GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection Article Swipe
Pummy Dhiman
,
Amandeep Kaur
,
Deepali Gupta
,
Sapna Juneja
,
Ali Nauman
,
Ghulam Muhammad
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.heliyon.2024.e35865
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.heliyon.2024.e35865
The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques-BERT's deep contextual understanding and the generative capabilities of GPT-to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.heliyon.2024.e35865
- OA Status
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All OpenAlex metadata
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https://openalex.org/W4401357833Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.heliyon.2024.e35865Digital Object Identifier
- Title
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GBERT: A hybrid deep learning model based on GPT-BERT for fake news detectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-08-01Full publication date if available
- Authors
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Pummy Dhiman, Amandeep Kaur, Deepali Gupta, Sapna Juneja, Ali Nauman, Ghulam MuhammadList of authors in order
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https://doi.org/10.1016/j.heliyon.2024.e35865Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.heliyon.2024.e35865Direct OA link when available
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Deep learning, Fake news, Artificial intelligence, Computer science, Machine learning, Internet privacyTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 24, 2024: 5Per-year citation counts (last 5 years)
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70Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2742330194, https://openalex.org/W4320728788, https://openalex.org/W3208189837, https://openalex.org/W4367627723, https://openalex.org/W6792304553, https://openalex.org/W6805172842, https://openalex.org/W6739901393, https://openalex.org/W4200069439, https://openalex.org/W6838671621, https://openalex.org/W6758335716, https://openalex.org/W4205754643, https://openalex.org/W6851700545, https://openalex.org/W3108671495, https://openalex.org/W4389160627, https://openalex.org/W6717661696, https://openalex.org/W6858055675, https://openalex.org/W3152519896, https://openalex.org/W3118463782, https://openalex.org/W6787695717, https://openalex.org/W3205757788, https://openalex.org/W4319320113, https://openalex.org/W4387807044, https://openalex.org/W4205633808, https://openalex.org/W6803118573, https://openalex.org/W6796653167, https://openalex.org/W4281898186, https://openalex.org/W6852481957, https://openalex.org/W4288538225, https://openalex.org/W6855604291, https://openalex.org/W6745474050, https://openalex.org/W6735963731, https://openalex.org/W3164279875, https://openalex.org/W4210299703, https://openalex.org/W4213079790, https://openalex.org/W4226297919, https://openalex.org/W4200097352, https://openalex.org/W6745744838, https://openalex.org/W4391872494, https://openalex.org/W6864762050, https://openalex.org/W6787128884, https://openalex.org/W4391360895, https://openalex.org/W6861276541, https://openalex.org/W6868355470, https://openalex.org/W6851655570, https://openalex.org/W6846100707, https://openalex.org/W6850501444, https://openalex.org/W6849859687, https://openalex.org/W2945044133, https://openalex.org/W4254639601, https://openalex.org/W4362500576, https://openalex.org/W4245696960, https://openalex.org/W4303857474, https://openalex.org/W4250879943, https://openalex.org/W4320803104, https://openalex.org/W4391503438, https://openalex.org/W4200177521, https://openalex.org/W4250411456, https://openalex.org/W4378072572, https://openalex.org/W4399377864, https://openalex.org/W3138979178, https://openalex.org/W3012090365, https://openalex.org/W4367397315, https://openalex.org/W4388018269, https://openalex.org/W4200373042, https://openalex.org/W4323655380, https://openalex.org/W2604264634, https://openalex.org/W4395070449, https://openalex.org/W3205684765, https://openalex.org/W4255102369, https://openalex.org/W4385245566 |
