Clickbait is a catchy way of attracting attention online. It often uses sensationalist titles that can be misleading.
Journalism and clickbait
Have you ever clicked on an article that promised something amazing but turned out to be a disappointment? Clickbait is a strategy of attracting users with exaggerated headlines and thumbnails. Specifically, the aim is to increase website traffic and advertising revenue. In the context of journalism, clickbait is a technique used to attract readers with sensational and often misleading headlines. Examples include headlines such as: ‘You won’t believe what happened!’ or ‘Discover a secret that will change your life!’ Despite its annoyance, clickbait effectively taps into people’s curiosity and emotions.
Contemporary media studies
Clickbait, although originally seen as a manipulative technique, has become an important object of research in digital media. Therefore, below, I provide an overview of key research papers on the phenomenon.
Multimodal clickbait detection models
The use of state-of-the-art methods, such as multi-modal cue tuning (MSP), not only allows both textual and visual content to be included in clickbait analysis, but also enhances the overall accuracy. Moreover, the MSP model, based on Graph Attention Network and Contrastive Language-Image Pre-training, has achieved state-of-the-art results in clickbait studies on three Chinese datasets (Wang et al., 2024).
The impact of emotion on the effectiveness of clickbait in video marketing
An analysis of 16,215 video thumbnails on YouTube revealed that expressive visual emotions – both positive and negative – contribute to increased views. However, in contrast, excessive emotionality in descriptions often reduces the appeal of the content. Thus, the results highlight the importance of balance in the use of clickbait in marketing strategies (Cui et al., 2024).
Clickbaits in the context of fake news
Moreover, research has shown that clickbait is a common element of fake news, contributing to misinformation on social media. Therefore, the authors propose multilingual detection models that can counteract the global spread of clickbait and fake content (Sharma & Singh, 2024).
Age differences in the reception of clickbait content
In experiments involving younger and older people, it has been observed that older people are more receptive to clickbait with high social value, while younger audiences show greater indifference to less popular content. Consequently, the results point to the need to adapt educational strategies to different age groups (Swirsky & Spaniol, 2024).
Rapid learning in detecting attention-grabbing content
Furthermore, soft learning with internal knowledge expansion (SPCD_IE) eliminates dependence on external knowledge bases. With this method, the detection of manipulative headlines and material becomes more effective, especially in scenarios with limited data, as demonstrated in experiments using three public datasets (Dong & Wu, 2024).
Conclusions
The research shows that manipulative content is a tool not only to mislead, but also to effectively attract attention in marketing strategies. At the same time, their relationship with fake news points to the need for further development of analytical tools.
Bibliography
- Cui, G., Chung, S. Y. H., Peng, L., & Wang, Q. (2024). Clicks for money: Predicting video views through a sentiment analysis of titles and thumbnails. Journal of Business Research, 183, 114849. https://doi.org/10.1016/j.jbusres.2024.114849
- Dong, B., & Wu, X. (2024). Soft Prompt Learning with Internal Knowledge Expansion for Clickbait Detection. Pattern Recognition and Artificial Intelligence, 37 (9), 798-810. https://doi.org/10.16451/j.cnki.issn1003-6059.202409004
- Sharma, U., & Singh, J. (2024). A comprehensive overview of fake news detection on social networks. Social Network Analysis and Mining, 14(1), 120. https://doi.org/10.1007/s13278-024-01280-3
- Swirsky, L. T., & Spaniol, J. (2024). Moderators of curiosity and information seeking in younger and older adults. Psychology and Aging, 39(7), 701-714. https://doi.org/10.1037/pag0000847
- Wang, Y., Zhu, Y., Li, Y., Wei, L., Yuan, Y., & Qiang, J. (2024). Multi-modal soft prompt-tuning for Chinese Clickbait Detection. Neurocomputing, 128829. https://doi.org/10.1016/j.neucom.2024.128829