In this paper, we propose a new feature engineering mechanism based on a deep neural network trained using Bi-LSTM. We name the extracted features “deep-learnt ...
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The results show that machine learning models trained using deep-learnt features can detect. Twitter spam more accurately than models trained using word2vec ...
A new feature engineering mechanism based on a deep neural network trained using Bi-LSTM that can detect Twitter spam more accurately than models trained ...
Popular Twitter spam detection methods include statistical techniques, graph-based methods, blocked lists [10], anti-spam strategies [11], and machine learning ...
An important step of applying machine learning for Twitter spam detection is feature engineering. Existing works mainly use URL based features, meta-data based ...
In this paper, we present a new approach based on deep learning (DL) techniques. Our approach leverages both on tweet text as well as users' meta-data.
Researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter.
Missing: learnt | Show results with:learnt
Scalable Learning Framework for Detecting New Types of Twitter ...
pmc.ncbi.nlm.nih.gov › PMC11014207
Apr 2, 2024 · This study proposes an anomaly detection-based framework to detect new Twitter spam, which works by modeling the characteristics of non-spam ...
Hence, we propose a classification based on different feature selection analyses, namely content analysis, user analysis, tweet analysis, network analysis, and ...
Feb 27, 2024 · In this paper, we propose a new hybrid architecture that combines Principal Component Analysis (PCA) with Convolutional Neural Network (CNN) to give birth to a ...