{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:HXI4HSIHIULUSYHS2GFTL5PKTV","short_pith_number":"pith:HXI4HSIH","canonical_record":{"source":{"id":"2207.03317","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-07-07T14:21:52Z","cross_cats_sorted":[],"title_canon_sha256":"3d65afb42931486ec7e29d64b5590afc8f2cc1901489a3368061d0ded98ad91a","abstract_canon_sha256":"f550625b664119b21bbb4a66ebd4f18d842dd10a3738554ceb365d47676bb192"},"schema_version":"1.0"},"canonical_sha256":"3dd1c3c90745174960f2d18b35f5ea9d72836657c228a7571a37071af232a770","source":{"kind":"arxiv","id":"2207.03317","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.03317","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"arxiv_version","alias_value":"2207.03317v1","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.03317","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"pith_short_12","alias_value":"HXI4HSIHIULU","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"pith_short_16","alias_value":"HXI4HSIHIULUSYHS","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"pith_short_8","alias_value":"HXI4HSIH","created_at":"2026-07-05T04:38:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:HXI4HSIHIULUSYHS2GFTL5PKTV","target":"record","payload":{"canonical_record":{"source":{"id":"2207.03317","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-07-07T14:21:52Z","cross_cats_sorted":[],"title_canon_sha256":"3d65afb42931486ec7e29d64b5590afc8f2cc1901489a3368061d0ded98ad91a","abstract_canon_sha256":"f550625b664119b21bbb4a66ebd4f18d842dd10a3738554ceb365d47676bb192"},"schema_version":"1.0"},"canonical_sha256":"3dd1c3c90745174960f2d18b35f5ea9d72836657c228a7571a37071af232a770","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:38:20.974415Z","signature_b64":"j2onfKOmeuNO2iGtVx9awet5LBH0noyrhnb4L78gkdToHNsXh21ArJdJqiCVH4VqgJvivXx5t6DtCxg+kAGECg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dd1c3c90745174960f2d18b35f5ea9d72836657c228a7571a37071af232a770","last_reissued_at":"2026-07-05T04:38:20.974065Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:38:20.974065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.03317","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:38:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aUwk1RFGSr/0hh2ENl/K5q0gFVuHZlu89M131tFwD/E3NiSZ7UWEK/VyG+bLxZqBYxePevDl6phfPScd71IEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T20:35:31.071015Z"},"content_sha256":"ef3b1df69091b9e63b4617983d39377103db2d6ffa3d92f74c62af83d94160b4","schema_version":"1.0","event_id":"sha256:ef3b1df69091b9e63b4617983d39377103db2d6ffa3d92f74c62af83d94160b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:HXI4HSIHIULUSYHS2GFTL5PKTV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multimodal Feature Extraction for Memes Sentiment Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Sofiane Ouaari, Tomas Horvath, Tsegaye Misikir Tashu","submitted_at":"2022-07-07T14:21:52Z","abstract_excerpt":"In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.03317","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.03317/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:38:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vFAvEizpvo01Bt0qE8PZ0TWtNLb+A/psojvjZkeh5JJ1LVvSCY7SyJkp/pDs/XFoJ+EHPmlXYeWAri4Aj1G3Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T20:35:31.071525Z"},"content_sha256":"ff6011340b311b18744f4ffda7fa98bb27fbf3b1cb758defed3917591eee99ca","schema_version":"1.0","event_id":"sha256:ff6011340b311b18744f4ffda7fa98bb27fbf3b1cb758defed3917591eee99ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HXI4HSIHIULUSYHS2GFTL5PKTV/bundle.json","state_url":"https://pith.science/pith/HXI4HSIHIULUSYHS2GFTL5PKTV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HXI4HSIHIULUSYHS2GFTL5PKTV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T20:35:31Z","links":{"resolver":"https://pith.science/pith/HXI4HSIHIULUSYHS2GFTL5PKTV","bundle":"https://pith.science/pith/HXI4HSIHIULUSYHS2GFTL5PKTV/bundle.json","state":"https://pith.science/pith/HXI4HSIHIULUSYHS2GFTL5PKTV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HXI4HSIHIULUSYHS2GFTL5PKTV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:HXI4HSIHIULUSYHS2GFTL5PKTV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f550625b664119b21bbb4a66ebd4f18d842dd10a3738554ceb365d47676bb192","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-07-07T14:21:52Z","title_canon_sha256":"3d65afb42931486ec7e29d64b5590afc8f2cc1901489a3368061d0ded98ad91a"},"schema_version":"1.0","source":{"id":"2207.03317","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.03317","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"arxiv_version","alias_value":"2207.03317v1","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.03317","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"pith_short_12","alias_value":"HXI4HSIHIULU","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"pith_short_16","alias_value":"HXI4HSIHIULUSYHS","created_at":"2026-07-05T04:38:20Z"},{"alias_kind":"pith_short_8","alias_value":"HXI4HSIH","created_at":"2026-07-05T04:38:20Z"}],"graph_snapshots":[{"event_id":"sha256:ff6011340b311b18744f4ffda7fa98bb27fbf3b1cb758defed3917591eee99ca","target":"graph","created_at":"2026-07-05T04:38:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2207.03317/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all mod","authors_text":"Sofiane Ouaari, Tomas Horvath, Tsegaye Misikir Tashu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-07-07T14:21:52Z","title":"Multimodal Feature Extraction for Memes Sentiment Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.03317","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ef3b1df69091b9e63b4617983d39377103db2d6ffa3d92f74c62af83d94160b4","target":"record","created_at":"2026-07-05T04:38:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f550625b664119b21bbb4a66ebd4f18d842dd10a3738554ceb365d47676bb192","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-07-07T14:21:52Z","title_canon_sha256":"3d65afb42931486ec7e29d64b5590afc8f2cc1901489a3368061d0ded98ad91a"},"schema_version":"1.0","source":{"id":"2207.03317","kind":"arxiv","version":1}},"canonical_sha256":"3dd1c3c90745174960f2d18b35f5ea9d72836657c228a7571a37071af232a770","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3dd1c3c90745174960f2d18b35f5ea9d72836657c228a7571a37071af232a770","first_computed_at":"2026-07-05T04:38:20.974065Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:38:20.974065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j2onfKOmeuNO2iGtVx9awet5LBH0noyrhnb4L78gkdToHNsXh21ArJdJqiCVH4VqgJvivXx5t6DtCxg+kAGECg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:38:20.974415Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.03317","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef3b1df69091b9e63b4617983d39377103db2d6ffa3d92f74c62af83d94160b4","sha256:ff6011340b311b18744f4ffda7fa98bb27fbf3b1cb758defed3917591eee99ca"],"state_sha256":"fe75101a5ddbe788485eba973482637395f74273934542823216adcc5dae0de9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oMojA3haTC6XIKhZcpkdxvMyFBZsqQ3BaktWTH43ZJp4sR7vWmv3MM2iZLxerxEKp6WnnTPfPNSFRSiOpat5Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T20:35:31.075933Z","bundle_sha256":"917cf4621572d333364a2eeb2c13ecc4bbd3630ec361382cde43637cd25832ba"}}