{"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/a-two-level-classification-approach-for","title":"A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features","arxiv_id":"1710.08528","date":"2017-10-23","proceeding":null,"authors":["Olga Papadopoulou","Markos Zampoglou","Symeon Papadopoulos","Ioannis Kompatsiaris"],"abstract":"The emergence of social media as news sources has led to the rise of\nclickbait posts attempting to attract users to click on article links without\ninforming them on the actual article content. This paper presents our efforts\nto create a clickbait detector inspired by fake news detection algorithms, and\nour submission to the Clickbait Challenge 2017. The detector is based almost\nexclusively on text-based features taken from previous work on clickbait\ndetection, our own work on fake post detection, and features we designed\nspecifically for the challenge. We use a two-level classification approach,\ncombining the outputs of 65 first-level classifiers in a second-level feature\nvector. We present our exploratory results with individual features and their\ncombinations, taken from the post text and the target article title, as well as\nfeature selection. While our own blind tests with the dataset led to an F-score\nof 0.63, our final evaluation in the Challenge only achieved an F-score of\n0.43. We explore the possible causes of this, and lay out potential future\nsteps to achieve more successful results.","url_abs":"http://arxiv.org/abs/1710.08528v1","url_pdf":"http://arxiv.org/pdf/1710.08528v1.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":"a-two-level-classification-approach-for","repo_url":"https://github.com/clickbait-challenge/snapper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clickbait-detection","task_name":"Clickbait Detection"},{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}