Novel authorship verification model for social media accounts compromised by a human

Alterkavı, Suleyman; Erbay, Hasan

doi:10.1007/s11042-020-10361-2

Cited by 10 publications

(2 citation statements)

References 20 publications

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“…The authors of the article [16] applied authorship identification to prevent the spread of malicious messages on social media. The proposed model for extracting informative features used XGBoost as a preprocessor.…”

Section: Related Work On Classical Machine Learning Methods and Deep Neural Network For Authorship Attributionmentioning

confidence: 99%

Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

et al. 2021

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Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is a continuation of the series of studies aimed at the identification of the Russian-language text’s author and reducing the required text volume. The focus of the study was aimed at the attribution of textual data created as a product of human online activity. The effectiveness of the models was evaluated on the two Russian-language datasets: literary texts and short comments from users of social networks. Classical machine learning (ML) algorithms, popular neural networks (NN) architectures, and their hybrids, including convolutional neural network (CNN), networks with long short-term memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and fastText, that have not been used in previous studies, were applied to solve the problem. A particular experiment was devoted to the selection of informative features using genetic algorithms (GA) and evaluation of the classifier trained on the optimal feature space. Using fastText or a combination of support vector machine (SVM) with GA reduced the time costs by half in comparison with deep NNs with comparable accuracy. The average accuracy for literary texts was 80.4% using SVM combined with GA, 82.3% using deep NNs, and 82.1% using fastText. For social media comments, results were 66.3%, 73.2%, and 68.1%, respectively.

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Section: Related Work On Classical Machine Learning Methods and Deep Neural Network For Authorship Attributionmentioning

confidence: 99%

Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

et al. 2021

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“…On the other hand, the introduction of deep learning in the artificial intelligence field along with advances in computational power led computerized decision systems to spread diverge fields, such as medicine [6], agriculture [7], and [8,9]. Regarding the content of the current study, different approaches such as fuzzy model, artificial neural network, regression model, empiric model, and long short term memory (LSTM) unit network are used for solar irradiance predictions [10].…”

Section: Introductionmentioning

confidence: 99%

Solar irradiation forecastby deep learning architectures

et al. 2022

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Global solar irradiation data is a crucial component to measure solar energy potential when we plan, size, and design solar photovoltaic fields. Often, due to the absence of measuring equipment at meteorological stations, data for the place of interest are not available. However, solar irradiation can be estimated by ordinary meteorological data such as humidity, and air temperature. Herein we propose two different deep learning methods, one based on a deep neural network regression and the other based on multivariate long short term memory unit networks, to estimate solar irradiation at given locations. Validation criteria include mean absolute error, mean squared error, and coefficient of determination (R2 value). According to the simulation results, multivariate long short term memory unit networks performs slightly better than deep neural network. Even though both have very close R2 values, multivariate long short term memory?s R2 values are more consistent. The same is true for mean squared error and mean absolute error.

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