In this article, we explore the concept of clickbait in social media posts and discuss various machine learning approaches to detect and prevent it. Clickbait refers to sensational or misleading headlines that entice users to click on a post, often leading to disappointment or disgust. With the growing popularity of online applications, clickbait has become a significant issue, degrading user experience and even causing mistrust towards news media.
To address this challenge, researchers have proposed various machine learning models, including BiLSTM, GATED CNN, FAST TEXT, and BERT. These models use different techniques to learn the extent to which a word contributes to the clickbait score of a social media post. Some methods consider the sequence of words, while others focus on distributed representations of words based on ordered words or sentence-level context.
The article presents statistics for three news datasets and shows examples of training few-shot models using 5-shot methods. The results demonstrate the effectiveness of these approaches in detecting clickbait, with a high accuracy rate in classifying clickbait versus non-clickbait posts. Overall, the study highlights the potential of machine learning in improving user experience on social media platforms by detecting and preventing clickbait.
Key Takeaways
- Clickbait refers to sensational or misleading headlines that entice users to click on a post, leading to disappointment or disgust.
- Machine learning approaches can be used to detect and prevent clickbait in social media posts.
- Various models have been proposed for automatic clickbait detection, including BiLSTM, GATED CNN, FAST TEXT, and BERT.
- These models use different techniques to learn the extent to which a word contributes to the clickbait score of a social media post.
- Few-shot methods have been shown to be effective in detecting clickbait with high accuracy rates.
