@inproceedings{mihalcea-nisioi-2023-clark,
    title = "{C}lark {K}ent at {S}em{E}val-2023 Task 5: {SVM}s, Transformers, and Pixels for Clickbait Spoiling",
    author = "Mihalcea, Dragos-stefan  and
      Nisioi, Sergiu",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.167",
    doi = "10.18653/v1/2023.semeval-1.167",
    pages = "1204--1212",
    abstract = "In this paper we present an analysis of our approaches for the 2023 SemEval-2023 Clickbait Challenge. We only participated in the sub-task aiming at identifying different clikcbait spoiling types comparing several machine learning and deep learning approaches. Our analysis confirms previous results on this task and show that automatic methods are able to reach approximately 70{\textbackslash}{\%} accuracy at predicting what type of additional content is needed to mitigate sensationalistic posts on social media. Furthermore, we provide a qualitative analysis of the results, showing that the models may do better in practice than the metric indicates since the evaluate does not depend only on the predictor, but also on the typology we choose to define clickbait spoiling.",
}

In this paper we present an analysis of our approaches for the 2023 SemEval-2023 Clickbait Challenge. We only participated in the sub-task aiming at identifying different clikcbait spoiling types comparing several machine learning and deep learning approaches. Our analysis confirms previous results on this task and show that automatic methods are able to reach approximately 70{\textbackslash}{\%} accuracy at predicting what type of additional content is needed to mitigate sensationalistic posts on social media. Furthermore, we provide a qualitative analysis of the results, showing that the models may do better in practice than the metric indicates since the evaluate does not depend only on the predictor, but also on the typology we choose to define clickbait spoiling.
Mihalcea, Dragos-stefan Nisioi, Sergiu
Clark Kent at SemEval-2023 Task 5: SVMs, Transformers, and Pixels for Clickbait Spoiling