{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:N42NH53XBO6SIL5ZPDCIMJ22EV","short_pith_number":"pith:N42NH53X","schema_version":"1.0","canonical_sha256":"6f34d3f7770bbd242fb978c486275a2547c8cfe6c296987be5d35cef310cbbc9","source":{"kind":"arxiv","id":"1710.06699","version":1},"attestation_state":"computed","paper":{"title":"Detecting Clickbait in Online Social Media: You Won't Believe How We Did It","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Aviad Elyashar, Jorge Bendahan, Rami Puzis","submitted_at":"2017-10-18T12:33:30Z","abstract_excerpt":"In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user's attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best perfo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1710.06699","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2017-10-18T12:33:30Z","cross_cats_sorted":[],"title_canon_sha256":"400f22e397afa3c0f9c944bbb0306cf7c1784549cb1670e5c64c638f3661525a","abstract_canon_sha256":"f97dddee8007449fb2f5720b333f2a98972120fdd0156932fac989692dee2d1b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:32.936772Z","signature_b64":"IZYViCJrlaOwSBmNn0+hhAom1A3oEAW/+F101lIZqpCxaCV7DiuitBvV81iLb74ubJ4U28nbuoydPTjFkZegDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f34d3f7770bbd242fb978c486275a2547c8cfe6c296987be5d35cef310cbbc9","last_reissued_at":"2026-05-18T00:32:32.936083Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:32.936083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Clickbait in Online Social Media: You Won't Believe How We Did It","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Aviad Elyashar, Jorge Bendahan, Rami Puzis","submitted_at":"2017-10-18T12:33:30Z","abstract_excerpt":"In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user's attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best perfo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06699","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":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1710.06699","created_at":"2026-05-18T00:32:32.936169+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.06699v1","created_at":"2026-05-18T00:32:32.936169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06699","created_at":"2026-05-18T00:32:32.936169+00:00"},{"alias_kind":"pith_short_12","alias_value":"N42NH53XBO6S","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"N42NH53XBO6SIL5Z","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"N42NH53X","created_at":"2026-05-18T12:31:31.346846+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV","json":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV.json","graph_json":"https://pith.science/api/pith-number/N42NH53XBO6SIL5ZPDCIMJ22EV/graph.json","events_json":"https://pith.science/api/pith-number/N42NH53XBO6SIL5ZPDCIMJ22EV/events.json","paper":"https://pith.science/paper/N42NH53X"},"agent_actions":{"view_html":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV","download_json":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV.json","view_paper":"https://pith.science/paper/N42NH53X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.06699&json=true","fetch_graph":"https://pith.science/api/pith-number/N42NH53XBO6SIL5ZPDCIMJ22EV/graph.json","fetch_events":"https://pith.science/api/pith-number/N42NH53XBO6SIL5ZPDCIMJ22EV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV/action/storage_attestation","attest_author":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV/action/author_attestation","sign_citation":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV/action/citation_signature","submit_replication":"https://pith.science/pith/N42NH53XBO6SIL5ZPDCIMJ22EV/action/replication_record"}},"created_at":"2026-05-18T00:32:32.936169+00:00","updated_at":"2026-05-18T00:32:32.936169+00:00"}