An Experimental Evaluation of Data Classification Models for Credibility Based Fake News Detection

Ramkissoon, Amit Neil; Mohammed, Shareeda

doi:10.1109/icdmw51313.2020.00022

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…In the judgment matrix of the information entropy weight distribution method, e n represents the entropy value of the nth feature in the evaluation set, M max − M min is the divergence of the user information feature, and p nn represents the ratio of the entropy value of any two items in the evaluation set, as shown in equations ( 7) and (8).…”

Section: Weight Distribution Methods Of Information Entropymentioning

confidence: 99%

“…TNs generate a large number of false and malicious behaviors or information in social networks, and they increase their credibility within social networks by inventing profile information [6]. To ensure the accuracy and rationality of user credibility evaluation, it is necessary to strengthen the processing and quantification of each feature in the user profile information and user-generated content information, as well as improving the accuracy of user credibility evaluation algorithms [7][8][9]. To further study and solve these problems, researchers at home and abroad have proposed a variety of user credibility evaluation algorithm models and methods [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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S3UCA: Soft-Margin Support Vector Machine-Based Social Network User Credibility Assessment Method

Zhao

Xing

2021

Mobile Information Systems

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Among the algorithms used to assess user credibility in social networks, most of them quantify user information and then calculate the user credibility measure by linear summation. The algorithm above, however, ignores the aliasing of user credibility results under the linear summation dimension, resulting in a low evaluation accuracy. To solve this problem, we propose a user credibility evaluation method based on a soft-margin support-vector machine (SVM). This method transforms the user credibility evaluation dimension from a linear summation dimension to a plane coordinate dimension, which reduces the evaluation errors caused by user aliasing in the classification threshold interval. In the quantization of user information, the ladder assignment method is used to process the user text information and numeric information, and the weight assignment method of information entropy is used to calculate the weight assignment among different feature items, which reduces the errors caused by the inconsistency of the order of magnitude among different types of user information. Simulation results demonstrate the superiority of the proposed method in the user’s credibility evaluation results.

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Section: Weight Distribution Methods Of Information Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

S3UCA: Soft-Margin Support Vector Machine-Based Social Network User Credibility Assessment Method

Zhao

Xing

2021

Mobile Information Systems

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“…The Two Class Boosted Decision Tree model has been proposed by Microsoft Azure Machine Learning Studio (classic). This paper utilises the Two Class Boosted Decision Tree based upon the results stated in (Ramkissoon & Mohammed, 2020). According to (Ramkissoon & Mohammed, 2020) From the experiments performed and the results obtained it is noted that the Two Class Boosted Decision Tree performed the best.…”

Section: Two-class Boosted Decision Tree (Bdt)mentioning

confidence: 99%

“…This paper utilises the Two Class Boosted Decision Tree based upon the results stated in (Ramkissoon & Mohammed, 2020). According to (Ramkissoon & Mohammed, 2020) From the experiments performed and the results obtained it is noted that the Two Class Boosted Decision Tree performed the best. Hence, it can be concluded that based upon our selected dataset the Two Class Boosted Decision Tree is the best method suited for detecting and predicting Credibility Based Fake News.…”

Section: Two-class Boosted Decision Tree (Bdt)mentioning

confidence: 99%

An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging

Ramkissoon

Goodridge

2022

Preprint

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Mobile Adhoc Networks (MANETs) are utilised in a variety of mission critical situations and as such it is important to detect any fake news that exists in such networks. This research combines the power of Veracity, a unique, computational social system with that of Legitimacy, a dedicated ensemble learning technique, to detect Fake News in MANET Messaging. Veracity uses five algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features using computational social intelligence. To validate Veracity, a dataset of publisher credibility-based and message content-based features is generated to predict fake news. To analyse the data features, Legitimacy, a unique ensemble learning prediction model is used. Four analytical methodologies are used to analyse these experimental results. The analysis of the results reports a good performance of the Veracity architecture combined with the Legitimacy model for the task of fake news detection in MANET Messaging.

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“…Any news piece that is purposefully untrue and can be independently verified is considered fake news [7]. The project [8], which found 171,365 stories from 60 fake news websites between the years 2014 and 2016, is one demonstration of the proliferation of false information.…”

Section: Introductionmentioning

confidence: 99%

NISONet: Detecting fake news on social media with a nature-inspired memory efficient and space optimized deep learning approach

Kaliyar,

Goswami,

Narang

2023

Preprint

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Numerous enhancements have been made to the mobile internet, which has led to an increase in people?s attention to posting more multi-modal posts among social media platforms. Detecting fake news continues to be a serious concern in today's social culture. Machine learning and deep learning algorithms have shown significant results in predicting and identifying fake news. In recent studies, existing deep learning 1-D CNN models have proved very effective for text classification problems. Due to a large number of floating-point operations, a large number of layers with many hidden units necessitates an enormous amount of memory space and a high response time. Today, the number of edge devices is growing exponentially, making it difficult to deploy CNN models that have been pre-trained directly on these devices. This paper addresses the compression issue by proposing a particle swarm-based optimization technique to retain the best-hidden units without compromising the classification model's accuracy. In this paper, we have chosen Top-1 accuracy and compression ratio as the dual objective fitness functions, so that accuracy should not degrade excessively after compression. The results of these experiments are examined using the proposed methodologies. The analysis of the results reveals positive performance with the use of the proposed deep neural network.

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An Experimental Evaluation of Data Classification Models for Credibility Based Fake News Detection

Cited by 13 publications

References 12 publications

S3UCA: Soft-Margin Support Vector Machine-Based Social Network User Credibility Assessment Method

S3UCA: Soft-Margin Support Vector Machine-Based Social Network User Credibility Assessment Method

An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging

NISONet: Detecting fake news on social media with a nature-inspired memory efficient and space optimized deep learning approach

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