Text-Based Spam Tweets Detection Using Neural Networks

Mardi, Vanyashree; Kini, Anvaya; Sukanya, V. M.; Rachana, S

doi:10.1007/978-981-15-0222-4_37

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…However, we have compared our model with other methods to give a good clarification of the performance of our proposal.Vanyashree et al [44] proposed a framework to detect text spam using Bayes' naive classification algorithm and the artificial neural network. The study of the performance of these two algorithms shows that the Artificial Neural Network algorithm performs better than the Bayes naive classification algorithm.…”

Section: Comparison (Vssyntax-based Methods)mentioning

confidence: 99%

“…Xiao Sun et al [43], proposed a hybrid neural network model called Convolutional Neural Network-Long-Short Term Memory(CNN-LSTM), the model to sentiment analysis on a microblog Big-data platform and obtains significant improvements that enhance the generalization ability. Based on the sentiment of a single post in Weibo, this study also adopted the multivariate Gaussian model and the power law distribution to analyze the users' emotion and detect abnormal emotion on microblog, anomaly detection accuracy of an individual user is 83.49%.Vanyashree Mardi et al [44],proposed a framework to detect the text-based spam tweets using Naive Bayes Classification algorithm and Artificial Neural Network. Performance study of these two algorithms shows that Artificial Neural Network performs better than Naive Bayes Classification algorithm.…”

Section: Related Workmentioning

confidence: 99%

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A Framework for Spam Detection in Twitter Based on Recommendation System

Elmendili¹,

Idrissi²

2020

IJIES

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The rapidly growing online social networking sites have been infiltrated by a large amount of spam. Spammers are a particular kind of ill-intentioned users who degrade the quality of OSNs information through misusing all possible services provided by OSNs. Social spammers spread many intensive posts/tweets to lure legitimate users to malicious or commercial sites containing malware downloads, phishing, and drug sales. Given the fact that Twitter is not immune towards the social spam problem, different researchers have designed various detection methods, which inspect individual tweets or accounts for the existence of spam contents. Today, social networks are exposed to various threats that exploit their vulnerability. However, although of the high detection rates of the account-based spam detection methods, these methods are not suitable for filtering tweets in the real-time detection because of the need for information from Twitter's servers. At tweet spam detection level, many light features have been proposed for real-time filtering; however, the existing classification models separately classify a tweet without considering the state of previous handled tweets associated with a topic. First, they propose the identification of spam tweet by the security approach based on social honeypots and then they propose a method based on an algorithm "content filtering" in order to detect those that are similar to spam tweet detected by the approach of honeypots. Our approach has greatly improved the quality of abstraction in terms of performance and design. The algorithm is also fast and simple to implement. Experimental results show the stability and accuracy (over 99%), F-measure 98% of our approach.

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Section: Comparison (Vssyntax-based Methods)mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Framework for Spam Detection in Twitter Based on Recommendation System

Elmendili¹,

Idrissi²

2020

IJIES

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Bio-Inspired Algorithm Based Undersampling Approach and Ensemble Learning for Twitter Spam Detection

Kiruthika Devi,

Sathish Kumar

2024

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Currently, social media networks such as Facebook and Twitter have evolved into valuable platforms for global communication. However, due to their extensive user bases, Twitter is often misused by illegitimate users engaging in illicit activities. While there are numerous research papers available that delve into combating illegitimate users on Twitter, a common shortcoming in most of these works is the failure to address the issue of class imbalance, which significantly impacts the effectiveness of spam detection. Few other research works that have addressed class imbalance have not yet applied bio-inspired algorithms to balance the dataset. Therefore, we introduce PSOB-U, a particle swarm optimization-based undersampling technique designed to balance the Twitter dataset. In PSOB-U, various classifiers and metrics are employed to select majority samples and rank them. Furthermore, an ensemble learning approach is implemented to combine the base classifiers in three stages. During the training phase of the base classifiers, undersampling techniques and a cost-sensitive random forest (CS-RF) are utilized to address the imbalanced data at both the data and algorithmic levels. In the first stage, imbalanced datasets are balanced using random undersampling, particle swarm optimization-based undersampling, and random oversampling. In the second stage, a classifier is constructed for each of the balanced datasets obtained through these sampling techniques. In the third stage, a majority voting method is introduced to aggregate the predicted outputs from the three classifiers. The evaluation results demonstrate that our proposed method significantly enhances the detection of illegitimate users in the imbalanced Twitter dataset. Additionally, we compare our proposed work with existing models, and the predicted results highlight the superiority of our spam detection model over state-of-the-art spam detection models that address the class imbalance problem. The combination of particle swarm optimization-based undersampling and the ensemble learning approach using majority voting results in more accurate spam detection.

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A Weak-Region Enhanced Bayesian Classification for Spam Content-Based Filtering

Nosrati

Rahmani

Jolfaei

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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This paper proposes an improved Bayesian scheme by focusing on the region in which Bayesian may fail to correctly identify labels and improve classification performance by handling those errors. Bayesian method, as a probabilistic classifier, uses Bayes’ theorem to calculate the probability of an instance belonging to a class, where the class label with a maximum probability is assigned to the instance. In a spam detection problem, it can be considered that the prediction of Bayesian classifier is weak when the probability obtained for classes spam and non-spam are close to each other. Therefore, we define a threshold to determine weak prediction against strong prediction. A hybrid strategy using a two-layer Bayesian approach is presented: basic Bayesian (BBayes) and corrected weak region Bayesian (CWRBayes), which are concerned with strong and weak predictions, respectively. Both techniques, BBayes and CWRBayes, have the same classification mechanism, but they use different feature selection mechanisms. The proposed methods are implemented and evaluated over two datasets of spam emails, and the results show that the proposed method has better performance than the baseline of the naïve Bayesian and some other Bayesian variants.

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Text-Based Spam Tweets Detection Using Neural Networks

Cited by 4 publications

References 8 publications

A Framework for Spam Detection in Twitter Based on Recommendation System

A Framework for Spam Detection in Twitter Based on Recommendation System

Bio-Inspired Algorithm Based Undersampling Approach and Ensemble Learning for Twitter Spam Detection

A Weak-Region Enhanced Bayesian Classification for Spam Content-Based Filtering

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