 generated features were analyzed, and the best features were selected. Our performance results show that the random forest classification algorithm with 16 features performs best, with accuracy rates greater than 90%.

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“…Other than emails there are also attempts for detecting spam in social networks, such as Twitter. Such work was done by El-Mawass and Alaboodi in [68]. They elaborated a system to detect spam in Arabic tweets.…”

Section: Arabic Languagementioning

confidence: 99%

A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

Haidar¹,

Chamoun²,

Serhrouchni³

2017

Adv. sci. technol. eng. syst. j.

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With the abundance of Internet and electronic devices bullying has moved its place from schools and backyards into cyberspace; to be now known as Cyberbullying. Cyberbullying is affecting a lot of children around the world, especially Arab countries. Thus, concerns from cyberbullying are rising. A lot of research is ongoing with the purpose of diminishing cyberbullying. The current research efforts are focused around detection and mitigation of cyberbullying. Previously, researches dealt with the psychological effects of cyberbullying on the victim and the predator. A lot of research work proposed solutions for detecting cyberbullying in English language and a few more languages, but none till now covered cyberbullying in Arabic language. Several techniques contribute in cyberbullying detection, mainly Machine Learning (ML) and Natural Language Processing (NLP). This journal extends on a previous paper to elaborate on a solution for detecting and stopping cyberbullying. It first presents a thorough survey for the previous work done in cyberbullying detection. Then a solution that focuses on detecting cyberbullying in Arabic content is displayed and assessed.

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Detecting Arabic spammers and content polluters on Twitter

Cited by 25 publications

References 12 publications

Hateful People or Hateful Bots?

Hateful People or Hateful Bots?

Predicting Rogue Content and Arabic Spammers on Twitter

A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning

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