2018/12/27 by Martin Potthast, Tim Gollub, Potthast, Martin +5
Computer Science · Health Professions · Social Sciences · #Advanced Malware Detection Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Health Literacy and Information Accessibility #Information Retrieval (cs.IR) #Misinformation and Its Impacts #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.1812.10847
openalex publication_date 2018/12/27 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
Clickbait has grown to become a nuisance to social media users and social\nmedia operators alike. Malicious content publishers misuse social media to\nmanipulate as many users as possible to visit their websites using clickbait\nmessages. Machine learning technology may help to handle this problem, giving\nrise to automatic clickbait detection. To accelerate progress in this\ndirection, we organized the Clickbait Challenge 2017, a shared task inviting\nthe submission of clickbait detectors for a comparative evaluation. A total of\n13 detectors have been submitted, achieving significant improvements over the\nprevious state of the art in terms of detection performance. Also, many of the\nsubmitted approaches have been published open source, rendering them\nreproducible, and a good starting point for newcomers. While the 2017 challenge\nhas passed, we maintain the evaluation system and answer to new registrations\nin support of the ongoing research on better clickbait detectors.\n