To Click it or not to Click it: An Italian Dataset for Neutralising Clickbait Headlines
Authors: Daniel Russo, Oscar Araque, Marco Guerini (Università di Trento, Fondazione Bruno Kessler, University of Madrid, 2024)
Topic: Fact Checking and Fake News Detection

Abstract
Clickbait is a common technique aimed at attracting a reader's attention, although it can result in inaccuracies and lead to misinformation. This work explores the role of current Natural Language Processing methods to reduce its negative impact. To do so, a novel Italian dataset is generated, containing manual annotations for classification, spoiling, and neutralisation of clickbait. Besides, several experimental evaluations are performed, assessing the performance of current language models. On the one hand, we evaluate the performance in the task of clickbait detection in a multilingual setting, showing that augmenting the data with English instances largely improves overall performance. On the other hand, the generation tasks of clickbait spoiling and neutralisation are explored. The latter is a novel task, designed to increase the informativeness of a headline, thus removing the information gap. This work opens a new research avenue that has been largely uncharted in the Italian language.

Introduction
Accuracy and truthfulness are essential characteristics of journalism. Nevertheless, in an effort to improve revenue, a large number of newspapers and magazines publish clickbait articles, a viral journalism strategy that seeks to attract users to click on a link to a page through tactics such as sensationalist stories and catchy headlines that act as bait. The use of these tactics harms the quality of news pieces and thus hinders the ability of citizens to obtain reliable and objective information. The literature distinguishes between two main types of clickbait. (i) Classical clickbait AUTHOR embeds within the headlines information gaps, also known as curiosity gaps AUTHOR, in order to arouse curiosity in the reader that is forced to access the article's content which is ultimately disappointing. Classical clickbait usually makes use of hyperbolic language, caps lock, demonstrative pronouns and superlative to grasp the user's attention AUTHOR. (ii) Deceptive clickbait AUTHOR refers to headlines that resemble traditional media headlines by offering a summary of the article, still leading to content that differs from the reader's expectations. These headlines promise high news value but deliver content with low news value, resulting in reader disappointment. Although clickbait headlines are considered one of the less harmful forms of fake news, as their main goal is to increase profit by driving traffic to their website AUTHOR, they can sometimes pose a danger, especially when they deal with potentially harmful topics such as health and science. To address this problem, Natural Language Processing techniques have been widely employed to detect clickbait headlines, with a particular focus on the English language AUTHOR. \citet{hagen-etal-2022-clickbait} proposed the clickbait spoiling task, i.e., the generation of a short text that satisfies the curiosity induced by a clickbait post. In light of this, this work addresses the issue of clickbait in the Italian language, studying its characteristics and the possibilities of current technology to reduce its negative impact. In doing so, we have generated a novel Italian dataset that gathers a large collection of clickbait articles, which is made public for the community to use~}. We named the dataset ClickBaIT. This dataset contains manually annotated instances as clickbait/non-clickbait, as well as manually generated spoilers and neutralised headlines. We have also performed a thorough multilingual evaluation, exploiting the availability of English data to complement our dataset in the task of clickbait detection. Finally, this work also explores the use of our annotated dataset and large language models to automatically generate both spoilers and, as a novel task, a neutralised version of clickbait headlines. A graphical illustration of the experimental design is presented in Figure fig:exp_design. [t!] \centering \includegraphics[width=\textwidth]{exp_clic_example.pdf , spoiler generation, and clickbait neutralisation. The robot icon represents the language model used for either classification or generation. We utilized DistilBERT and Llama3-8B for task 1, and LLaMAntino-3-8B for tasks 2 and 3. The models were tested for generative tasks using zero-shot, few-shot, and fine-tuning configurations, except for question rewriting, for which we employed a few-shot approach.

Conclusion
This work presents ClickBaIT, a novel Italian dataset for clickbait modelling, as well as a diverse set of experiments to assess the effectiveness of current models for clickbait detection, spoiling and neutralisation. The dataset includes news articles that have been manually annotated to indicate the presence of clickbait, spoilers associated with clickbait headlines, and their respective neutral headlines. The experiments explore the effectiveness of current NLP methods for the modelling of clickbait headlines in Italian through ClickBaIT. The evaluation for clickbait detection shows how training data can be augmented in a multilingual setting, which leads to classification improvements that are in line with previous research AUTHOR. The generation experiments, for both spoiling and neutralisation, evidence that the evaluated model does benefit from in-domain knowledge extracted from the proposed dataset. As seen, these informed generations are more accurate and align better with the golden text. Considering the effect of clickbait, we argue that while there are initially harmless articles, lack of accuracy can have a detrimental effect on readers. This is clear when considering certain sensitive domains such as health. Thus, we hope that this work facilitates future research on the topic for example, by addressing the link between clickbait and misinformation, considering both in a unified framework.