A comprehensive survey of multimodal fake news detection techniques: advances, challenges, and opportunities

Tufchi, Shivani; Yadav, Ashima; Ahmed, Tanveer

doi:10.1007/s13735-023-00296-3

Cited by 9 publications

(1 citation statement)

References 139 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, text classification based on intelligent algorithms, such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) algorithms, the Naive Bayes algorithm, and so on, has been widely used in fault diagnosis [4], sentiment analysis [5], fact checking [6], and other fields. However, SVMs are primarily used for handling binary classification problems and hardly applied to multi-class problems.…”

Section: Introductionmentioning

confidence: 99%

Building Materials Classification Model Based on Text Data Enhancement and Semantic Feature Extraction

Yan,

Jiao,

Peng

2024

Buildings

View full text Add to dashboard Cite

In order to accurately extract and match carbon emission factors from the Chinese textual building materials list and construct a precise carbon emission factor database, it is crucial to accurately classify the textual building materials. In this study, a novel classification model based on text data enhancement and semantic feature extraction is proposed and applied for building materials classification. Firstly, the explanatory information on the building materials is collected and normalized to construct the original dataset. Then, the Latent Dirichlet Allocation and statistical-language-model-based hybrid ensemble data enhancement methods are explained in detail, and the semantic features closely related to the carbon emission factor are extracted by constructed composite convolutional networks and the transformed word vectors. Finally, the ensemble classification model is designed, constructed, and applied to match the carbon emission factor from the textual building materials. The experimental results show that the proposed model improves the F1Macro score by 4–12% compared to traditional machine learning and deep learning models.

show abstract

Section: Introductionmentioning

confidence: 99%