A novel approach to fake news classification using LSTM-based deep learning models
The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-01-01
|
| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2023.1320800/full |
| _version_ | 1797362396100034560 |
|---|---|
| author | Halyna Padalko Halyna Padalko Halyna Padalko Vasyl Chomko Dmytro Chumachenko |
| author_facet | Halyna Padalko Halyna Padalko Halyna Padalko Vasyl Chomko Dmytro Chumachenko |
| author_sort | Halyna Padalko |
| collection | DOAJ |
| description | The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts. |
| first_indexed | 2024-03-08T16:07:26Z |
| format | Article |
| id | doaj.art-772dad495f5e4f87a6eb9d42909e2c9d |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-03-08T16:07:26Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-772dad495f5e4f87a6eb9d42909e2c9d2024-01-08T05:05:31ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2024-01-01610.3389/fdata.2023.13208001320800A novel approach to fake news classification using LSTM-based deep learning modelsHalyna Padalko0Halyna Padalko1Halyna Padalko2Vasyl Chomko3Dmytro Chumachenko4Mathematical Modelling and Artificial Intelligence Department, National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, UkraineUbiquitous Health Technology Lab, University of Waterloo, Waterloo, ON, CanadaGlobal Governance Department, Balsillie School of International Affairs, Waterloo, ON, CanadaSystem Design Engineering Department, University of Waterloo, Waterloo, ON, CanadaMathematical Modelling and Artificial Intelligence Department, National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, UkraineThe rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.https://www.frontiersin.org/articles/10.3389/fdata.2023.1320800/fullmisinformationdisinformationfake newsdeep learningLSTMBiLSTM |
| spellingShingle | Halyna Padalko Halyna Padalko Halyna Padalko Vasyl Chomko Dmytro Chumachenko A novel approach to fake news classification using LSTM-based deep learning models Frontiers in Big Data misinformation disinformation fake news deep learning LSTM BiLSTM |
| title | A novel approach to fake news classification using LSTM-based deep learning models |
| title_full | A novel approach to fake news classification using LSTM-based deep learning models |
| title_fullStr | A novel approach to fake news classification using LSTM-based deep learning models |
| title_full_unstemmed | A novel approach to fake news classification using LSTM-based deep learning models |
| title_short | A novel approach to fake news classification using LSTM-based deep learning models |
| title_sort | novel approach to fake news classification using lstm based deep learning models |
| topic | misinformation disinformation fake news deep learning LSTM BiLSTM |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2023.1320800/full |
| work_keys_str_mv | AT halynapadalko anovelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT halynapadalko anovelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT halynapadalko anovelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT vasylchomko anovelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT dmytrochumachenko anovelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT halynapadalko novelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT halynapadalko novelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT halynapadalko novelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT vasylchomko novelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels AT dmytrochumachenko novelapproachtofakenewsclassificationusinglstmbaseddeeplearningmodels |