Diving Deep into Clickbaits Who Use Them to What Extents in Which Topics with What Effects?

• Published in: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 pp. 232 - 239
• Main Authors: Rony, Md Main Uddin, Hassan, Naeemul, Yousuf, Mohammad
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 31.07.2017
• Series: ACM Conferences
• Subjects: Information systems; Information systems > Information retrieval; Information systems > Information retrieval > Document representation; Information systems > Information retrieval > Retrieval models and ranking; Information systems > Information retrieval > Retrieval tasks and goals; Information systems > Information retrieval > Retrieval tasks and goals > Document filtering; Information systems > Information retrieval > Retrieval tasks and goals > Information extraction; Information systems > Information systems applications; Information systems > Information systems applications > Data mining
• ISBN: 1450349935; 9781450349932
• DOI: 10.1145/3110025.3110054

The use of alluring headlines (clickbait) to tempt the readers has become a growing practice nowadays. For the sake of existence in the highly competitive media industry, most of the on-line media including the mainstream ones, have started following this practice. Although the wide-spread practice of clickbait makes the reader's reliability on media vulnerable, a large scale analysis to reveal this fact is still absent. In this paper, we analyze 1.67 million Facebook posts created by 153 media organizations to understand the extent of clickbait practice, its impact and user engagement by using our own developed clickbait detection model. The model uses distributed sub-word embeddings learned from a large corpus. The accuracy of the model is 98.3%. Powered with this model, we further study the distribution of topics in clickbait and non-clickbait contents.