{"as_of":"2026-08-08T05:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:52af928d66146d8c673d9e489299bd6414fed3f5cd73e52587a8899b407a6a9b","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T22:39:55.150683Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.09097/citation-record","integrity":"/paper/2502.09097/integrity","json":"/paper/2502.09097/citation-record.json","paper":"/paper/2502.09097"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.483038Z","title":"Fake news classification based on content level features,","venue":null,"work_id":"2c0b32fe-abcb-4656-a973-8d47a5f19631","year":2022},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.066222Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:8d1789ea2035ab222129d26a05b9007d855317d5a07b588b3db1203678aef650","observation_id":"dfb3ec74-c0de-47ae-9a2e-e63826bc3244","resolution":{"observed_at":"2026-08-07T22:39:55.487483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.469043Z","title":"A taxonomy of fake news classification techniques: Survey and implementation aspects,","venue":null,"work_id":"c26ae51c-51c3-40f1-a6b3-ee6feb03c974","year":2022},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.071321Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:92ca8893e10c336e715f50c2d9c9d5a4b0e5bb2d1a9d6395cb57daeec5eb2ca2","observation_id":"56abdc37-facf-4c7b-9d3c-8e1d044e13bc","resolution":{"observed_at":"2026-08-07T22:39:55.473862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06613","last_updated":"2025-05-01T23:47:14Z","snapshot_observed_at":"2026-07-06T18:12:43.301678Z","submitted_at":"2024-05-10T17:22:51Z","title":"Simultaneously detecting spatiotemporal changes with penalized Poisson regression models","version":2},"cited_work":{"arxiv_id":"2405.06613","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.06613","snapshot_observed_at":"2026-08-07T22:39:55.338847Z","title":"Simultaneously detecting spatiotemporal changes with penalized Poisson regression models","venue":"stat.ME","work_id":"fecc6337-e4aa-40bd-a2b7-de3a500e1cc8","year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.075989Z"},"links":{"cited_paper":"/paper/2405.06613","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:510cd270e199ab749aa2f385e3092df29ae23737b53974e2e0ba5ff6f41e443d","observation_id":"ee9ce1a4-07bc-4243-9294-93e0afa73df6","resolution":{"observed_at":"2026-08-07T22:39:55.344140Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11967","last_updated":"2025-01-21T08:26:20Z","snapshot_observed_at":"2026-07-06T20:23:45.022640Z","submitted_at":"2025-01-21T08:26:20Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","version":1},"cited_work":{"arxiv_id":"2501.11967","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.11967","snapshot_observed_at":"2026-08-07T22:39:55.318647Z","title":"A Hybrid Attention Framework for Fake News Detection with Large Language Models","venue":"cs.CL","work_id":"ba1b3c24-c54e-453f-9a79-650b8b50a93f","year":2025},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.081095Z"},"links":{"cited_paper":"/paper/2501.11967","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:60535bf7d270881f47514ff3a23cb9602b3a58fddc52d15780ce7ee86358c1e2","observation_id":"dd34e57d-c717-4e8b-8aaf-b686a0835bac","resolution":{"observed_at":"2026-08-07T22:39:55.323497Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.453559Z","title":"Improving academic skills assessment with nlp and ensemble learning,","venue":null,"work_id":"e655e26d-ad69-4fed-a752-737203be6a06","year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.086376Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:87d87bcafea7c08f6fcaf6f6f2b8780837fecba332559cddd46c33f9bffbf0d7","observation_id":"58c48013-90bd-4bd4-9da6-5a837188040a","resolution":{"observed_at":"2026-08-07T22:39:55.459380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00339","last_updated":"2025-02-01T06:56:17Z","snapshot_observed_at":"2026-08-03T14:34:49.909375Z","submitted_at":"2025-02-01T06:56:17Z","title":"Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00339","snapshot_observed_at":"2026-08-07T22:39:55.090976Z","title":"Challenges and innovations in llm-powered fake news detection: A synthesis of approaches and future directions,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.090976Z"},"links":{"cited_paper":"/paper/2502.00339","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:45f16db9ddd1a93c80f62fbd7a5c250749fec000272e06ede7cc094180dfe77c","observation_id":"01cce3d6-94b9-4c10-aaef-637ac03e3f44","resolution":{"observed_at":"2026-08-07T22:39:55.090976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08475","last_updated":"2025-05-29T04:09:28Z","snapshot_observed_at":"2026-07-06T19:31:36.072099Z","submitted_at":"2024-10-11T03:05:06Z","title":"GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08475","snapshot_observed_at":"2026-08-07T22:39:55.096118Z","title":"Give: