{"as_of":"2026-08-09T14:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:057b6b05e1701cab0d756372be5181bde0abae8c29bf0f8e7c8041b100f8d050","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:42:51.675687Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2506.01627/citation-record","integrity":"/paper/2506.01627/integrity","json":"/paper/2506.01627/citation-record.json","paper":"/paper/2506.01627"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:42:52.181489Z","title":"Informa- tion credibility on twitter.In Proceedings of the 20th international conference on World wide web.2011, pp","venue":null,"work_id":"e74c1607-520d-4f83-bd42-896b9fb30a16","year":2011},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.494436Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:dad63978e247b51a1124ce88de45848699393f06b4c80d973ecdf193ce835d2d","observation_id":"c4c2085f-2f07-43f2-b75a-8abf439c7a8b","resolution":{"observed_at":"2026-08-07T11:42:52.185612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.171244Z","title":"Rumor has it: Identifying misinformation in mi- croblogs.In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing","venue":null,"work_id":"31a0affc-fd5c-462c-bece-130acf2487d3","year":2011},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.498903Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:d45375d27d13af5ec8ac09a255d6705def5e7b5a86fae740a9c093cbfe327a2c","observation_id":"d9d5b51f-57e5-4565-b091-77dd27e1a58c","resolution":{"observed_at":"2026-08-07T11:42:52.174961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.161670Z","title":"Assessing the credibility of claims on the web.In Proceedings of the 26th International Confer- ence on World Wide Web Companion.2017, pp","venue":null,"work_id":"f9b824d8-9db0-4387-bc58-f2b04a455c47","year":2017},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.502553Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:c518bd8e14fe7f3257aba8555d1b455543b909af3d23eb2314099288f8ea5a97","observation_id":"81d6a91f-acb1-4d5c-83c2-04ddc61c99d2","resolution":{"observed_at":"2026-08-07T11:42:52.165094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.150468Z","title":"Automatic de- tection of rumor on sina weibo.In Proceedings of the ACM SIGKDD workshop on mining data semantics","venue":null,"work_id":"be786a69-dbe3-4300-bc22-f5326de4beca","year":2012},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.507013Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:8b68e37a6f2bf6b87e6b45f14fe6bd683a1b7eb073040644510897522e4e7881","observation_id":"d9e7d08d-35e4-4bf1-94e5-4f2fbcd69b56","resolution":{"observed_at":"2026-08-07T11:42:52.154621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.140105Z","title":"Epidemiological modeling of news and ru- mors on twitter.In Proceedings of the 7th workshop on social network mining and analysis.2013, pp","venue":null,"work_id":"02e176bf-a718-46c5-93ad-335a31747352","year":2013},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.511652Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:13cd14f3e488589647f3d6eb285893193dfaff3a50c538040b81b88205347af9","observation_id":"5abeb163-7e69-4001-8fc7-e5d2dad5d0c6","resolution":{"observed_at":"2026-08-07T11:42:52.144137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.129474Z","title":"Lever- aging the implicit structure within social media for emergent rumor detection,","venue":null,"work_id":"9fd22bf8-f0bf-4365-b96c-c1136e57444d","year":2016},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.515108Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:ec5eb7f7f564dc49b8c9ac46cd072182952c78f65e4a909cbfc3212528b731d7","observation_id":"ebfd236c-8055-449e-9714-862438fe1d5e","resolution":{"observed_at":"2026-08-07T11:42:52.133710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.117521Z","title":"Detect rumors in microblog posts using propagation structure via kernel learning,","venue":null,"work_id":"f78645f0-b224-42cc-acae-e6ea42263fc7","year":2017},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.519193Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:5983b74b6e3ca96c2583304c993dabf29d335a639737b523f0d1a6b29a18c8c6","observation_id":"a0b995ab-273d-44b1-abb7-2debe07ad7a6","resolution":{"observed_at":"2026-08-07T11:42:52.121676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.107326Z","title":"Early detection of fake news on social media