Social Spam Detection Research Articles

Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data

Cybercriminals use social media platforms to disseminate spam, misleading facts, fake news, and malicious links. Blocking such deceptive social media spam is essential. However, extracting relevant features from social networks is challenging due to privacy and time constraints. Traditional frequency-based word representation techniques are time-consuming and inefficient in producing contextual word vectors. Word embeddings and deep learning models have recently shown good results in text classification. Also, most existing approaches assumed balanced class distribution, which is false for most real-world datasets. In this paper, an attempt is made to advance the performance of the social spam detection system by leveraging dataset balancing, advanced word embedding techniques, machine learning, and deep learning approaches with the self-attention mechanism. In the proposed framework, the datasets are balanced using NearMiss and SmoteTomek techniques to feed several machine-learning models. Later, the baseline ML models and proposed voting-based ensemble models are evaluated on imbalanced and balanced datasets. For the proposed deep learning-based hybrid approaches, embeddings are generated using GloVe and FastText word embeddings on the balanced combined dataset and passed into the deep neural network comprised of Conv1D and Bi-directional recurrent neural network layers with the self-attention mechanism for improved context understanding and effective results. This study examines hybrid approaches for detecting social spam using imbalanced social network data and picks the optimum combination. Besides, Machine learning ensembles, word embeddings, deep learning with hyper-parameter optimization, and a self-attention method are compared thoroughly. Experiments and comparisons with other techniques show that the proposed hybrid framework with deep learning-based approaches achieves better performance.

Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations

Malicious web links, social rumors, fraudulent advertisements, faked comments, and biased propaganda are overwhelmingly influencing online social networks. Enabling information integrity is a hot topic in both academia and industry. Traditional social spam detection techniques rely on centralized processing, focusing only on one specific set of data sources, thereby ignoring the social spam correlations between distributed data sources. In this paper, we propose an online and scalable misinformation detection system, named Spiral, to uncover social spam by leveraging the correlations between different social data sources in geo-distributed sites. The key insight in our approach is to amplify the effectiveness of state-of-the-art techniques to detect inappropriate posts by enabling the efficient large-scale propagation of detection information across domains. The novelty of our design lies in three key components: (1) a decentralized distributed hash-table-based tree overlay deployment for harvesting and uncovering deceptive information spreading in multiple online social networks communities; (2) a progressive aggregation tree for collecting the properties of these posts and creating new classifiers to actively filter out the propagation of inappropriate posts; and (3) a group communication structure that allows multiple groups to exchange the correlations among distributed social data sources. We designed and implemented a prototype of the Spiral system. Our large-scale experiments, using real-world social data, demonstrate Spiral’s scalability, effective load-balancing, and efficiency in online spam detection for social networks.

SOCIAL MEDIA SPAM DETECTION USING DIFFERENT TEXT FEATURE SELECTION TECHNIQUE AND MACHINE LEARNING

The messaging systems and social media is popular and has essential contributions to our social and professional life. Similarly, Spam is a part of the messaging system and social media. In social media, spam is found in various places (i.e. in posts, in comments, in reviews, and in chatting). Social media Spam is aimed to influence the user’s decision, point of view, and credibility of the service or brand. Therefore, social spam detection is essential. However, using the social media data a number of contributions are available in literature, but a fewer amount of work is available for social media spam detection. In this paper, we proposed a social media spam detection technique using machine learning and text feature extraction techniques. In this context first, a review on social media spam detection techniques has been carried out. Using this review, we extract the different machine learning techniques used, techniques of text feature selection, and experimental datasets used. In this review, we found that the spam messages with the URLs are more critical and harmful. Next step, we design a theoretical model for social media spam detection, which includes text feature selection techniques (i.e. TF-IDF, POS, and Information Gain) and their combinations (POS+TF-IDF and POS+IG). These features are used with Support Vector Machine (SVM), Artificial Neural Network, and Naïve Bayes classifier for training. Experimental analysis with dataset available in Kaggle we found that hybrid features is more effective for accurate classification as compared to individual features. Additionally, we found for classification the SVM and ANN are more accurate as compared to the Bayes classifier.

An intelligent system for multi-topic social spam detection in microblogging

The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users’ behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules.

Boosting Social Spam Detection via Attention Mechanisms on Twitter

Twitter is one of the largest social networking platforms, which allows users to make friends, read the latest news, share personal ideas, and discuss social issues. The huge popularity of Twitter mean it attracts a lot of online spammers. Traditional spam detection approaches have shown the effectiveness for identifying Twitter spammers by extracting handcrafted features and training machine learning models. However, such models need knowledge from domain experts. Moreover, the behaviors of spammers can change according to the defense strategies of Twitter. These result in the ineffectiveness of the traditional feature-based approaches. Although deep-learning-based approaches have been proposed for detecting Twitter spammers, they all treat each tweet equally, and ignore the differences among them. To solve these issues, in this paper, we propose a new attention-based deep learning model to detect social spammers in Twitter. In particular, we first introduce the state-of-the-art pretraining model BERTweet for learning the representation of each tweet, and then use the proposed novel attention-based mechanism to learn the user representations by distinguishing the differences among tweets posted by each user. Moreover, we take social interactions into consideration and propose that a graph attention network is used to update the learned user representations, to further improve the accuracy of identifying spammers. Experiments on a publicly available, real-world Twitter dataset show the effectiveness of the proposed model, which is able to significantly enhance the performance.

