Revealing and Detecting Malicious Retweeter Groups

Vo, Nguyen; Lee, Kyumin; Cao, Cheng; Tran, Thanh; Choi, Hongkyu

doi:10.1145/3110025.3110068

Cited by 25 publications

(21 citation statements)

References 15 publications

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“…Creating unique content or running every single account separately to hide the similarity among the group would greatly increase the costs and time to administer these accounts; therefore, researchers have suggested several systems and strategies expose various types of malicious accounts at the campaign level. For instance, a study [14] proposed examining the social graph between users and pages to reveal Fake-Likes campaigns on Facebook, and several studies have used the synchronized behavior and timing of social spammers' fraud activities, fake Twitter followers, and malicious retweeter groups to expose their accounts on Twitter [15], [16], [17], [18], [19]. Besides, a variety of analysis studies have been carried out to understand the various aspects of social spammers.…”

Section: A Malicious Campaign Studiesmentioning

confidence: 99%

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: A Malicious Campaign Studiesmentioning

confidence: 99%

Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

Alharthi¹,

Alhothali²,

Moria³

2021

IJACSA

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“…To complement the existing approaches, [5] recently proposed an algorithm called Attractor, which is based on dynamic distance between linked nodes. This algorithm has been investigated and extended in [6], [16]. Despite Attractor's high precision, it was less efficient, requiring many iterations to converge [6].…”

Section: Related Workmentioning

confidence: 99%

“…Each reduce instance receives a list of star graph (u, Γ(u)) routed by the sorted key (i, j, k) of subgraph G ijk , which can be viewed as an adjacent list representation of subgraph G ijk . From lines [8][9][10][11][12][13] in Algorithm 3, we find set S M of main edges (u, v, d(u, v)) ∈ E ijk , which are used for computing DI, CI and EI in Lines [14][15][16][17][18][19][20][21][22][23][24][25][26]. After computing DI, CI and EI of every non-converged main edge in G ijk , each reduce instance will emit key-value pairs where key is a main edge (u, v) and value is the aggregated DI, CI and EI of edge (u, v).…”

Section: Algorithm 3 Mr2: Decgp -Computing 3 Types Interactionsmentioning

confidence: 99%

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MRAttractor: Detecting communities from large-scale graphs

Lee

Tran

2017

2017 IEEE International Conference on Big Data (Big Data)

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Detecting groups of users, who have similar opinions, interests, or social behavior, has become an important task for many applications. A recent study showed that dynamic distance based Attractor, a community detection algorithm, outperformed other community detection algorithms such as Spectral clustering, Louvain and Infomap, achieving higher Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI). However, Attractor often takes long time to detect communities, requiring many iterations. To overcome the drawback and handle large-scale graphs, in this paper we propose MRAttractor, an advanced version of Attractor to be runnable on a MapReduce framework. In particular, we (i) apply a sliding window technique to reduce the running time, keeping the same community detection quality; (ii) design and implement the Attractor algorithm for a MapReduce framework; and (iii) evaluate MRAttractor's performance on synthetic and real-world datasets. Experimental results show that our algorithm significantly reduced running time and was able to handle large-scale graphs. (8)After computing DI, CI, EI for each edge (u, v) ∈ E, new distance d(u, v) at timestamp t + 1 is updated as follows:Attractor algorithm is looped until every edge distance converged (e.g., its distance becomes either 0 or 1). Thirdly, Attractor removes edges with distance 1 and finds connected communities with breath first search. Each connected component is an identified community.

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“…(P2) How can we stimulate them to disseminate fact-checked news/information? To deal with the first problem (P1), we may deploy bots [26,48] to disseminate information but it may violate terms of services of online platforms due to abusing behavior. Another approach is to hire crowd workers [28] and cyber troops to shape public opinion [5].…”

Section: Introductionmentioning

confidence: 99%

The Rise of Guardians

Lee

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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100

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A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%∼33%. Our source code and dataset are available at

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Revealing and Detecting Malicious Retweeter Groups

Cited by 25 publications

References 15 publications

Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

Comprehensive Analysis of Two Malicious Arabic-Language Twitter Campaigns

MRAttractor: Detecting communities from large-scale graphs

The Rise of Guardians

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