through propagation path classifica- tion with recurrent and convolutional networks,","venue":null,"work_id":"096040b9-5bf2-49ef-88e7-83a6090228c3","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.523455Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:c71b97f7b27620226c65ae546c3ce333bd6ad3531e849de2c008cf47ec015631","observation_id":"dc869c92-ba5e-43c8-9b1d-c361080c257c","resolution":{"observed_at":"2026-08-07T11:42:52.111332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.097109Z","title":"CED: credible early detection of social media ru- mors.IEEE Transactions on Knowledge and Data En- gineering.2019","venue":null,"work_id":"48e13f0c-17c1-443d-8fb2-26510049bfe0","year":2019},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.527735Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:e7c740c896c546f2bd6c26fb982e2bd4d079a2ab4f28d2518b219a673b44d3d4","observation_id":"a4cb72b4-3ed0-474c-baff-301102a664df","resolution":{"observed_at":"2026-08-07T11:42:52.101101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.085768Z","title":"J., Wong, K","venue":null,"work_id":"9a1248a1-8743-4b20-9eb0-dbc152658c6b","year":2016},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.531132Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:f9225aad33ae325390e7d84c4057e29be08e8ff2f1a8f2b5e0045ccf27d90903","observation_id":"cbc465e6-753e-4fe8-891f-62fde2bc092e","resolution":{"observed_at":"2026-08-07T11:42:52.089975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.075648Z","title":null,"venue":null,"work_id":"39625bc9-654b-4276-9c67-e61f8e642b75","year":2020},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.535075Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:447d3c3f13956ec5084012f1561aea1b4ce4249b218d5906386bfeee9a76fb3b","observation_id":"199a3a50-2d1c-4fc3-9a59-537421f61e30","resolution":{"observed_at":"2026-08-07T11:42:52.079329Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.065502Z","title":null,"venue":null,"work_id":"ec77121a-a334-4db3-8ac2-d93eaa7f0276","year":2015},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.539210Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:de4822d420796ee780374f6c64c7d9073ed409e28d700abfc807b861fdf71cc9","observation_id":"0b20197e-2588-4d22-8849-c660b2e7c88b","resolution":{"observed_at":"2026-08-07T11:42:52.068731Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.055922Z","title":null,"venue":null,"work_id":"f855b53d-77eb-47a1-81f3-ccc6bde39e10","year":2017},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.543517Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:60c20e3c5a2caf6c64736d5f01fb5109aa88dfda217fc8e03e5840164d3490c1","observation_id":"98138d3b-cd04-4e2e-b9ea-d8789279894c","resolution":{"observed_at":"2026-08-07T11:42:52.059066Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.045435Z","title":"A con- volutional approach for misinformation identification","venue":null,"work_id":"17943397-8756-494f-b736-f263fa3cf049","year":2017},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.547159Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:8a1eb97a9d8dfdc830666d3b9844ea259c17c3f22519cadf65b71a320c77b52c","observation_id":"b4c5d0e6-6fce-45f5-8d15-18a928149071","resolution":{"observed_at":"2026-08-07T11:42:52.049166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.035214Z","title":"Call atten- tion to rumors: Deep attention based recurrent neu- ral networks for early rumor detection.In Pacific-Asia conference on knowledge discovery and data mining","venue":null,"work_id":"7fc855ee-3500-412a-8eef-61e53eab2f0c","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.550788Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:feb44a675187976d3782c0b35a96c4c618db2c780695d99d656424d7d8e8be9b","observation_id":"19365491-9225-4111-a75b-6415cda3f288","resolution":{"observed_at":"2026-08-07T11:42:52.039003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.024654Z","title":null,"venue":null,"work_id":"e2900cc3-f80d-4ea7-a198-520570612aea","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.554539Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:a5b7d542d12720de6cecbf503c6ffb088338379bd737506741af73586f08732a","observation_id":"4471feec-f3fa-45a8-b9e4-ec6b4abd171d","resolution":{"observed_at":"2026-08-07T11:42:52.028738Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.014251Z","title":"Fake news identification on twitter