Structured reasoning with knowledge graph inspired veracity extrapola- tion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.096118Z"},"links":{"cited_paper":"/paper/2410.08475","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:0c264e2a35674efbc49760abc8468f4dbf67e5e1062611971656aa13c22f99cf","observation_id":"bddaefb9-860e-4d53-810a-738444bde341","resolution":{"observed_at":"2026-08-07T22:39:55.096118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.439590Z","title":"A systematic review of multimodal approaches to online misinformation detection,","venue":null,"work_id":"1088f10a-8597-48be-9eb2-299921d2367f","year":2022},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.100793Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:bbbaf0c534267a8a9ab196cfb431a266b4f27cdcc218eda1ef1c050d01ecbaee","observation_id":"f16ebde5-8072-4a0d-bbe5-a7d6d8392ea6","resolution":{"observed_at":"2026-08-07T22:39:55.444137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.426555Z","title":"Applications of large language models in multimodal learning,","venue":null,"work_id":"39800858-096f-49c8-b84d-4ed8b1183d83","year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.105096Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:5d3efdc58445b3dd9bd3e488053ff38c23057eca6feb0c282e4537410977ba61","observation_id":"36d966cb-3222-46b9-ad7d-cf2a7e68de38","resolution":{"observed_at":"2026-08-07T22:39:55.430687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.00353","last_updated":"2024-12-31T09:00:51Z","snapshot_observed_at":"2026-07-06T20:15:08.295841Z","submitted_at":"2024-12-31T09:00:51Z","title":"RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.00353","snapshot_observed_at":"2026-08-07T22:39:55.109344Z","title":"Rag- instruct: Boosting llms with diverse retrieval-augmented instructions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.109344Z"},"links":{"cited_paper":"/paper/2501.00353","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:a866f213cbbe1ed6a4b66a2fc64452528abf26c1f23dd1b8fcdd507306a7a021","observation_id":"bc6c258a-bcfa-406e-8cce-d8dbe5b6196f","resolution":{"observed_at":"2026-08-07T22:39:55.109344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.06993","last_updated":"2023-05-26T13:28:36Z","snapshot_observed_at":"2026-07-06T14:17:43.815489Z","submitted_at":"2022-11-13T18:59:15Z","title":"GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost","version":3},"cited_work":{"arxiv_id":"2211.06993","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.06993","snapshot_observed_at":"2026-08-07T22:39:55.255862Z","title":"GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost","venue":"cs.CL","work_id":"a0c1fd7b-2cf6-41e7-a1af-7a1efff1c9a2","year":2022},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.114194Z"},"links":{"cited_paper":"/paper/2211.06993","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:d127204a3a07ae36d62f399b12370b47121a6581029ffd8f80ec6ec53d6040d3","observation_id":"f843dc37-32ff-4622-b291-1f5a44f400bf","resolution":{"observed_at":"2026-08-07T22:39:55.260938Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11282","last_updated":"2024-07-19T14:16:35Z","snapshot_observed_at":"2026-08-04T06:44:26.928298Z","submitted_at":"2024-07-15T23:41:11Z","title":"Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.11282","snapshot_observed_at":"2026-08-07T22:39:55.118786Z","title":"Uncertainty is fragile: Manipulating uncertainty in large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.118786Z"},"links":{"cited_paper":"/paper/2407.11282","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:0eff7787aa5d2a23b05ec16cac8bf70acb0545bbef363d044bc59aac4072c3c1","observation_id":"12fb5ba9-14f2-4c04-9d21-971de2d88985","resolution":{"observed_at":"2026-08-07T22:39:55.118786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.00377","last_updated":"2022-05-01T01:48:48Z","snapshot_observed_at":"2026-08-02T08:59:23.262156Z","submitted_at":"2022-05-01T01:48:48Z","title":"Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF","version":1},"cited_work":{"arxiv_id":"2205.00377","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.00377","snapshot_observed_at":"2026-08-07T22:39:55.222043Z","title":"Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF","venue":"cs.CL","work_id":"dc7c06a5-f904-472b-8a3b-8ef350959b6f","year":2022},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.123790Z"},"links":{"cited_paper":"/paper/2205.00377","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:7a681735b175bd235c5cf373dac5bca5f044720addb84a9d38ad0ed4cc13b0f6","observation_id":"a9f5dcce-c4b2-4b49-8639-5d6c6d6b51fb","resolution":{"observed_at":"2026-08-07T22:39:55.227206Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.412372Z","title":"Detection