Detecting spam campaign in twitter with semantic similarity

Mostafa, Mohamed Saber; Abdelwahab, Amira; Sayed, Hanan M El

doi:10.1088/1742-6596/1447/1/012044

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…Thus, some studies [15] depend on a social graph to tackle the problem of fabrication by calculating distance and connectivity of each tweet between sender and receiver to examine whether it is spam. Yang et al [16] built a more robust feature using a bidirectional link ratio between centrality and local cluster coefficient with performance 99% true positive, while [17] provides a new solution that can detect most campaigns and classify each of them into spam or not spam using deep learning techniques and semantic similarity methods.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam

Abdelwahab

Mostafa

2022

Applied Sciences

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The social network is considered a part of most user’s lives as it contains more than a billion users, which makes it a source for spammers to spread their harmful activities. Most of the recent research focuses on detecting spammers using statistical features. However, such statistical features are changed over time, and spammers can defeat all detection systems by changing their behavior and using text paraphrasing. Therefore, we propose a novel technique for spam detection using deep neural network. We combine the tweet level detection with statistical feature detection and group their results over meta-classifier to build a robust technique. Moreover, we embed our technique with initial text paraphrasing for each detected tweet spam. We train our model using different datasets: random, continuous, balanced, and imbalanced. The obtained experimental results showed that our model has promising results in terms of accuracy, precision, and time, which make it applicable to be used in social networks.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam

Abdelwahab

Mostafa

2022

Applied Sciences

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“…They didn't carry out any strategy on the clarified module; they just recommended how to distinguish spam information. [33] Present another setting up camp identification model that relies upon vector-based characteristics for sentence introducing. The entire exploration relies upon 3 essential advances: Firstly, to examine the similitude of Twitter accounts in which posts or tweets are on a similar point.…”

Section: VImentioning

confidence: 99%

Identifying Spammers on Twitter Using Machine Learning: A Survey

Panwar¹

2021

IJRASET

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The emergence of social media like twitter has reduced the size of the world from a humungous oblate ellipsoid to a complex network wherein individuals are just a few clicks away. Twitter, a free micro-blogging social media platform has become ever more popular not only as a social site for communication but also it has become a global source for stance on controversial politics, business advertising and follow up on current news. However, this has also lead to the rise of undesirable activities like spamming and presence of fake accounts. Today, a total of 1.3 billion accounts have been created on twitter and it estimates approximately 23 million of its active users are actually bots. One popular approach suggest to tackle these problems are employing Machine Learning tools to detect spam and subsequently remove them. In this paper, we have presented a detailed analysis on identifying spam accounts by key-features and latest methods, also by using machine learning algorithms and prepared comprehensive review for the best algorithm to implement based on inferences drawn from previous research papers and articles published in this field with discussion on recent features implemented by twitter to eradicate this sophisticated but tough to obliterate issue. Also, we have highlighted some common spammers’ behavior that has been observed over the years. Index Terms: Twitter spam; spam detection; machine learning

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“…To sum up, spam is a real issue that affects the user experience in social media and there are multiple research papers [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] aimed to fight the existence of spam. Many of them focus on social media as a broad category and since Twitter is considered a microblogging service with different user requirements, this broad category of research does not always fit to Twitter.…”

Section: Related Workmentioning

confidence: 99%

“…This indicates that there is a problem with the currently used spam detection framework. Hence, many researchers are concerned with investigating and solving the problem of detecting/preventing spam and phishing on Twitter platform [16,24,[29][30][31][32][33][34][35][36][37][38][39][40]. This paper introduces a new approach for detecting spam on microblogging services using domain popularity and Machine Learning (ML) algorithms.…”

Section: Introductionmentioning

confidence: 99%

Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity

Binsaeed¹,

Stringhini²,

Youssef³

2020

IJACSA

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Detecting Internet malicious activities has been and continues to be a critical issue that needs to be addressed effectively. This is essential to protect our personal information, computing resources, and financial capitals from unsolicited actions, such as, credential information theft, downloading and installing malware, extortion, etc. The introduction of the social media such as Twitter has given malicious users a new and a promising platform to perform their activities, ranging from a simple spam message to taking a full control over the victim's machine. Twitter revealed that its algorithms for detecting spam are not very effective; most of the trending hashtags include unrelated spam and advertising tweets which indicates that there is a problem with the currently used spam detection framework. This paper proposes a new approach for detecting spam in Twitter microblogging using Machine Learning (ML) techniques and domain popularity services. The proposed approach comprises two main stages: 1) Tweets are collected periodically and filtered by selecting the ones that appear more frequently than a decided threshold in the specified period (i.e. common tweets). Then, an inspection is conducted on the common tweets by checking the associated URL domain with Alexa's top one million globally viewed websites. If a tweet is common on Twitter but does not appear on the top one million globally viewed websites, it is flagged as a potential spam. 2) The second stage kicks in by running ML algorithms on the flagged tweets to extract features that help detect the cluster of spam and prevent it in real-time. The performance of the proposed approach has been evaluated using three most popular classification models (random forest, J48, and Naïve Bayes). For all classifiers, results showed the effectiveness of the proposed method in terms of different performance metrics (e.g. precision, sensitivity, F1score, accuracy) and using different test scenarios.

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Detecting spam campaign in twitter with semantic similarity

Cited by 10 publications

References 11 publications

A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam

A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam

Identifying Spammers on Twitter Using Machine Learning: A Survey

Detecting Spam in Twitter Microblogging Services: A Novel Machine Learning Approach based on Domain Popularity

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