with hybrid cnn and rnn mod- els.In Proceedings of the 9th international conference on social media and society.2018, pp","venue":null,"work_id":"e194b49e-3c05-4208-a15e-708328088560","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.559047Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:bdfe090c260ce18baf31024dffabe676cdc719975772433b28939334a259a0d0","observation_id":"f61328c3-ffd9-4c47-9381-5480c897ea30","resolution":{"observed_at":"2026-08-07T11:42:52.018461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:52.003848Z","title":null,"venue":null,"work_id":"aab1a21c-1cfa-4c89-a789-8ae3a9f54b5b","year":2019},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.562816Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:2e0dd1eb60f235483d3f50520a53081e512706a4e9c24081b050ee2fab0d2890","observation_id":"a19caa91-b152-4ab5-9138-a1b255ca8e5c","resolution":{"observed_at":"2026-08-07T11:42:52.007269Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.993295Z","title":"defend: Explainable fake news detec- tion.In:Proceedings of the 25th ACM SIGKDD Inter- national Conference on Knowledge Discovery & Data Mining.2019","venue":null,"work_id":"875f5632-1627-43db-b59f-3e037e349ac6","year":2019},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.566697Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:b05d840882f0b9104850e6e0f2816cdd292a8ea094495b9d9fee722df1cfa900","observation_id":"b42be414-8205-42bd-9330-f4415fcedee0","resolution":{"observed_at":"2026-08-07T11:42:51.996926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.983506Z","title":null,"venue":null,"work_id":"0b67f273-a0a5-471e-8672-d7b55c04f51c","year":2020},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.570237Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:ef7955f5b648da519fb00e56db46954d35b9c2c1041b596585fb66d5d8fbb9b6","observation_id":"e4db4e4b-9e59-440f-8e9e-e5a4c3078716","resolution":{"observed_at":"2026-08-07T11:42:51.986772Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.972998Z","title":null,"venue":null,"work_id":"96a8e441-f4fb-4363-983b-518495c26b60","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.574811Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:c5159a68c71b6d31085159468086e29bf473bc8ee75f94b47a92bb24d28c4021","observation_id":"b7daa6fc-c683-4822-9b1d-3b28c05f4019","resolution":{"observed_at":"2026-08-07T11:42:51.976219Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.06673","last_updated":"2019-02-10T15:21:45Z","snapshot_observed_at":"2026-08-07T20:20:09.340389Z","submitted_at":"2019-02-10T15:21:45Z","title":"Fake News Detection on Social Media using Geometric Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.06673","snapshot_observed_at":"2026-08-07T11:42:51.578774Z","title":"Fake news detection on so- cial media using geometric deep learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.578774Z"},"links":{"cited_paper":"/paper/1902.06673","citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:3232106ce2fa7047f3143a66558cb32f2774a2f474dc01c9a3e90e3484cbd56c","observation_id":"ceb7c5b9-d42c-4c57-8c6d-abf8fd703919","resolution":{"observed_at":"2026-08-07T11:42:51.578774Z","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":"2026-08-07T11:42:51.962471Z","title":"Graph-based Rumour detection for so- cial media,","venue":null,"work_id":"67857bf0-5ab1-45ce-8f08-45bcd9d8df36","year":2019},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.582465Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:84fddb49933610eed6a7b0c2c7555a7fd1d045244b42cce77db1483465c7f1bb","observation_id":"7acf7ae1-b212-456d-a282-f066ff39a602","resolution":{"observed_at":"2026-08-07T11:42:51.966815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.952612Z","title":"”Rumor Detection on Social Me- dia with Bi-Directional Graph Convolutional Net- works.”Proceedings of the AAAI Conference on Arti- ficial Intelligence.V ol","venue":null,"work_id":"66879487-972f-49cf-a2cb-7cab113f1399","year":2020},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.586318Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:714f23cdb67a71aff24ddeaeed38e099fb504628bb2741e5a1ca2bac12413330","observation_id":"529f3429-3249-415e-9bd7-4e1e394c4c1b","resolution":{"observed_at":"2026-08-07T11:42:51.956009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.941526Z","title":null,"venue":null,"work_id":"ff0ec45e-d6f9-4933-83ed-8f3a41b6a806","year":2020},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.589589Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:58e4768dc3637097df29dc75255aaaf2e1336735a3bc8b5983ef3c1ad7304581","observation_id":"87536cf9-b9b1-4884-96be-d8a4061bfe29","resolution":{"observed_at":"2026-08-07T11:42:51.945458Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.929739Z","title":"Ex- ploiting