A hybrid approach to classifying Wikipedia article quality flaws with feature fusion framework

Wang, Ping; Li, Muyan; Li, Xiaodan; Zhou, Heshen; Hou, Jingrui

doi:10.1016/j.eswa.2021.115089

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…Gated recurrent unit (GRU) ( Wang et al, 2021a ): GRU is also a type of RNN unit. It uses the reset gate and update gate to control the contextual information update.…”

Section: Experiments Resultsmentioning

confidence: 99%

See 1 more Smart Citation

COVID-19 personal health mention detection from tweets using dual convolutional neural network

Luo

Wang

Liu

2022

Expert Systems with Applications

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“…Gated recurrent unit (GRU) ( Wang et al, 2021a ): GRU is also a type of RNN unit. It uses the reset gate and update gate to control the contextual information update.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Research has shown that LSTM could capture the contextual dependency in the text. Gated recurrent unit (GRU) ( Wang et al, 2021a ): GRU is also a type of RNN unit. It uses the reset gate and update gate to control the contextual information update.…”

Section: Experiments Resultsmentioning

confidence: 99%

COVID-19 personal health mention detection from tweets using dual convolutional neural network

Luo

Wang

Liu

2022

Expert Systems with Applications

View full text Add to dashboard Cite

“…Adopting only the features of a single type may not ensure a comprehensive description of papers (Wnag et al. , 2021b).…”

Section: Methodsmentioning

confidence: 99%

Predictable by publication: discovery of early highly cited academic papers based on their own features

Tang

Zhou

2023

LHT

Self Cite

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PurposePredicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.Design/methodology/approachThis research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.FindingsExperimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.Originality/valueBased on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

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Evaluating the quality of student-generated content in learnersourcing: A large language model based approach

Li,

Qian,

Yang

2024

Educ Inf Technol

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A hybrid approach to classifying Wikipedia article quality flaws with feature fusion framework

Cited by 8 publications

References 23 publications

COVID-19 personal health mention detection from tweets using dual convolutional neural network

COVID-19 personal health mention detection from tweets using dual convolutional neural network

Predictable by publication: discovery of early highly cited academic papers based on their own features

Evaluating the quality of student-generated content in learnersourcing: A large language model based approach

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