Clickbait Classification and Spoiling Using Natural Language Processing
Adhitya Thirumala Elisa Ferracane
Clickbait is the practice of engineering titles to incentivize readers to
click through to articles. Such titles with sensationalized language reveal as
little information as possible. Occasionally, clickbait will be intentionally
misleading, so natural language processing (NLP) can scan the article and
answer the question posed by the clickbait title, or spoil it. We tackle two
tasks: classifying the clickbait into one of 3 types (Task 1), and spoiling the
clickbait (Task 2). For Task 1, we propose two binary classifiers to determine
the final spoiler type. For Task 2, we experiment with two approaches: using a
question-answering model to identify the span of text of the spoiler, and using
a large language model (LLM) to generate the spoiler. Because the spoiler is
contained in the article, we frame the second task as a question-answering
approach for identifying the starting and ending positions of the spoiler. We
created models for Task 1 that were better than the baselines proposed by the
dataset authors and engineered prompts for Task 2 that did not perform as well
as the baselines proposed by the dataset authors due to the evaluation metric
performing worse when the output text is from a generative model as opposed to
an extractive model.