Microblog Conversation Structures to Detect Rumors.Proceedings of the 28th International Con- ference on Computational Linguistics.2020","venue":null,"work_id":"cac5e376-89d9-4622-b1d5-759efcc069a3","year":2020},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.592837Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:6b603346ecb131b11c6ae204777ea86fbaf20775ccf7539e809438904c6ad996","observation_id":"60271084-4b1f-48de-9338-50f65c289109","resolution":{"observed_at":"2026-08-07T11:42:51.934080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.919213Z","title":"”Birds of a Feather Rumor Together? Exploring Homogeneity and Conversation Structure in Social Media for Rumor De- tection.”IEEE Access.2020","venue":null,"work_id":"acc37b61-938f-44c0-801b-f37ef28a9423","year":2020},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.597191Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:9373a1777177f790a788b44406bdb483b5a06d1a326946e45a276d54e7376d5b","observation_id":"d9c68ad2-cea9-4dab-9119-b0c96ded3139","resolution":{"observed_at":"2026-08-07T11:42:51.922709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.908582Z","title":"R., and Jain, L","venue":null,"work_id":"df162c26-1e9e-48bd-ab92-aaa6595cad06","year":2001},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.601015Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:687d71e0dcfe0f6a70dd78cf532a56cd56e514e10885edb359acc3a6489bcd31","observation_id":"0be25912-4bed-49bb-95fc-3d547dd1ac08","resolution":{"observed_at":"2026-08-07T11:42:51.912088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.897831Z","title":"”Learning long-term dependencies with gradient de- scent is difficult.”IEEE transactions on neural net- works5.2 (1994): 157-166","venue":null,"work_id":"e02b8fc2-595c-499c-9982-1721712ab1fc","year":1994},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.604633Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:44e432e7eae14e42973c819131cf118a32ebbad587a226516d29b2a08253b97e","observation_id":"3a56d469-c099-4d54-9822-526bac522d79","resolution":{"observed_at":"2026-08-07T11:42:51.901908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.887949Z","title":"LSTM can solve hard long time lag problems.In: Advances in neural information processing systems","venue":null,"work_id":"77ae7308-07ec-4729-8c27-ae9ca22ba6c8","year":null},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.608313Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:c84a87e2482370806b2f6bf350a01d6c7de469ce5e825561b9be52e3e6c0e8ae","observation_id":"ad09a8f0-cae1-4836-8d5a-165266d72b3f","resolution":{"observed_at":"2026-08-07T11:42:51.891488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.877859Z","title":"”Speech recognition with deep recurrent neu- ral networks.”2013 IEEE international conference on acoustics, speech and signal processing.IEEE, 2013","venue":null,"work_id":"9017c3ed-312e-42c5-9131-4f3a8e9dfff5","year":2013},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.612314Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:1a3d5b5a6093ebc56b3e0775ac41cb16a46981a363fca452a0cf3ea14dd76793","observation_id":"a5ce9619-0914-4635-a6aa-a3a18a8e6fe8","resolution":{"observed_at":"2026-08-07T11:42:51.881441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.3555","last_updated":"2014-12-11T06:46:53Z","snapshot_observed_at":"2026-08-03T11:40:51.182181Z","submitted_at":"2014-12-11T06:46:53Z","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.3555","snapshot_observed_at":"2026-08-07T11:42:51.616485Z","title":"”Empirical evaluation of gated recurrent neural networks on sequence model- ing.”arXiv preprint arXiv:1412.3555(2014)","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.616485Z"},"links":{"cited_paper":"/paper/1412.3555","citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:ff4a54a2817b4d8cd7f858487792b9c4f0f72f028aef21aa14f43d51f0a45a69","observation_id":"f3a10424-d60f-41af-bc04-131bfc92a539","resolution":{"observed_at":"2026-08-07T11:42:51.616485Z","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":"2026-08-07T11:42:51.865294Z","title":"”A