A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

Madisetty, Sreekanth; Desarkar, Maunendra Sankar

doi:10.1109/tcss.2018.2878852

Cited by 124 publications

(39 citation statements)

References 25 publications

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“…Fake tweets [ 26 ] can be defined as if it contains the incorrect time or location related to the need and availability of resources or link to the misleading information, etc., is called fake tweets. Spam tweets [ 8 , 19 , 26 ] can be defined as if it contains links to the advertisements or loans or some other irrelevant content, etc., is called spam tweet.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…are helpful for collecting situational information [ 13 ] during a disaster like an earthquake, floods, disease outbreaks [ 25 ], etc. During these events, minor tweets are posted relevant to the specific classes such as infrastructure damage, resources [ 6 , 33 ], service requests [ 24 ], etc., and also spam tweets, communal tweets and emotion information are posted [ 8 , 16 , 17 , 19 , 31 , 38 ]. Therefore, it is required to design the powerful methodologies for the detection of specific class tweets (like Need, Availability of resources, etc.…”

Section: Introductionmentioning

confidence: 99%

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A stacked convolutional neural network for detecting the resource tweets during a disaster

Madichetty

Sridevi

2020

Multimed Tools Appl

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Social media platform like Twitter is one of the primary sources for sharing real-time information at the time of events such as disasters, political events, etc. Detecting the resource tweets during a disaster is an essential task because tweets contain different types of information such as infrastructure damage, resources, opinions and sympathies of disaster events, etc. Tweets are posted related to Need and Availability of Resources (NAR) by humanitarian organizations and victims. Hence, reliable methodologies are required for detecting the NAR tweets during a disaster. The existing works don’t focus well on NAR tweets detection and also had poor performance. Hence, this paper focus on detection of NAR tweets during a disaster. Existing works often use features and appropriate machine learning algorithms on several Natural Language Processing (NLP) tasks. Recently, there is a wide use of Convolutional Neural Networks (CNN) in text classification problems. However, it requires a large amount of manual labeled data. There is no such large labeled data is available for NAR tweets during a disaster. To overcome this problem, stacking of Convolutional Neural Networks with traditional feature based classifiers is proposed for detecting the NAR tweets. In our approach, we propose several informative features such as aid, need, food, packets, earthquake, etc. are used in the classifier and CNN. The learned features (output of CNN and classifier with informative features) are utilized in another classifier (meta-classifier) for detection of NAR tweets. The classifiers such as SVM, KNN, Decision tree, and Naive Bayes are used in the proposed model. From the experiments, we found that the usage of KNN (base classifier) and SVM (meta classifier) with the combination of CNN in the proposed model outperform the other algorithms. This paper uses 2015 and 2016 Nepal and Italy earthquake datasets for experimentation. The experimental results proved that the proposed model achieves the best accuracy compared to baseline methods.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A stacked convolutional neural network for detecting the resource tweets during a disaster

Madichetty

Sridevi

2020

Multimed Tools Appl

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“…For example, Tang et al [17] proposed an ensemble method using three CS-SVMs with different parameters as base classifiers, combined with resampling technology, and achieved good performances for microblog spam detection. Madisetty et al [27] developed an ensemble method involving five CNNs and a feature-based model; the metaclassifier used a multilayer neural network and achieved a good performance on Twitter. Thus, we chose heterogeneous ensemble learning as the basic framework of spam detection.…”

Section: Spam Detection Approachesmentioning

confidence: 99%

A Heterogeneous Ensemble Learning Framework for Spam Detection in Social Networks with Imbalanced Data

Zhao

Yang

et al. 2020

Applied Sciences

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The popularity of social networks provides people with many conveniences, but their rapid growth has also attracted many attackers. In recent years, the malicious behavior of social network spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have mined the behavior characteristics of spammers and have obtained good results by applying machine learning algorithms to identify spammers in social networks. However, most of these studies overlook class imbalance situations that exist in real world data. In this paper, we propose a heterogeneous stacking-based ensemble learning framework to ameliorate the impact of class imbalance on spam detection in social networks. The proposed framework consists of two main components, a base module and a combining module. In the base module, we adopt six different base classifiers and utilize this classifier diversity to construct new ensemble input members. In the combination module, we introduce cost sensitive learning into deep neural network training. By setting different costs for misclassification and dynamically adjusting the weights of the prediction results of the base classifiers, we can integrate the input members and aggregate the classification results. The experimental results show that our framework effectively improves the spam detection rate on imbalanced datasets.

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“…This thesis focused on a Neural Network-based Ensemble approach for detecting Spam in Twitter [28]. In this study, 5 CNNs and one feature-based model were used in the ensemble.…”

Section: A Neural Network-based Ensemble Approach For Spam Detection mentioning

confidence: 99%

<strong>Cooking is All About People: Comment Classification on Cookery Channels Using Bert and Classification Models (Malayalam-English Mix-Code)</strong>

Kazhuparambil

Kaushik

2020

Preprint

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The scope of a lucrative career promoted by Google through its video distribution platform YouTube 1 has attracted a large number of users to become content creators. An important aspect of this line of work is the feedback received in the form of comments which show how well the content is being received by the audience. However, volume of comments coupled with spam and limited tools for comment classification makes it virtually impossible for a creator to go through each and every comment and gather constructive feedback. Automatic classification of comments is a challenge even for established classification models, since comments are often of variable lengths riddled with slang, symbols and abbreviations. This is a greater challenge where comments are multilingual as the messages are often rife with the respective vernacular. In this work, we have evaluated top-performing classification models and four different vectorizers, for classifying comments which are a mix of different combinations of English and Malayalam (only English, only Malayalam and Mix of English and Malayalam). The statistical analysis of results indicates that Multinomial Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest and Decision Trees offer similar level of accuracy in comment classification. Further, we have also evaluated 3 multilingual sub-types of the novel NLP language model, BERT and compared its’ performance to the conventional machine learning classification techniques. XLM was the top-performing BERT model with an accuracy of 67.31%. Random Forest with Term Frequency Vectorizer was the best the top-performing model out of all the traditional classification models with an accuracy of 63.59%.

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A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

Cited by 124 publications

References 25 publications

A stacked convolutional neural network for detecting the resource tweets during a disaster

A stacked convolutional neural network for detecting the resource tweets during a disaster

A Heterogeneous Ensemble Learning Framework for Spam Detection in Social Networks with Imbalanced Data

<strong>Cooking is All About People: Comment Classification on Cookery Channels Using Bert and Classification Models (Malayalam-English Mix-Code)</strong>

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