  Clickbait (headlines) make use of misleading titles that hide critical
information from or exaggerate the content on the landing target pages to
entice clicks. As clickbaits often use eye-catching wording to attract viewers,
target contents are often of low quality. Clickbaits are especially widespread
on social media such as Twitter, adversely impacting user experience by causing
immense dissatisfaction. Hence, it has become increasingly important to put
forward a widely applicable approach to identify and detect clickbaits. In this
paper, we make use of a dataset from the clickbait challenge 2017
(clickbait-challenge.com) comprising of over 21,000 headlines/titles, each of
which is annotated by at least five judgments from crowdsourcing on how
clickbait it is. We attempt to build an effective computational clickbait
detection model on this dataset. We first considered a total of 331 features,
filtered out many features to avoid overfitting and improve the running time of
learning, and eventually selected the 60 most important features for our final
model. Using these features, Random Forest Regression achieved the following
results: MSE=0.035 MSE, Accuracy=0.82, and F1-sore=0.61 on the clickbait class.