neural probabilistic language model.”Journal of machine learning research3.Feb (2003): 1137-1155","venue":null,"work_id":"51dcea3a-a5d8-413f-848a-5e31df775e3a","year":2003},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.620694Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:249a94ba9192f2b3df67cb4881cf2860e9e01b17f2953bca9f62941648f15bf7","observation_id":"e7eb1495-e802-4087-8123-9cccfd340c2e","resolution":{"observed_at":"2026-08-07T11:42:51.870095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.854269Z","title":"”Recurrent models of visual attention.”Advances in neural information processing systems27 (2014): 2204-2212","venue":null,"work_id":"714a597d-56b8-4f94-939f-8890782fafa5","year":2014},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.624700Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:59c7e01d9a1adb847a65596ee5eafd32f913bda7ecc4064e3457bd9dc7bbe455","observation_id":"31ebe1da-b7fc-4476-841f-98763151399b","resolution":{"observed_at":"2026-08-07T11:42:51.858148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.0473","last_updated":"2016-05-19T21:53:22Z","snapshot_observed_at":"2026-07-06T03:53:10.336430Z","submitted_at":"2014-09-01T16:33:02Z","title":"Neural Machine Translation by Jointly Learning to Align and Translate","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.0473","snapshot_observed_at":"2026-08-07T11:42:51.629423Z","title":"”Neural machine translation by jointly learning to align and translate.”arXiv preprint arXiv:1409.0473(2014)","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.629423Z"},"links":{"cited_paper":"/paper/1409.0473","citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:0d40b91c7dd7bc36a6eb57739f06e421cbda2e0ffbccbcebc7b2e37eb96e7add","observation_id":"00e866cb-6dc6-45da-95a0-3ef00caba207","resolution":{"observed_at":"2026-08-07T11:42:51.629423Z","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":"2026-08-07T11:42:51.843371Z","title":"”Attention is all you need.”Advances in neural information processing sys- tems.2017","venue":null,"work_id":"dcd680d2-9d8d-4ece-816b-7d0ebd7999e2","year":2017},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.633608Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:d8034281dba75c3969f1ef4db0d437b3410a39321360964c4cd1a0f086e54afe","observation_id":"bbcf4ca5-77bb-4b38-97a4-97c6a7643887","resolution":{"observed_at":"2026-08-07T11:42:51.847164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.08434","last_updated":"2021-10-06T12:26:15Z","snapshot_observed_at":"2026-07-06T07:22:29.205966Z","submitted_at":"2018-12-20T09:30:12Z","title":"Graph Neural Networks: A Review of Methods and Applications","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.08434","snapshot_observed_at":"2026-08-07T11:42:51.637901Z","title":"”Graph neural networks: A re- view of methods and applications.”arXiv preprint arXiv:1812.08434 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.637901Z"},"links":{"cited_paper":"/paper/1812.08434","citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:2efa913ea9416473b8ac09c467d90cc90609d5b35a7693af2808dd51751636ff","observation_id":"c3a3b3ac-cdaa-480f-9cfb-f198041f46ee","resolution":{"observed_at":"2026-08-07T11:42:51.637901Z","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":"2026-08-07T11:42:51.832538Z","title":"Graph attention networks,","venue":null,"work_id":"780f8d7f-2cf8-4b1c-9616-2b90592d8d5c","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.641905Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:6e3c4b96a67fa4d1d4cf20a82d73f0913ac2ac8bd5722e208e0a9aaa46cc9815","observation_id":"8a935f9e-18c1-4f51-a7f5-94befbced067","resolution":{"observed_at":"2026-08-07T11:42:51.836136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.821572Z","title":"”Rumor detection on twitter with tree-structured recursive neu- ral networks.”Association for Computational Linguis- tics,2018","venue":null,"work_id":"d04fd691-13ad-4e43-bc1b-22db6da9c348","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.645796Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:bb567260a0631f64d7cf5d8c72c67df39845841f4d4848e0821f02d292b37322","observation_id":"0d8b64d5-c10e-45fc-aead-cd1c0e31373c","resolution":{"observed_at":"2026-08-07T11:42:51.825912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05464","last_updated":"2016-09-26T20:49:16Z","snapshot_observed_at":"2026-07-06T05:00:21.356642Z","submitted_at":"2016-06-17T09:39:47Z","title":"Stance Detection with Bidirectional Conditional Encoding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05464","snapshot_observed_at":"2026-08-07T11:42:51.649780Z","title":"”Stance detection with bidirectional conditional encoding.” 