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. The proposed framework was applied on MPI_SWS and SAC’13 Tip spam datasets and the developed models were evaluated based on accuracy, precision, recall, f-measure, and area under the curve (AUC). From the experimental results, the proposed framework (that is, Ensemble + IG + PCA) outperformed other experimented methods on studied spam datasets. Specifically, the proposed method had an average accuracy value of 87.5%, an average precision score of 0.877, an average recall value of 0.845, an average F-measure value of 0.872 and an average AUC value of 0.943. Also, the proposed method had better performance than some existing methods. Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for detecting spam contents.

TextSpamDetector: textual content based deep learning framework for social spam detection using conjoint attention mechanism

Online Social Networks (OSNs) allow easy membership leading to registration of a huge population and generation of voluminous information. These characteristics attract spammers to spread spam which may cause annoyance, financial loss, or personal information loss to the user and also weaken the reputation of social network sites. Most of the spam detection methods are based on user and content-based features using machine learning techniques. But, these annotated features are difficult to extract in real-time due to the privacy policy of most social network sites. Even for the features that can be extracted, because of their large size, the manual extraction process is complex and time-consuming. So there is a need for text level spam detection that does not require extraction of hard-core features. Existing deep learning based or existing single attention mechanism based text classification methods could not perform well as social network data are sparse with short texts and noises. Moreover, Spammers avoid direct spam words and use indirect words to evade spam filtering techniques and thus resulting in the dynamic and non-stationary nature of the social network spam texts. These indirect words contain hidden context that creates attention drift problem. So conjoint attention mechanism along with two attention mechanisms namely normal attention and context preserving attention are proposed to avoid attention drift problem in this deep learning-based text level spam detection technique (TextSpamDetector). Attention drift problem is solved by one attention mechanism which helps to find the important words while another attention mechanism allows focusing on attention in target context by referring to higher level abstraction of context vector. These attention mechanisms are referring to different context representations of the input text for finding informative words from the structural context representation. This structural context representation containing both local semantic features as well as global semantic dependency features is generated by CNN and BiLSTM. The proposed model is evaluated with the existing spam detection techniques using three datasets and the experimental results have proved that the proposed model performs well in terms of accuracy, F measure, and false-positive rate.

SimilCatch: Enhanced social spammers detection on Twitter using Markov Random Fields

The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate the possibility of using the output of previously proposed supervised classification systems as a tool for spammers discovery. The hypothesis is that these systems are still highly capable of detecting spammers reliably even when their recall is far from perfect. We then propose to use the output of these classifiers as prior beliefs in a probabilistic graphical model framework. This framework allows beliefs to be propagated to similar social accounts. Basing similarity on a who-connects-to-whom network has been empirically critiqued in recent literature and we propose here an alternative definition based on a bipartite users-content interaction graph. For evaluation, we build a Markov Random Field on a graph of similar users and compute prior beliefs using a selection of state-of-the-art classifiers. We apply Loopy Belief Propagation to obtain posterior predictions on users. The proposed system is evaluated on a recent Twitter dataset that we collected and manually labeled. Classification results show a significant increase in recall and a maintained precision. This validates that formulating the detection problem with an undirected graphical model framework permits to restore the deteriorated performances of previously proposed statistical classifiers and to effectively mitigate the effect of spam evolution.

GAMEFEST: Genetic Algorithmic Multi Evaluation measure based FEature Selection Technique for social network spam detection

Social Network sites have become incredibly important in the present day. This popularity attracts the attacker to easily approach a large population and to have access to massive information for performing intrusion activities in Online Social Networks (OSN) including spamming. Spammers not only spread unsolicited messages but also perform malicious activities that harm the user’s financial or personal life and tarnish the reputation of social network platforms. Efficient spam detection requires the selection of relevant features to portray spammer behavior. Most of the existing feature selection techniques use any one of the evaluation measures such as, distance, dependence, consistency, information, and classifier error rate. The feature selection techniques select features from different perspectives based on the evaluation measures. Each evaluation measure produces different subset, and the detection rate differs accordingly. The majority of the existing works focus on the individual feature ranking, and discard the lowest weight feature. Lowest weight feature may produce more accurate prediction if, it is combined with other features. So, there is a need for the feature selection technique that considers the characteristics of all the evaluation measures to produce the appropriate subset, which increases the spam detection rate and assigns a weight for the combination of features. In regard to this, the paper proposes a new multi evaluation measure combined with feature subset selection based on the genetic algorithm, GAMEFEST. The performance of the proposed work has been evaluated using Twitter, Apontador, and YouTube datasets. Experimental results prove that our proposed GAMEFEST with Minimum Surplus Crossover (MSC) improves the efficiency of the learning process and increases the spam detection rate.

