{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/clickbait-detection-in-tweets-using-self","title":"Clickbait Detection in Tweets Using Self-attentive Network","arxiv_id":"1710.05364","date":"2017-10-15","proceeding":null,"authors":["Yiwei Zhou"],"abstract":"Clickbait detection in tweets remains an elusive challenge. In this paper, we\ndescribe the solution for the Zingel Clickbait Detector at the Clickbait\nChallenge 2017, which is capable of evaluating each tweet's level of click\nbaiting. We first reformat the regression problem as a multi-classification\nproblem, based on the annotation scheme. To perform multi-classification, we\napply a token-level, self-attentive mechanism on the hidden states of\nbi-directional Gated Recurrent Units (biGRU), which enables the model to\ngenerate tweets' task-specific vector representations by attending to important\ntokens. The self-attentive neural network can be trained end-to-end, without\ninvolving any manual feature engineering. Our detector ranked first in the\nfinal evaluation of Clickbait Challenge 2017.","url_abs":"http://arxiv.org/abs/1710.05364v1","url_pdf":"http://arxiv.org/pdf/1710.05364v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"clickbait-detection-in-tweets-using-self","repo_url":"https://github.com/zhouyiwei/cc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clickbait-detection","task_name":"Clickbait Detection"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.05364","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}