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.649780Z"},"links":{"cited_paper":"/paper/1606.05464","citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:23ce22514f966d0cf7fa0027f954ebdc7a3ac7ac203030404f8b9ee2e5deb9fc","observation_id":"0903c757-7651-4fc6-b9c4-87a1aa09dae5","resolution":{"observed_at":"2026-08-07T11:42:51.649780Z","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":"2026-08-07T11:42:51.809688Z","title":null,"venue":null,"work_id":"496e0fd0-4f84-4db1-be6b-05f48a54d37a","year":2017},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.653479Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:8ea49184aba11dd8f412e6d8e8899698df490f5e53f028b1d50ed2aca9fa5004","observation_id":"f1a8b782-79bb-4ebd-82fa-6bf264ca3bef","resolution":{"observed_at":"2026-08-07T11:42:51.813367Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.797788Z","title":"”Csi: A hybrid deep model for fake news detection.” Proceedings of the 2017 ACM on Conference on Infor- mation and Knowledge Management.2017","venue":null,"work_id":"0212204d-f7ed-4a81-8c9b-1f858c15226c","year":2017},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.658116Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:4821d8b7e1707c2f480f98e15e594fb78fda09bfabaea65ca71dfbdc6304401a","observation_id":"ac9bd9da-ddbb-4b94-94e5-2d6d8d50dfdd","resolution":{"observed_at":"2026-08-07T11:42:51.801756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.787837Z","title":null,"venue":null,"work_id":"a3bd13e9-1c90-409e-acab-94e0af6f4f23","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.662392Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:f14b39859cf43641128058318c20a2463bf01eb92cae10da9d347c2d1d274fbe","observation_id":"2ec604ca-dac8-44c7-b913-a3592c848443","resolution":{"observed_at":"2026-08-07T11:42:51.790999Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.776581Z","title":"Rumor detection with hierarchical so- cial attention network.Proceedings of the 27th ACM International Conference on Information and Knowl- edge Management.2018","venue":null,"work_id":"101ef157-83c7-4865-9bb3-eddc03fcee2b","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.667041Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:facec2c4b1e59f5791a6e12fbb4e124735b11c479bc7eac1dcf88e0c387718cf","observation_id":"eb14597d-cd39-44df-8df9-6271f9f1ca1c","resolution":{"observed_at":"2026-08-07T11:42:51.780449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.766123Z","title":"”Spatial temporal graph convolutional networks for skeleton- based action recognition.”Proceedings of the AAAI conference on artificial intelligence.V ol","venue":null,"work_id":"610a50dd-05b2-4ab6-ab70-bbefd1110fa8","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.671470Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:c548bd1be398a2ccd8e2a0a980359675f3a5be7782d0486860c352dc17d55b00","observation_id":"033c664e-a918-451d-934e-06eb925c5ca5","resolution":{"observed_at":"2026-08-07T11:42:51.769713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T11:42:51.753044Z","title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.In- ternational Conference on Learning Representations","venue":null,"work_id":"03abfe27-fb44-4ce4-8f71-df582b0bf457","year":2018},"citing_paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:42:51.675687Z"},"links":{"citing_paper":"/paper/2506.01627"},"observation_digest":"sha256:9d630dbb6cd059c1bee74a283225165cd98315282b1871fe522cb5cd2e80fc95","observation_id":"5b6b9021-0da4-407a-ac5a-ee23a2496692","resolution":{"observed_at":"2026-08-07T11:42:51.758614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.01627","last_updated":"2025-06-02T13:05:23Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T11:34:50.801977Z","submitted_at":"2025-06-02T13:05:23Z","title":"MVAN: Multi-View Attention Networks for Fake News Detection on Social Media"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":46},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.01627."}