Detecting and Characterizing Arab Spammers Campaigns in Twitter

Alharthi, Reem; Alhothali, Areej; Moria, Kawthar

doi:10.1016/j.procs.2019.12.106

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Secondly, synchronized retweets from a set of accounts are a strong indicator of software or botnet accounts. According to our previous experiment regarding several clusters' timestamps from the two campaigns [12], we found that the accounts have synchronized retweet timestamps. Figure 14 shows an example of a central account's activities over a couple of hours and the retweet timestamps.…”

Section: Practice Of Managing Spam Accountsmentioning

confidence: 79%

See 1 more Smart Citation

Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

Alharthi¹,

Alhothali²,

Moria³

2021

IJACSA

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Fake malicious accounts are one of the primary causes of the deterioration of social network content quality. Numerous such accounts are generated by attackers to achieve multiple nefarious goals, including phishing, spamming, spoofing, and promotion. These practices pose significant challenges regarding the availability of credible data that reflect realworld social media interactions. This has led to the development of various methods and approaches to combat spammers on social media networks. Previous studies, however, have almost exclusively focused on studying and identifying English-language spam profiles, whereas the problem of malicious Arabic-language accounts remains under-addressed in the literature. In this paper, therefore, we conduct a comprehensive investigation of malicious Arabic-language campaigns on Twitter. The study involves analyzing the accounts of these campaigns from several perspectives, including their number, content, social interaction graphs, lifespans, and day-to-day activities. In addition to exposing their spamming tactics, we find that these spam accounts are more successful in avoiding Twitter suspensions that has been previously reported in the literature.

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Section: Practice Of Managing Spam Accountsmentioning

confidence: 79%

“…Finally, we note that a part of this paper appeared previously as a conference publication [12]. This part was included in the Data Analysis section in the conference paper, in which we briefly presented the clusters' organization and the automated behavior of the campaigns' accounts.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

Alharthi¹,

Alhothali²,

Moria³

2021

IJACSA

Self Cite

View full text Add to dashboard Cite

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“…Another work of Shaabani et al [32] present a semi-supervised self-training architecture capable of capturing Pathogenic Social Media users. To identify single and batches of spam accounts, Alharthy et al [33] use two semi-supervised techniques plus a set of specified features. A recent work of Guo [34] symmetrically involved BERT and GCN (Graph Convolutional Network, GCN),and a new architecture for bot identification that merged large-scale pre-training and transductive learning was proposed.…”

Section: Related Workmentioning

confidence: 99%

Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Based on Autoencoder for Social Bot Detection.

Hamida

Refouf

Drif

et al. 2022

IJCAI

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Social bots are defined as computer algorithms that generate massive amounts of obnoxious or meaningful information. Most bot detection methods leverage multitudinous characteristics, from network features, temporal dynamics features, activities features, and sentiment features. However, there has been fairly lower work exploring lexicon measurement and linguistic indicators to detect bots. The main purpose of this research is to recognize the social bots through their writing style. Thus, we carried out an exploratory study on the effectiveness of only a set of linguistic features (17 features) exploitable for bot detection, without the need to resort to other types of features. And we develop a novel framework in a hybrid fashion of Mixing Engineered Linguistic features based on Autoencoders (Hybrid-MELAu). The semisupervised Hybrid-MELAu framework is composed of two essential constituents: the features learner and the predictors. We establish the features learner innovated on two powerful structures: a) the first is a Deep dense Autoencoder fed by the Lexical and the Syntactic content (DALS) that represents the high order lexical and syntactic features in latent space, b) the second one is a Glove-BiLSTM autoencoder, which sculpts the semantic features; subsequently, we generate elite elements from the pre-trained encoder part from each latent space with transfer learning. We consider a sample of 1 Million from Cresci datasets to conduct our linguistic analysis comparison between the writing style of humans and bots. With this dataset, we observe that the bot's textual lexical diversity median is greater than the human one and the syntactic analysis based on speech-tagging shows a creative behavior in human writing style. Finally, we test the model's robustness on several public dataset (celebrity, pronbots-2019, and political bots). The proposed framework achieves a good accuracy of 92.22%. Overall, the results shown in this paper, and the related discussion, argue that it is possible to discern the differences between humans' and bots' writing styles based on an efficient linguistic deep framework.Povzetek: V prispevku je opisana metoda za detekcijo pogovornih asistentov (botov) na osnovi jezkovnega stila.

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“…Semi-supervised techniques are effective when determining patterns in a huge amount of data, such as social networks in which labeling is an expensive and time-wasting operation. Alharthi et al [49] developed a semi-supervised technique to label their dataset and classify Twitter accounts into spam or genuine. They targeted Arab spammers' accounts and figured out if it behaves like Botnet or software behavior.…”

Section: ) Semi-supervised Learning Techniquesmentioning

confidence: 99%