DiG-MFV: Dual-integrated Graph for Multilingual Fact Verification Article Swipe
Nova Agustina
,
Kusrini Kusrini
,
Ema Utami
,
Tonny Hidayat
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.29207/resti.v9i4.6695
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.29207/resti.v9i4.6695
The proliferation of misinformation in political domains, especially across multilingual platforms, presents a major challenge to maintaining public information integrity. Existing models often fail to effectively verify claims when the evidence spans multiple languages and lacks a structured format. To address this issue, this study proposes a novel architecture called Dual-integrated Graph for Multilingual Fact Verification (DiG-MFV), which combines semantic representations from multilingual language models (i.e., mBERT, XLM-R, and LaBSE) with two graph-based components: an evidence graph and a semantic fusion graph. These components are processed through a dual-path architecture that integrates the outputs from a text encoder and a graph encoder, enabling deeper semantic alignment and cross-evidence reasoning. The PolitiFact dataset was used as the source of claims and evidence. The model was evaluated by using a data split of 70% for training, 20% for validation, and 10% for testing. The training process employed the AdamW optimizer, cross-entropy loss, and regularization techniques, including dropout and early stopping based on the F1-score. The evaluation results show that DiG-MFV with LaBSE achieved an accuracy of 85.80% and an F1-score of 85.70%, outperforming the mBERT and XLM-R variants, and proved to be more effective than the DGMFP baseline model (76.1% accuracy). The model also demonstrated stable convergence during training, indicating its robustness in cross-lingual political fact verification tasks. These findings encourage further exploration in graph-based multilingual fact verification systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.29207/resti.v9i4.6695
- http://jurnal.iaii.or.id/index.php/RESTI/article/download/6695/1104
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4412693608Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.29207/resti.v9i4.6695Digital Object Identifier
- Title
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DiG-MFV: Dual-integrated Graph for Multilingual Fact VerificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-27Full publication date if available
- Authors
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Nova Agustina, Kusrini Kusrini, Ema Utami, Tonny HidayatList of authors in order
- Landing page
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https://doi.org/10.29207/resti.v9i4.6695Publisher landing page
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https://jurnal.iaii.or.id/index.php/RESTI/article/download/6695/1104Direct link to full text PDF
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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://jurnal.iaii.or.id/index.php/RESTI/article/download/6695/1104Direct OA link when available
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Dig, Computer science, Dual (grammatical number), Graph, Programming language, Operating system, Embedded system, World Wide Web, Theoretical computer science, Art, LiteratureTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.used | 113 |
| abstract_inverted_index.when | 28 |
| abstract_inverted_index.with | 70, 168 |
| abstract_inverted_index.AdamW | 146 |
| abstract_inverted_index.DGMFP | 194 |
| abstract_inverted_index.Graph | 51 |
| abstract_inverted_index.LaBSE | 169 |
| abstract_inverted_index.These | 82, 216 |
| abstract_inverted_index.XLM-R | 184 |
| abstract_inverted_index.based | 158 |
| abstract_inverted_index.early | 156 |
| abstract_inverted_index.graph | 76, 100 |
| abstract_inverted_index.lacks | 35 |
| abstract_inverted_index.loss, | 149 |
| abstract_inverted_index.mBERT | 182 |
| abstract_inverted_index.major | 13 |
| abstract_inverted_index.model | 122, 196, 200 |
| abstract_inverted_index.novel | 47 |
| abstract_inverted_index.often | 22 |
| abstract_inverted_index.spans | 31 |
| abstract_inverted_index.split | 129 |
| abstract_inverted_index.study | 44 |
| abstract_inverted_index.using | 126 |
| abstract_inverted_index.which | 57 |
| abstract_inverted_index.(76.1% | 197 |
| abstract_inverted_index.(i.e., | 65 |
| abstract_inverted_index.85.80% | 174 |
| abstract_inverted_index.LaBSE) | 69 |
| abstract_inverted_index.XLM-R, | 67 |
| abstract_inverted_index.across | 8 |