of fake news text classification on covid-19 using deep learning approaches,","venue":null,"work_id":"a30ec061-3bb7-4787-b7f5-9309c88a43f1","year":2021},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.128099Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:8727f1343498b4635afe53b122848afb2ea22815d9084b415a5498c05739c26c","observation_id":"ca0974ce-0f14-4a1f-93e7-aa3ebc06b29b","resolution":{"observed_at":"2026-08-07T22:39:55.417081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.396785Z","title":"Toward a better performance evaluation framework for fake news classification,","venue":null,"work_id":"cad2b4f4-0a7c-4a14-bf4e-6265010294c2","year":2020},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.132154Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:f4e0a5175d8ce8c05c8fd08bae3ae2fd1b150a1b14de90360c282c0565976f35","observation_id":"2372618e-03d3-462c-bd63-a49f8d33d9b6","resolution":{"observed_at":"2026-08-07T22:39:55.402272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05991","last_updated":"2023-10-20T02:20:20Z","snapshot_observed_at":"2026-08-04T12:40:26.081242Z","submitted_at":"2023-10-08T11:29:10Z","title":"Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance","version":3},"cited_work":{"arxiv_id":"2310.05991","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.05991","snapshot_observed_at":"2026-08-07T22:39:55.199863Z","title":"Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance","venue":"cs.CL","work_id":"530491be-80de-412e-a352-e7e76402389a","year":2023},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.135864Z"},"links":{"cited_paper":"/paper/2310.05991","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:d8d44a4d3632a91c9ee3419c0ef11f2f0b35ed2ea978270dc5e1f40ea06429e8","observation_id":"0e011ab1-a0d5-449b-a86d-bb40c16619ad","resolution":{"observed_at":"2026-08-07T22:39:55.206658Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.383946Z","title":"Analysis and classification of fake news using sequential pattern mining,","venue":null,"work_id":"5b8e5f9b-5f60-4365-8037-5f63fe3cac44","year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.139714Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:ecf72f2c4abfcfa257d2bb2733890329d269d3c31e720dbb73cfe2cd576cd4f7","observation_id":"03f0efbc-2331-49ee-b94f-2421e7999458","resolution":{"observed_at":"2026-08-07T22:39:55.388024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.368830Z","title":"Fake news classi- fication using transformer based enhanced lstm and bert,","venue":null,"work_id":"d4010818-6f9f-4c20-8f07-c5cf304e2a1a","year":2022},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.143314Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:4d3324b85452fc472a63ae8f4947b00fe795bba412c0826c65a3cdd1ac83f085","observation_id":"d05289db-2438-475f-98e3-31531546eafc","resolution":{"observed_at":"2026-08-07T22:39:55.373975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T22:39:55.353558Z","title":"Machine learning for fake news classification with optimal feature selection,","venue":null,"work_id":"4eb0c435-ae83-4dcd-aa47-2614efb847b6","year":2022},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.146997Z"},"links":{"citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:a2579e2174f7603c051d00a6cfaaf970e16fb1fe2e5aba5ba723b498f51f58b0","observation_id":"f2253852-a5d8-4a88-bf7a-23c808e2f322","resolution":{"observed_at":"2026-08-07T22:39:55.358772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02801","last_updated":"2025-01-07T10:09:18Z","snapshot_observed_at":"2026-08-03T14:39:49.817854Z","submitted_at":"2024-12-03T20:04:32Z","title":"Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02801","snapshot_observed_at":"2026-08-07T22:39:55.150683Z","title":"Optimization of transformer heart disease prediction model based on particle swarm optimization algorithm,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T22:39:55.150683Z"},"links":{"cited_paper":"/paper/2412.02801","citing_paper":"/paper/2502.09097"},"observation_digest":"sha256:c6304de2710671bdfa3f77fd21b510bbace39068234ff216eba25d4746107a3e","observation_id":"ed9dfafa-d5b2-4579-b7b4-aa80ee62af39","resolution":{"observed_at":"2026-08-07T22:39:55.150683Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.09097","last_updated":"2025-03-02T07:58:08Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T22:35:14.343994Z","submitted_at":"2025-02-13T09:13:23Z","title":"A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":5,"verified_fuzzy":10},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2502.09097."}