Online Social Networking services and Spam Detection Approaches in Opinion Mining- A review

Social network platforms have gained enormous growth in present days. Users are more attracted towards social network sites such as Twitter, Facebook, micro blogs and YouTube to express their opinion and views and share the knowledge. With the tremendous increase in technological growth and with the diversity of products, online e-commerce servicing sites are becoming competitor's for each other to increase their proliferation in business offering to post positive product spam reviews. Users generally express their opinion based on different sentiment orientations, ratings and the features of the product. This tends to create ambiguity at the customers end in arriving at a decision based on the criticism, building fake opinion on the products. Opinion spam detection provides scope in building opinion from the reviews of various networking sites. A novel framework using meta-heuristic and k-means clustering approach is proposed for identifying opinion spam detection using flower pollination, grey wolf and moth flame. Amazon automotive product dataset is chosen for analysis and it is observed that grey wolf algorithm performs better than flower pollination and moth flame optimisation algorithms in terms of improved convergence speed, mean, standard deviation, variance and elapsed run time.

Mining Based Design and Analysis of Social Spam Detection in Micro-blogging

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection

With the rise of social web, there has also been a great concern about the quality of user-generated content on social media sites (SMSs). Deceptive comments harm users' trust in online social medi...

Recent developments in social spam detection and combating techniques: A survey

Spam in recent years has pervaded all forms of digital communication.The increase in user base for social platforms like Facebook, Twitter, YouTube, etc., has opened new avenues for spammers. The liberty to contribute content freely has encouraged the spammers to exploit the social platforms for their benefits. E-mail and web search engine being the early victims of spam have attracted serious attention from the information scientists for quite some time. A substantial amount of research has been directed to combat spam on these two platforms. Social networks being quite different in nature from the earlier two, have different kinds of spam and spam-fighting techniques from these domains seldom work. Moreover, due to the continuous and rapid evolution of social media, spam themselves evolve very fast posing a great challenge to the community. Despite being relatively new, there has been a number of attempts in the area of social spam in the recent past and a lot many are certain to come in near future. This paper surveys the recent developments in the area of social spam detection and mitigation, its theoretical models and applications along with their qualitative comparison. We present the state-of-the-art and attempt to provide challenges to be addressed, as the nature and content of spam are bound to get more complicated.

SPADE: a social-spam analytics and detection framework

Social media such as Facebook, MySpace, and Twitter have become increasingly important for attracting millions of users. Consequently, spammers are increasing using such networks for propagating spam. Although existing filtering techniques such as collaborative filters and behavioral analysis filters are able to significantly reduce spam, each social network needs to build its own independent spam filter and support a spam team to keep spam prevention techniques current. To alleviate those problems, we propose a framework for spam analytics and detection which can be used across all social network sites. Specifically, the proposed framework SPADE has numerous benefits including (1) new spam detected on one social network can quickly be identified across social networks; (2) accuracy of spam detection will be improved through cross-domain classification and associative classification; (3) other techniques (such as blacklists and message shingling) can be integrated and centralized; (4) new social networks can plug into the system easily, preventing spam at an early stage. In SPADE, we present a uniform schema model to allow cross-social network integration. In this paper, we define the user, message, and web page model. Moreover, we provide an experimental study of real datasets from social networks to demonstrate the flexibility and feasibility of our framework. We extensively evaluated two major classification approaches in SPADE: cross-domain classification and associative classification. In cross-domain classification, SPADE achieved over 0.92 F-measure and over 91 % detection accuracy on web page model using Naive Bayes classifier. In associative classification, SPADE also achieved 0.89 F-measure on message model and 0.87 F-measure on user profile model, respectively. Both detection accuracies are beyond 85 %. Based on those results, our SPADE has been demonstrated to be a competitive spam detection solution to social media.

Virtual Celebrator Machine

There has been a huge growth of social network in the recent years. This trend does not only allow us to get connected and share the information in an efficient way, but also reveals some potential beneficial in dealing with several social issues, such as earthquake detection, social spam detection, flu pandemic tracking, media monitoring, etc. In this paper, we propose a new way of utilizing social network. By implementing what is called a Virtual Celebrator Machine (VCM), we are able to let everyone who has connection with this machine in term of social networking be able to share their cultural experience and points of view about certain social events locally or globally. In that way, we provide a way to reinforce the relationship and connection between people virtually, which, we believe, would help to flourish cultural heritage preservation.

LSSVM-Based Social Spam Detection Model

To Resolve the garbage tag issue in Folksonomy, Lssvm algorithm for social spam detection model (least Squares support vector machine classifiers) was proposed. The method of inequality change the constraints in the traditional support vector machine into equality constraints, and take the empirical function of the squared error loss function as the Experience function in training set. so that the quadratic programming problem convert QP into solving linear equations, it was improving solution the speed of solution and accuracy of convergence.The experimental results show that we have got higher classification accuracyand less predict time than traditional svm detection methods based on least squares support vector machine algorithm garbage tag detection model.