| abstract_inverted_index.called | 49 |
| abstract_inverted_index.claims | 27, 118 |
| abstract_inverted_index.deeper | 103 |
| abstract_inverted_index.during | 205 |
| abstract_inverted_index.fusion | 80 |
| abstract_inverted_index.graph. | 81 |
| abstract_inverted_index.issue, | 42 |
| abstract_inverted_index.mBERT, | 66 |
| abstract_inverted_index.models | 21, 64 |
| abstract_inverted_index.proved | 187 |
| abstract_inverted_index.public | 17 |
| abstract_inverted_index.source | 116 |
| abstract_inverted_index.stable | 203 |
| abstract_inverted_index.tasks. | 215 |
| abstract_inverted_index.verify | 26 |
| abstract_inverted_index.85.70%, | 179 |
| abstract_inverted_index.DiG-MFV | 167 |
| abstract_inverted_index.address | 40 |
| abstract_inverted_index.dataset | 111 |
| abstract_inverted_index.dropout | 154 |
| abstract_inverted_index.encoder | 97 |
| abstract_inverted_index.format. | 38 |
| abstract_inverted_index.further | 219 |
| abstract_inverted_index.outputs | 93 |
| abstract_inverted_index.process | 143 |
| abstract_inverted_index.results | 164 |
| abstract_inverted_index.through | 86 |
| abstract_inverted_index.Existing | 20 |
| abstract_inverted_index.F1-score | 177 |
| abstract_inverted_index.accuracy | 172 |
| abstract_inverted_index.achieved | 170 |
| abstract_inverted_index.baseline | 195 |
| abstract_inverted_index.combines | 58 |
| abstract_inverted_index.domains, | 6 |
| abstract_inverted_index.employed | 144 |
| abstract_inverted_index.enabling | 102 |
| abstract_inverted_index.encoder, | 101 |
| abstract_inverted_index.evidence | 30, 75 |
| abstract_inverted_index.findings | 217 |
| abstract_inverted_index.language | 63 |
| abstract_inverted_index.multiple | 32 |
| abstract_inverted_index.presents | 11 |
| abstract_inverted_index.proposes | 45 |
| abstract_inverted_index.semantic | 59, 79, 104 |
| abstract_inverted_index.stopping | 157 |
| abstract_inverted_index.systems. | 226 |
| abstract_inverted_index.testing. | 140 |
| abstract_inverted_index.training | 142 |
| abstract_inverted_index.F1-score. | 161 |
| abstract_inverted_index.alignment | 105 |
| abstract_inverted_index.challenge | 14 |
| abstract_inverted_index.dual-path | 88 |
| abstract_inverted_index.effective | 191 |
| abstract_inverted_index.encourage | 218 |
| abstract_inverted_index.evaluated | 124 |
| abstract_inverted_index.evidence. | 120 |
| abstract_inverted_index.including | 153 |
| abstract_inverted_index.languages | 33 |
| abstract_inverted_index.political | 5, 212 |
| abstract_inverted_index.processed | 85 |
| abstract_inverted_index.training, | 133, 206 |
| abstract_inverted_index.variants, | 185 |
| abstract_inverted_index.(DiG-MFV), | 56 |
| abstract_inverted_index.PolitiFact | 110 |
| abstract_inverted_index.accuracy). | 198 |
| abstract_inverted_index.components | 83 |
| abstract_inverted_index.especially | 7 |
| abstract_inverted_index.evaluation | 163 |
| abstract_inverted_index.indicating | 207 |
| abstract_inverted_index.integrates | 91 |
| abstract_inverted_index.integrity. | 19 |
| abstract_inverted_index.optimizer, | 147 |
| abstract_inverted_index.platforms, | 10 |
| abstract_inverted_index.reasoning. | 108 |
| abstract_inverted_index.robustness | 209 |
| abstract_inverted_index.structured | 37 |
| abstract_inverted_index.components: | 73 |
| abstract_inverted_index.convergence | 204 |
| abstract_inverted_index.effectively | 25 |
| abstract_inverted_index.exploration | 220 |
| abstract_inverted_index.graph-based | 72, 222 |
| abstract_inverted_index.information | 18 |
| abstract_inverted_index.maintaining | 16 |
| abstract_inverted_index.techniques, | 152 |
| abstract_inverted_index.validation, | 136 |
| abstract_inverted_index.Multilingual | 53 |
| abstract_inverted_index.Verification | 55 |
| abstract_inverted_index.architecture | 48, 89 |
| abstract_inverted_index.demonstrated | 202 |
| abstract_inverted_index.multilingual | 9, 62, 223 |
| abstract_inverted_index.verification | 214, 225 |
| abstract_inverted_index.cross-entropy | 148 |
| abstract_inverted_index.cross-lingual | 211 |
| abstract_inverted_index.outperforming | 180 |
| abstract_inverted_index.proliferation | 1 |
| abstract_inverted_index.cross-evidence | 107 |
| abstract_inverted_index.misinformation | 3 |
| abstract_inverted_index.regularization | 151 |
| abstract_inverted_index.Dual-integrated | 50 |
| abstract_inverted_index.representations | 60 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.13490959 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |