A hybrid approach for spam detection for Twitter

Mateen, Malik; Iqbal, Muhammad Azhar; Aleem, Muhammad; Islam, Muhammad Arshad

doi:10.1109/ibcast.2017.7868095

Cited by 58 publications

(23 citation statements)

References 12 publications

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“…Online social networks (OSNs) ( Figure 2) have developed as vital platforms for people to commune across the world. After introduction of very first Social Network SixDegrees in 1997, several social networking platforms such as Facebook, Twitter and LinkedIn have been developed and became popular [1]. Advancement in Mobile Phones and Computers pushes the social network to strive for new developed applications for socializing and for fun.…”

Section: Online Social Network (Osn)mentioning

confidence: 99%

Community Spam Detection Methodologies for Recommending Nodes

2019

ijrte

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The most popular and leading social network service online now days is Facebook, twitter and Linked In. When socializing becomes usual, the probability of threats and unwanted posts (Spams) comes naturally. To identify and block such Spams, there are a few techniques available recently. However, the efficiency of such tools to combat with spammers seem tedious due to the public unavailability of critical pieces of Facebook Information like Profile, Network Information, Posts and more. Literature shows that there are many researches been carried out to find and combat malicious accounts and spammers over last two decades. In this paper, a review of similar methods that works with detection of spammers in a community on Social Networking Website with the help of mindmap that is given. The work is comprehended in how data is collected, types of spammers, classifiers, machine learning, review on spammers and community detection and whether it is graph based or non graph based dataset. A survey of research publications on Spammers and Malicious account based on malicious categories for the detected communities with the help of various categories discussed in the mindmap

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Section: Online Social Network (Osn)mentioning

confidence: 99%

Community Spam Detection Methodologies for Recommending Nodes

2019

ijrte

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“…Facebook, the most popular OSN in the world, had 1.87 billion monthly active users and 1.15 billion daily active users during the period of January to February 2017 (Watcharenwong and Saikaew, 2017). A study reported that over the course of one month, Twitter has two million users sharing 8.3 million tweets per hour (Mateen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

An Approach for Detecting Image Spam in OSNs

Imam¹

2019

Proceedings of the Conference for Truth and Trust Online 2019

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In recent years, the number of images uploaded into Online Social Networks (OSNs), such as Facebook and Twitter has been growing, which presents challenges to Machine Learning-based spam detector. Most current detection models use text-based, statistic info-based and graph-based features can easily be fooled by image-based spam. These approaches do not have the ability to recognize text embedded in images. Adversaries take advantage of this issue to launch more sophisticated attacks, such as evasion attacks. Thus, this paper proposes an adversary-aware model for detecting spam images in OSNs. The proposed model adopted EAST (an Efficient and Accurate Scene Text Detector) and CRNN (Convolutional Recurrent Neural Network) models for text detection/ recognition tasks. After the text extraction step, a blacklist and white-list with Human-in-the-loop approach is applied for text classification task. Although the technique used is simple, it is adaptable and robust against adversarial text attacks.

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“…[2][3][4] The class imbalance problem has been identified as one of the ten challenging problems in data mining research. Unfortunately, spammers usually use Twitter as a tool to post unsolicited messages that contain malicious links, and even hijack trending topics.…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4] The class imbalance problem has been identified as one of the ten challenging problems in data mining research. It has been showed that the security threats caused by Twitter spam can reach far beyond the social media platform itself.…”

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A comparative study of the class imbalance problem in Twitter spam detection

Liu

2017

Concurrency and Computation

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Recently, online social network (OSN) such as Twitter has become an important and popular source for real-time information and news dissemination, and Twitter is inevitably a prime target of spammers. It has been showed that the security threats caused by Twitter spam can reach far beyond the social media platform itself. To mitigate the damage caused by Twitter spam, machine learning classification algorithms have been employed by researchers and communities to detect the Twitter spam. However, most of these studies have overlooked the class imbalance problem in Twitter spam detection. In this paper, we have studied the class imbalance problem in Twitter spam detection. Firstly, we have conducted a comparative study regarding some popular methods in handling the class imbalance problem in order to identify the most effective approach for addressing the class imbalance problem. Then, we have conducted another comparative study from Twitter spam detection based on several classic techniques. Experimental results demonstrate that a fuzy-based ensemble learning can significantly improve the classification performance on imbalance ground truth Twitter data. KEYWORDSclassification, class imbalance, online social network, Twitter spam detection INTRODUCTIONTwitter is used to exchange messages among friends. Unfortunately, spammers usually use Twitter as a tool to post unsolicited messages that contain malicious links, and even hijack trending topics. In this respect, the exponential growth of Twitter contributes to the increase of online spamming activities. Study shows that more than 3% messages are most probably abused by spammers. 1 In order to solve the security threats caused by spammers, a lot of researchers have proposed machine learning based algorithm for Twitter spam detection. However, most of these studies have neglected a fundamental issue that is the class imbalance problem, which is widely seen in real-world Twitter data. [2][3][4] The class imbalance problem has been identified as one of the ten challenging problems in data mining research. 5 This issue occurs in two different types of data sets: binary and multiclass. For binary problem, the training data from the minority class or positive class are very small, and the rest which make up the majority class or negative class are very large. While for multiclass problems, each of the class only contains a tiny fraction of samples. These problems are also especially critical in many real-world applications. For example, in Twitter spam detection, we used to have a large amount of normal Twitter data while only small number of spam samples, this gives us imbalanced data. Previous study has shown that the detection rate for Twitter spam can be decreased for about 33% in average with the class imbalance rate rises from 2 to 20. 6 Hence, a natural question in data mining research is how to improve the performance of classifiers facing with imbalanced data?Existing techniques for handling the class imbalance problem are mainly from three perspectives, includi...

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A hybrid approach for spam detection for Twitter

Cited by 58 publications

References 12 publications

Community Spam Detection Methodologies for Recommending Nodes

Community Spam Detection Methodologies for Recommending Nodes

An Approach for Detecting Image Spam in OSNs

A comparative study of the class imbalance problem in Twitter spam detection

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