Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative Prediction
Common legislative prediction methods often emphasize bill content or social relationships. This paper, motivated by the insight that similar policy texts reflect comparable political ideologies and can lead to similar voting outcomes, proposes a deep learning method that exploits attention mechanis...
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| Language: | English |
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Tsinghua University Press
2025-06-01
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| Series: | Journal of Social Computing |
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| Online Access: | https://www.sciopen.com/article/10.23919/JSC.2025.0005 |
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| author | Bofeng Wang Yanyan Li Cong Xu |
| author_facet | Bofeng Wang Yanyan Li Cong Xu |
| author_sort | Bofeng Wang |
| collection | DOAJ |
| description | Common legislative prediction methods often emphasize bill content or social relationships. This paper, motivated by the insight that similar policy texts reflect comparable political ideologies and can lead to similar voting outcomes, proposes a deep learning method that exploits attention mechanisms to incorporate semantic similarity between bills into legislative prediction models. Our approach uses attention scores to identify bills that are most similar to the one being predicted, and combines the encoded features of these similar bills as additional auxiliary information. By integrating these related features, the model goes beyond the semantic information of individual bills, leading to a more comprehensive use of roll-call data. Empirical results show that utilizing bill similarity along with traditional social relationships, voter characteristics, and bill content significantly improves performance in terms of accuracy, recall, precision, and F1 score compared to models that ignore bill similarity. The results also confirm that legislators tend to maintain consistent views or voting patterns on bills that are similar in nature. In addition, we demonstrate that the attention mechanism is more effective than conventional similarity measures, such as cosine similarity and Euclidean distance, in capturing the similarities between bills. |
| format | Article |
| id | doaj-art-bb958e5aab7d42f7b61bf64d0f3d0485 |
| institution | Kabale University |
| issn | 2688-5255 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Social Computing |
| spelling | doaj-art-bb958e5aab7d42f7b61bf64d0f3d04852025-08-20T03:29:31ZengTsinghua University PressJournal of Social Computing2688-52552025-06-016211212510.23919/JSC.2025.0005Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative PredictionBofeng Wang0Yanyan Li1Cong Xu2School of Mathematics and Information Sciences, Yantai University, Yantai 264005, ChinaSchool of Literature and Journalism, Yantai University, Yantai 264005, ChinaSchool of Computer Science and Technology, East China Normal University, Shanghai 200062, ChinaCommon legislative prediction methods often emphasize bill content or social relationships. This paper, motivated by the insight that similar policy texts reflect comparable political ideologies and can lead to similar voting outcomes, proposes a deep learning method that exploits attention mechanisms to incorporate semantic similarity between bills into legislative prediction models. Our approach uses attention scores to identify bills that are most similar to the one being predicted, and combines the encoded features of these similar bills as additional auxiliary information. By integrating these related features, the model goes beyond the semantic information of individual bills, leading to a more comprehensive use of roll-call data. Empirical results show that utilizing bill similarity along with traditional social relationships, voter characteristics, and bill content significantly improves performance in terms of accuracy, recall, precision, and F1 score compared to models that ignore bill similarity. The results also confirm that legislators tend to maintain consistent views or voting patterns on bills that are similar in nature. In addition, we demonstrate that the attention mechanism is more effective than conventional similarity measures, such as cosine similarity and Euclidean distance, in capturing the similarities between bills.https://www.sciopen.com/article/10.23919/JSC.2025.0005legislative predictionbill similarityattention mechanismroll call datadeep learning |
| spellingShingle | Bofeng Wang Yanyan Li Cong Xu Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative Prediction Journal of Social Computing legislative prediction bill similarity attention mechanism roll call data deep learning |
| title | Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative Prediction |
| title_full | Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative Prediction |
| title_fullStr | Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative Prediction |
| title_full_unstemmed | Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative Prediction |
| title_short | Exploring Bill Similarity with Attention Mechanism for Enhanced Legislative Prediction |
| title_sort | exploring bill similarity with attention mechanism for enhanced legislative prediction |
| topic | legislative prediction bill similarity attention mechanism roll call data deep learning |
| url | https://www.sciopen.com/article/10.23919/JSC.2025.0005 |
| work_keys_str_mv | AT bofengwang exploringbillsimilaritywithattentionmechanismforenhancedlegislativeprediction AT yanyanli exploringbillsimilaritywithattentionmechanismforenhancedlegislativeprediction AT congxu exploringbillsimilaritywithattentionmechanismforenhancedlegislativeprediction |