| referenced_works_count | 70 |
| abstract_inverted_index.% | 181, 184, 187, 192 |
| abstract_inverted_index.a | 33, 67, 78, 91, 103, 118, 159, 163, 190, 216 |
| abstract_inverted_index.As | 77 |
| abstract_inverted_index.F1 | 193 |
| abstract_inverted_index.In | 100 |
| abstract_inverted_index.an | 85 |
| abstract_inverted_index.as | 96 |
| abstract_inverted_index.be | 53, 215 |
| abstract_inverted_index.in | 40, 227 |
| abstract_inverted_index.is | 25, 48, 56, 60, 114 |
| abstract_inverted_index.it | 213 |
| abstract_inverted_index.of | 43, 94, 120, 145, 156, 162, 202, 224 |
| abstract_inverted_index.on | 27, 172 |
| abstract_inverted_index.or | 98 |
| abstract_inverted_index.to | 22, 52, 62, 69, 89 |
| abstract_inverted_index.GPT | 167 |
| abstract_inverted_index.The | 0, 195 |
| abstract_inverted_index.and | 59, 74, 125, 132, 152, 168, 177, 189, 210 |
| abstract_inverted_index.are | 170 |
| abstract_inverted_index.but | 55 |
| abstract_inverted_index.can | 20, 214 |
| abstract_inverted_index.era | 2 |
| abstract_inverted_index.for | 11, 15, 206, 219 |
| abstract_inverted_index.get | 21 |
| abstract_inverted_index.has | 3, 37, 83 |
| abstract_inverted_index.new | 104 |
| abstract_inverted_index.the | 29, 41, 72, 134, 142, 153, 200, 203, 228 |
| abstract_inverted_index.two | 173 |
| abstract_inverted_index.BERT | 169 |
| abstract_inverted_index.Both | 166 |
| abstract_inverted_index.Fake | 46, 64 |
| abstract_inverted_index.Now, | 18 |
| abstract_inverted_index.This | 139 |
| abstract_inverted_index.also | 38 |
| abstract_inverted_index.best | 143 |
| abstract_inverted_index.both | 146 |
| abstract_inverted_index.deep | 149 |
| abstract_inverted_index.easy | 8 |
| abstract_inverted_index.fake | 44, 135, 207, 225 |
| abstract_inverted_index.from | 111, 129 |
| abstract_inverted_index.have | 178 |
| abstract_inverted_index.just | 32 |
| abstract_inverted_index.know | 23 |
| abstract_inverted_index.news | 47, 65, 81, 136, 208, 226 |
| abstract_inverted_index.test | 197 |
| abstract_inverted_index.text | 95 |
| abstract_inverted_index.that | 50, 116, 212 |
| abstract_inverted_index.this | 36, 101, 221 |
| abstract_inverted_index.true | 54 |
| abstract_inverted_index.what | 24 |
| abstract_inverted_index.with | 7, 31 |
| abstract_inverted_index.(GPT) | 124 |
| abstract_inverted_index.95.13 | 183 |
| abstract_inverted_index.95.30 | 180 |
| abstract_inverted_index.96.23 | 191 |
| abstract_inverted_index.97.35 | 186 |
| abstract_inverted_index.bogus | 80 |
| abstract_inverted_index.false | 58 |
| abstract_inverted_index.given | 92, 164 |
| abstract_inverted_index.globe | 30 |
| abstract_inverted_index.going | 26 |
| abstract_inverted_index.issue | 42, 223 |
| abstract_inverted_index.news. | 45 |
| abstract_inverted_index.piece | 93 |
| abstract_inverted_index.poses | 66 |
| abstract_inverted_index.text. | 165 |
| abstract_inverted_index.(BERT) | 131 |
| abstract_inverted_index.GPT-to | 157 |
| abstract_inverted_index.access | 10 |
| abstract_inverted_index.around | 28 |
| abstract_inverted_index.become | 84 |
| abstract_inverted_index.called | 106 |
| abstract_inverted_index.click; | 34 |
| abstract_inverted_index.create | 158 |
| abstract_inverted_index.domain | 88 |
| abstract_inverted_index.global | 16, 222 |
| abstract_inverted_index.mobile | 12 |
| abstract_inverted_index.paper, | 102 |
| abstract_inverted_index.people | 19 |
| abstract_inverted_index.public | 75 |
| abstract_inverted_index.score. | 194 |
| abstract_inverted_index.social | 5 |
| abstract_inverted_index.threat | 68 |
| abstract_inverted_index.users, | 13 |
| abstract_inverted_index.(GBERT) | 113 |
| abstract_inverted_index.Encoder | 109, 127 |
| abstract_inverted_index.content | 49 |
| abstract_inverted_index.corpora | 176 |
| abstract_inverted_index.digital | 1, 229 |
| abstract_inverted_index.genuine | 97 |
| abstract_inverted_index.result, | 79 |
| abstract_inverted_index.results | 198 |
| abstract_inverted_index.suggest | 211 |
| abstract_inverted_index.actually | 57 |
| abstract_inverted_index.allowing | 14 |
| abstract_inverted_index.approach | 218 |
| abstract_inverted_index.attained | 179 |
| abstract_inverted_index.combines | 141 |
| abstract_inverted_index.defraud. | 63 |
| abstract_inverted_index.economy, | 73 |
| abstract_inverted_index.emerging | 86 |
| abstract_inverted_index.expanded | 4 |
| abstract_inverted_index.exposure | 6 |
| abstract_inverted_index.features | 144 |
| abstract_inverted_index.harmony, | 70 |
| abstract_inverted_index.however, | 35 |
| abstract_inverted_index.identify | 90 |
| abstract_inverted_index.indicate | 199 |
| abstract_inverted_index.internet | 9 |
| abstract_inverted_index.opinion. | 76 |
| abstract_inverted_index.pretends | 51 |
| abstract_inverted_index.problem. | 138 |
| abstract_inverted_index.proposed | 115 |
| abstract_inverted_index.research | 87 |
| abstract_inverted_index.resulted | 39 |
| abstract_inverted_index.accuracy, | 182 |
| abstract_inverted_index.addresses | 133 |
| abstract_inverted_index.benchmark | 175 |
| abstract_inverted_index.detection | 82, 209 |
| abstract_inverted_index.framework | 105, 140, 205 |
| abstract_inverted_index.leverages | 117 |
| abstract_inverted_index.politics, | 71 |
| abstract_inverted_index.promising | 217 |
| abstract_inverted_index.Generative | 107, 121 |
| abstract_inverted_index.contextual | 150 |
| abstract_inverted_index.fine-tuned | 171, 204 |
| abstract_inverted_index.generative | 154 |
| abstract_inverted_index.landscape. | 230 |
| abstract_inverted_index.precision, | 185 |
| abstract_inverted_index.real-world | 174 |
| abstract_inverted_index.combination | 119 |
| abstract_inverted_index.eradicating | 220 |
| abstract_inverted_index.fraudulent. | 99 |
| abstract_inverted_index.pre-trained | 122 |
| abstract_inverted_index.statistical | 196 |
| abstract_inverted_index.transformer | 123 |
| abstract_inverted_index.Transformers | 112, 130 |
| abstract_inverted_index.capabilities | 155 |
| abstract_inverted_index.cutting-edge | 147 |
| abstract_inverted_index.disseminated | 61 |
| abstract_inverted_index.sensitivity, | 188 |
| abstract_inverted_index.Bidirectional | 108, 126 |
| abstract_inverted_index.comprehensive | 160 |
| abstract_inverted_index.effectiveness | 201 |
| abstract_inverted_index.understanding | 151 |
| abstract_inverted_index.classification | 137 |
| abstract_inverted_index.communication. | 17 |
| abstract_inverted_index.representation | 161 |
| abstract_inverted_index.Representations | 110, 128 |
| abstract_inverted_index.techniques-BERT's | 148 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5055907404, https://openalex.org/A5009241024, https://openalex.org/A5080711681, https://openalex.org/A5048859324, https://openalex.org/A5090587594, https://openalex.org/A5075625518 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I28022161, https://openalex.org/I55240360, https://openalex.org/I74319210 |
| citation_normalized_percentile.value | 0.99850433 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |