We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Our contributions are approaches to classify the type of spoiler needed (i.e., a phrase or a passage), and to generate appropriate spoilers. A large-scale evaluation and error analysis on a new corpus of 5,000 manually spoiled clickbait posts the Webis Clickbait Spoiling Corpus 2022 shows that our spoiler type classifier achieves an accuracy of 80%, while the question answering model DeBERTa-large outperforms all others in generating spoilers for both types. Clickbait is the term used to describe posts in social media that are intended to inappropriately entice their readers to visit a web page. This is achieved through formulations such as sensationalism or cat-aphors that are believed to create a so-called curiosity gap: a form of cognitively induced deprivation that arises from the perception of a gap in knowledge or understanding (Loewenstein, 1994). Clickbait is perceived as inappropriate since its resolution is usually ordinary or trivial, comprising little more than a phrase, short passage, or a list of things that could just as easily have been included in the post. This observation motivates us to introduce the task of clickbait spoiling: identifying or generating a spoiler for a clickbait post. Figure 1 shows four examples of clickbait on Twitter, along with spoilers. The first two tweets explicitly or implicitly promise a surprising resolution to spark curiosity, but their spoilers are brief and trivial. The linked page of the first tweet adds almost nothing, and the spoiler of the second is common sense. The third spoiler is a passage from the linked page, and the fourth is a list of things. Even though there are length limits to the informativeness of tweets, the spoilers in all examples could easily have been part of the original tweets. This paper reports about our investigation into clickbait spoiling and the following contributions: (1) The Webis Clickbait Spoiling Corpus 2022 (Webis-Clickbait-22), consisting of 5,000 clickbait posts, their linked pages and a spoiling piece of text therein. 1 (2) A two-step approach to clickbait spoiling that first classifies a clickbait post according to its spoiler type (phrase or passage), and then treats spoiling either as a question answering or as a passage retrieval task. (3) A systematic evaluation of state-of-the-art methods for spoiler type classification, question answering, and passage retrieval. 2 Although the first step of spoiler type classification is not necessary, our results suggest that it can be helpful. Even more so, as we have not yet tackled multipart spoilers (bottom example in Figure 1; 876 cases also part of our corpus) that probably require a different spoiling approach. 1 Data: https://webis.de/data.html?q=clickbait 2 Code: https://github.com/webis-de/ACL-22 7025 2 Related Work Following an overview of research on clickbait and its operationalization so far, models of question answering and passage retrieval are examined. The underlying assumption of most research on clickbait is that it is a form of data-driven optimization of social media posts to exploit the curiosity gap described by Loewenstein (1994). At least that's what Peter Koechley (2012), the CEO of Upworthy, claimed. Upworthy became one of the first major spreaders of clickbait on Facebook, and their success has prompted Facebook to change its news recommendation algorithms to curb the amount of clickbait, twice (El-Arini and Tang, 2014; Peysakhovich and Hendrix, 2016). Exploratory and theoretical studies of clickbait and its impact on journalism analyzed its prevalence for more than 150 publishers (Rony et al., 2017); its economics for the news market (Munger, 2020); its impact on perceptions of credibility and quality (overall negative) (Molyneux and Coddington, 2020); and noted a slow decline over the past decade (Lischka and Garz, 2021). Journalistic studies of this kind rely on clickbait detection technologies. Originally proposed by Rubin et al. (2015) but not followed up, Potthast et al. (2016) and Chakraborty et al. (2016) independently developed the first detectors. Starting from a shared task organized by Potthast et al. (2018) shortly after, more than 50 approaches have been contributed to date. An overview is beyond the scope of our work, but transformer models dominate this task as well. For the clickbait generation task, preceded by a rule-based generator (Eidnes, 2015), only Shu et al. (2018) and Xu et al. (2019) have presented more advanced models, while Karn et al. (2019) generate teaser headlines that are explicitly not meant to be clickbait. So far, no attempt has been made to generate spoilers for clickbait. If one considers clickbait spoiling as a question answering problem, there are numerous possible solutions. Among the available question-answering benchmarks (Dzendzik et al., 2021), we select two to choose appropriate state-of-the-art models for our evaluation: (1) SQuAD (Rajpurkar et al., 2016) compiles 107,785 questions and answers based on 536 Wikipedia articles. Although a wide range of questions and answers are included, the vast majority of 93.6% are factual (32% names, 31.8% noun phrases, 19.8% numbers, 5.5% verb phrases, and 3.9% adjective phrases), while the remainder are descriptive (3.7% clauses and 2.7% other). We use SQuAD v1.1, not the v2.0 superset (Rajpurkar et al., 2018), which contains unanswerable questions, since we do not expect clickbait to be un-spoilable. (2) TriviaQA (Joshi et al., 2017) contains 95,000 questionanswer pairs, mostly dealing with trivia questions that are supposed to be particularly difficult to answer. These are comparable to clickbait in that many of them address rather trivial things (see Figure 1). The question answering models used in our experiments are ALBERT (Lan et al., 2020), AllenAI-Document-QA (Clark and Gardner, 2018), BERT (cased/uncased) (Devlin et al., 2019), Big Bird (Za-heer et al., 2020), DeBERTa (large) (He et al., 2021), ELECTRA (Clark et al., 2020), FunnelTransformer (Dai et al., 2020), MPNet (Song et al., 2020), and RoBERTa (base/large) (Liu et al., 2019). Many of them are or were state of the art on the above benchmarks and implement various different architectural paradigms. Passage retrieval relaxes the question answering task a bit in the sense of allowing longer passages of text as answers (e.g., one or more sentences), rather than exact phrases or statements. Neural retrieval models, as surveyed by Guo et al. (2020) and Lin et al. (2021), have been successfully applied to passage retrieval. One of the most important passage retrieval benchmarks is part of MS MARCO, a series of challenges whose first edition was a large question answering task (Nguyen et al., 2016). A passage retrieval dataset of 8.8 million passages was derived for the underlying set of 100,000 questions originally submitted to Bing. This dataset formed the basis for two consecutive shared tasks at the TREC 2019 and 2020 Deep Learning tracks (Craswell et al., 2019, 2020). The passage retrieval models used in our experiments are MonoBERT (Nogueira and Cho, 2019; Nogueira et al., 2019) and MonoT5 (Nogueira et al., 2020) (both topped the MS MARCO passage retrieval leaderboard once), and the classic baseline models BM25 (Robertson and Zaragoza, 2009) and Query Likelihood (Ponte and Croft, 1998), implemented in Anserini (Yang et al., 2017). To tackle clickbait spoiling for the first time, we created the Webis Clickbait Spoiling Corpus 2022 (Webis-Clickbait-22), a collection of 5,000 clickbait posts and their associated spoilers. Our corpus is primarily based on five social media accounts on Twitter, Reddit, and Facebook that manually spoil clickbait: r/savedyouaclick, @HuffPoSpoilers, @SavedYouAClick, @Upwor-thySpoiler, and @StopClickBaitOfficial. With the goal of collecting 5,000 spoilable clickbait posts at an expected rejection rate of around 10% of unusable posts, 5,555 were initially collected from the accounts. Each of them was manually reviewed, and those that turned out not to be spoiled clickbait were removed (e.g., funny posts not intended to be spoilers, or posts with unavailable linked doc-uments). The rejection rate was higher than expected, and only 4,204 posts remained. To reach our goal of 5,000 posts, we then sampled from the Webis-Clickbait-17 corpus used in the Clickbait Challenge 2017 (Potthast et al., 2018). The corpus contains 38,517 tweets, each of which was rated by 5 annotators on a 4-point Likert scale for clickbaitiness: no clickbait, slight clickbait, considerable clickbait, and heavy clickbait. Of the tweets, 1,845 scored an average of 0.8 or higher and can safely be considered clickbait. We selected tweets from this subset and manually spoiled them based on the linked document until our target size of 5,000 posts was reached. Thus, our final corpus consists of 4,204 posts from Twitter, Reddit, and Facebook that were spoiled by a third party specializing in this task, and 796 tweets from the Webis-Clickbait-17 corpus with an average clickbaitiness of at least 0.8 that we spoiled ourselves. For each of the 5,000 clickbait posts, we also reviewed and corrected erroneous spoilers and labeled their exact positions in the linked documents. Our internal guidelines dictated that a spoiler should be as short as possible (i.e., if one word is enough, not a whole sentence should be chosen). Since the underlying annotation task is simple, one main annotator was sufficient. Nevertheless, randomly selected as well as ambiguous cases were discussed with two additional experts among the co-authors. No systematic errors or unforeseen difficulties in solving the annotation task were identified during these discussions. During our annotation, we found that none of the common approaches to main content extraction worked reliably for all the documents linked in the clickbait posts. Yet, clean content is a prerequisite for research on clickbait spoiling to eliminate as many confounding variables as possible. To ensure a clean corpus, one annotator manually extracted the main content of the linked documents, removing (inline) advertisements, links to related articles (e.g., READ ALSO: [. . . ] or Also from CNBC [. . . ]), credits (e.g., Image credit: [. . . ] or Photo by [. . . ]), and social media links (e.g., Subscribe to [. . . ] or Follow us on [. . . ]). A random selection was reviewed to ensure high quality. Moreover, during spoiler annotation, it turned out that there are basically three types of spoilers: (1) phrase spoilers consisting of a single word or phrase from the linked document (e.g., the first two spoilers in Figure 1, but often named entity spoilers as well), (2) passage spoilers consisting of one or a few sentences of the linked document (e.g., the third spoiler in Figure 1), and (3) multipart spoilers consisting of more than one non-consecutive phrases or passages of the linked document (e.g., the fourth spoiler in Figure 1). Spoiler types were also annotated by the main annotator, and randomly checked by the other two. In sum, each of the 5,000 posts in our corpus consists of a unique ID, the platform from which it was taken, the respective platform's post ID, the post's text (i.e., the clickbait), the URL to the linked document, the manually extracted title and paragraph-divided main content of the linked document, the manually optimized spoiler, the spoiler's character position in the main content, and the type of spoiler (phrase, passage, or multipart). In total, the annotation took about 560 hours, which marked the limit of our budget dedicated for this step. Table 1 summarizes the main statistics of our corpus. Most spoiled clickbait posts come from Twitter (47.5%) and Reddit (36%), whereas the Facebook account contributes less (16.5%). Most spoilers are phrases (42.5%) and passages (40%). That there are fewer multi-part spoilers could be due to the fact that spoiler account operators prefer to spoil simpler clickbait posts. For the corpus, we also provide a fixed random 80/20/20 train/validation/test split to ensure future reproducibility and comparability with our results. Our approach to clickbait spoiling is based on the observation that there are three types of spoilers: (1) phrase spoilers, (2) passage spoilers, and (3) multipart spoilers. We assume that different tailored approaches will work best for each spoiler type. However, an important prerequisite for this is the corresponding classification of clickbait. Therefore, we first investigate how well the spoiler type of a clickbait post can be predicted (Section 4.1). The generation of phrase and passage spoilers for a given clickbait post is similar in that the solution to the problem in both cases amounts to extracting a coherent piece of text from the linked document. To this end, there are a variety of existing approaches in related disciplines whose output is either a phrase or a passage, and which may be adapted to clickbait spoiling. We therefore investigate whether phrase spoilers can be identified by conventional question answering methods (i.e., we treat a clickbait post as a question to which a phrase of the linked document should be returned as the answer; Section 4.2), and whether passage spoilers can be identified by conventional passage retrieval methods (i.e., we treat a clickbait post as a query and the paragraphs of the linked document as the collection from which to retrieve the best passage; Section 4.3). In our evaluation, we focus on phrase and passage spoilers and also examine the abilities of the above question answering and passage retrieval methods to serve as one-size-fits-all solutions for phrases and passages. For multipart spoilers, a novel approach will be needed, which is beyond the scope of our current work but an interesting direction for the future. For the spoiler classification subtask, we experimented with classic feature-based models (Nave Bayes, Logistic Regression, SVM) and the neural models BERT-, DeBERTa-, and RoBERTa. As feature types for the classic models, we use tf and tf  idf -weighted word and POS tag uni-and bigrams from the clickbait post and tf  idf weighted word and POS tag uniand bigrams from the linked document. We include features from the linked document, since it has to be analyzed for the spoiler generation anyway. The idf values are calculated on the OpenWebText corpus (Gokaslan and Cohen, 2019) to prevent any bias from the comparatively small size of our corpus. The input for the neural models is a post concatenated with the main content of the linked document. Viewing a clickbait post for which a phrase spoiler should be derived as a question and the linked document as potentially containing an answer, phrase spoiler generation can be tackled by question answering methods. We therefore employ ten state-of-the-art question answering methods trained on the SQuAD data and fine-tune them on our new clickbait spoiling training set: ALBERT, BERT (cased/uncased), BigBird, DeBERTa (large), ELECTRA, FunnelTransformer, MPNet, and RoBERTa (base/large). Treating the clickbait post whose spoiler type a passage as a query for which the most rele-vant passage from the linked document is to retrieved, passage spoiler generation can be tackled by passage retrieval methods. We therefore use ten state-of-the-art passage retrieval approaches trained on the MS MARCO data: BM25 and QLD in four variants each (alone or with RM3/Ax/PRF query expansion), MonoBERT, and MonoT5. In addition, we also adapt all of the above question answering models to retrieve passages by simply considering the passage as the returned result from which the question answering model extracts its answer. In our evaluation, we assume a setup in which a previous clickbait detection would have (perfectly) identified posts as clickbait. To then evaluate the effectiveness of spoiler type classification on such detected clickbait posts, we conduct three experiments: (1) multi-class, (2) one-vs-rest, and (3) one-vs-one for the types of phrase and passage spoilers. In all cases, the hyperparameters of the six studied classifiers were optimized based on the validation set of our corpus. For the three feature-based approaches, a chi-square feature selection step selected all post-based features and 70% of the document-based features. The post-based features are weighted 4-times higher than the document-based features. Most hyperparameters of the transformer models were left at their default values, but a grid search was used to find the most effective combination of learning rate (1e-5, 4e-5, 1e-4), warm-up ratio (0.02, 0.06, and 0.1), stack size (8, 16, and 32), number of epochs (1 to 10), and maximum sequence length (256, 384, 512). Table 2 shows the balanced accuracy of the six classifiers. All are less effective in the multi-class setting than in the one-vs-rest settings and the transformer-based classifiers are clearly more effec-Table 3: Effectiveness of spoiler type classification in the one-vs-one (phrase-vs-passage) setting on 826 test posts (training: 2,641; validation: 657). tive than the feature-based ones; DeBERTa is best in the multi-class setting (accuracy of 73.63) and RoBERTa in the one-vs-rest ones (79.12 to 80.39). Table 3 shows the accuracy of the six classifiers on the 826 test posts with phrase and passage spoilers (almost balanced setup, since there is hardly any class imbalance). Again, the transformer-based classifiers clearly are more effective than the feature-based ones; with RoBERTa achieving the best accuracy of 80.39. The substantial improvements of DeBERTa and RoBERTa over the feature-based classifiers in all settings (about 910 accuracy points) indicates that classifying the clickbait spoiler type requires more advanced language understanding than what is encoded in the basic features that the Nave Bayes, SVM, or logistic regression classifiers used. To assess the effectiveness of the question answering and passage retrieval methods for clickbait spoiling, we evaluate both for their respective intended spoiler types, but each also for the respective other spoiler type. Multipart spoilers are deferred to future work. We continue to assume that prior clickbait detection (perfectly) identifies clickbait posts as such. Our evaluation of the generated spoilers includes quantitative and qualitative assessments (Section 6.1). In a pilot study with ten question answering and ten passage retrieval models at their default settings, two models in each category dominate the respective others (Section 6.2). The computationally expensive step of hyperparameter optimization is restricted to these four models plus two baselines (Section 6.3). Then, the effectiveness of spoiling clickbait posts dependent on spoiler type is evaluated (Sections 6.4 and 6.5), and compared to an end-to-end clickbait spoiling setup independent of spoiler type (section 6.6). We introduce the measures used to evaluate generated spoilers and how we manually determined thresholds for them above which a generated spoiler is considered as correct. Evaluation measures. To assess the quantitative correspondence between a derived spoiler and the ground truth, we use three question answering-oriented and one passage retrieval-oriented measure: BLEU-4 (Papineni et al., 2002), METEOR (Banerjee and Lavie, 2005) in its extended version of Denkowski and Lavie (2014), BERTScore (Zhang et al., 2020), and Precision@1. The three question answering-oriented measures each calculate a (penalized) harmonic mean of measure-specific definitions of precision and recall when comparing a generated spoiler to the ground truth. In case of BLEU-4, the overlap of word 1to 4-grams is determined (if the length n of a generated spoiler is less than 4 words, we compute BLEUn ), in case of METEOR the overlap of word 1-grams, and in case of BERTScore the best matching embeddings of word pairs. Note that in their original formulation, BLEU-4 and METEOR penalize the score, the more the n -gram order differs. To arrange the measures on a spectrum from calculating predominantly syntactic (BLEU-4) to predominantly semantic similarity (BERTScore), we omit METEOR's penalization term. The question answering-oriented measures are not really suited to assess the effectiveness of passage retrieval models since a retrieved passage is often longer than the ground truth spoiler. Therefore, we also use Precision@1 to measure whether the top-ranked passage contains the ground truth spoiler (all phrase spoilers and 98% of the passage spoilers come from a single passage; for the other passage spoilers, we consider all containing passages as relevant). To calculate the Precision@1 of question answering models, we use the first passage that contains the returned spoiler. High-confidence thresholds. Candidates with higher scores on the question answering-oriented measures BLEU-4, METEOR, and BERTScore are closer to the ground truth. However, it is unclear what score threshold a particular spoiler candidate has to exceed so that it would be considered a true positive in a manual analysis. Determining such thresholds enables high confidence estimations of how many correct spoilers an approach gener-Table 4: Manually determined numbers of false posi-tives/negatives (FP/FN) on 500 sampled clickbait posts with phrase spoilers and 500 with passage spoilers for question answering (top row group) and passage retrieval models (bottom row group), dependent on score threshold (Thresh.), spoiler type, and effectiveness measure (BL4 = BLEU-4, MET = METEOR, BSc. = BERTScore). The thresholds selected for subsequent assessment are indicated by bold FP/FN numbers. In a pilot study, we thus determined such thresholds by running all question answering models (cf. Section 4.2 and 4.3) on a random sample of 500 clickbait posts with phrase spoilers and 500 with passage spoilers. For each post, a random spoiler generated by a question answering model and a random spoiler generated by a passage retrieval model were manually checked for whether they could be viewed as correct. Table 4 shows the number of manually determined false positives and false negatives for different thresholds of BLEU-4, METEOR, and BERTScore. The manually selected subjective thresholds (FP/FN in bold) for each combination of measure, spoiler type, and model type (question answering or passage retrieval) minimize the false positives at a rate where being more strict would incur too many false negatives. For instance, for phrase spoilers and BLEU-4, we set the question answering model threshold at 50% since a more strict threshold of 60% does not reduce the false positives but increases the false negatives. In addition to reporting quantitative mean effectiveness scores, applying the determined thresholds helps to estimate how many of the spoilers of a 7030 Table 5: Pilot study spoiling effectiveness of question answering and passage retrieval models on 200 validation posts (models ordered lexicographically). model would be perceived as good by human readers. This corresponds to a conservative assessment, since we believe that a model should only be deployed to production if it has been tuned to not return a spoiler if in doubt about its correctness; also probably somewhat minimizing the otherwise possible spread of auto-generated misinformation. In a pilot study on 1,000 clickbait posts (800 training, 200 validation), we compare ten question answering and ten passage retrieval models (cf. Table 5) at their default settings to select models for subsequent experiments with more extensive (and expensive) hyperparameter tuning. The question answering models were or are among the most effective in the SQuAD and TriviaQA question answering benchmarks. In our setup, they return a piece of text from the linked document as an an-swer to the clickbait post as the query. As passage retrieval models, we empoly MonoBERT and MonoT5 using their PyGaggle 3 implementations, and eight variants of the popular baseline retrieval models BM25 and QLD using their Anserini implementations (Yang et al., 2017). These models return the most relevant paragraph from the linked document for the clickbait post as the query. Using Nvidia A100 GPUs, the question answering models were first fine-tuned on SQuAD v1.1 and then on the pilot training data. This was the most effective setup from an ablation study with other fine-tuning regimes (e.g., the phrase spoiler BERTScore for RoBERTa-large dropped from 84.04 to 69.91 when only fine-tuned on our pilot study data, to 64.61 when only fine-tuned on SQuAD, and to 46.60 without fine-tuning). Interestingly, the models' SQuAD effectiveness does not predict their spoiling effectiveness (e.g., RoBERTa-base and FunnelTransformer were tied on SQuAD, but RoBERTa-base is more effective at spoiling). This indicates the importance of the pilot study. Table 5 shows the pilot study effectiveness of all models on the 200 validation posts. RoBERTa-large (for phrasal spoilers) and DeBERTa-large (for passage spoilers) are the most effective. Among the passage retrieval models, MonoBERT and MonoT5 achieve the best scores. Contrary to our original assumption that passage retrieval models might be particularly well-suited to identify passage spoilers, MonoBERT and MonoT5 have similar Preci-sion@1 scores on both phrase and passage spoilers and are substantially less effective than the best question answering models (e.g., DeBERTa-large has a Precision@1 of 48.39 for passage spoilers compared to 31.18 for MonoBERT). Given the pilot study results, six models are selected for a more extensive hyperparameter tuning: the best two question answering models (DeBERTa-large was best for phrase spoilers, RoBERTa-large for passage spoilers) plus BERT as baseline, as well as the best two passage retrieval models (MonoBERT and MonoT5) plus BM25 as baseline. As the ablation study in our pilot study showed that fine-tuning the question answering models on SQuAD first and then on our corpus works best, we apply this fine-tuning regime to DeBERTa-large, RoBERTa-large, and BERT using the clickbait spoiling training data (depending on the experiment, either only the phrase spoilers, only the passage spoilers, or both combined). Most hyperparameters of DeBERTa-large, RoBERTa-large, BERT, MonoBERT, and MonoT5 are left at their defaults, but a grid search is run to find the most effective combination of learning rate (1e-5, 4e-5, 1e-4), warmup ratio (0.02, 0.06, 0.1), batch size (8, 16, 32), number of epochs (1 to 10), and maximum sequence length (256, 384, 512). For BM25, we try combinations of k 1 from 0.1 to 0.4 and b from 0.1 to 1.0 with a step size of 0.1. The Phrase Spoilers' column group in Table 6 shows the effectiveness of the selected question answering and passage retrieval models on the 423 test clickbait posts with phrase spoilers. Given the ground-truth spoiler, we report the predicted spoilers' average BLEU-4, METEOR, BERTScore, and Precision@1 (using 1,367 posts with phrase spoilers for training and 335 posts for validation to tune the hyperparameters; cf. Table 1). Overall, DeBERTa-large is the most effective model for phrase spoilers. Based on our high-confidence score thresholds, it generates the correct spoiler for 250300 of the 423 test posts (i.e., for about 6070% of the cases) according to a BERTScore or BLEU-4 evaluation. Similar to our pilot study, the passage retrieval models are comparably ineffective in identifying phrase spoilers. Among them, MonoT5 achieves the highest scores but is even substantially less effective than the question answering baseline BERT. For instance, with a BLEU-4 of 58.89 and probably 257 correct spoilers (61% of the 423 test posts), BERT is way ahead of MonoT5 with a BLEU-4 of 4.95 and only 82 probably correct spoilers (19% of the 423 posts). The Passage Spoilers' column group in Table 6 shows the effectiveness of the selected passage retrieval models on the 403 test clickbait posts with passage spoilers (using 1,274 and 322 posts for training and validation). The numbers of probably correct spoilers are lower for all models compared to the phrase spoilers (even the higher amount of probably correct passage spoilers of the passage retrieval models according to their BERTScore threshold are still worse than the estimated probably correct phrase spoilers according to BLEU-4 or METEOR). Similar to the pilot study, all question answering models are also substantially more effective on passage spoilers than the passage retrieval models. Overall, DeBERTa-large and RoBERTa-large achieve the highest Preci-sion@1 scores and the highest amount of probably correct passage spoilers (about 3541% of the passage spoilers are correctly identified according to our high-confidence thresholds). We evaluate the entire spoiling pipeline using all 826 phrase and passage test posts by comparing two-step pipelines that first classify the spoiler type to then select an appropriately trained spoiler model (trained on the respective type) and single-step approaches that skip the spoiler type classification and simply run the same spoiler model on all posts (trained on the complete training data). For the two-step pipelines, we experiment with two variants: (1) using an artificial classifier that returns perfect oracle-style answers about a post's type, and (2) using the best RoBERTa-based phrase-vs-passage classifier from Section 5. Since the passage retrieval models were less effective in our spoiler experiments (cf. Table 6), we report results only for pipelines with question answering models. In the two-step pipelines the respective question answering models are fine-tuned on the respective spoiler types, in the single-step approach on the combined training data. Table 7 shows the achieved end-to-end effectiveness values. The individual two-step pipelines with oracle type classification (row group Oracle') are substantially more effective than their single-step counterparts without type classification (row group None') that again are more effective than the respective two-step pipelines with real RoBERTa-based type classification (row group Classif.'). Overall, the DeBERTa pipeline with oracle classifier achieves an estimated amount of about 5055% correctly spoiled posts (i.e., 411 to 457 of 826). This result confirms that classifying the required spoiler type can be beneficial for clickbait spoiling. Still, among the currently realistically applicable end-to-end spoiling approaches (with RoBERTa type classification or without spoiler type classi-fication), the one-step DeBERTa approach without spoiler type classification is the most effective according to the number of probably correctly spoiled posts (382 to 409 of the 826 posts, i.e., 4650%). This indicates that the currently best RoBERTa-based spoiler type classifier with its accuracy of 80.39% is still not good enough to result in an end-to-end system that actually benefits from spoiler type classification. Our results show that effectively spoiling clickbait with question answering models is possible in practice but also that there is still room for improvements (e.g., improved spoiler type classification, improved spoiler generation for the individual types, and taking multipart spoilers into account). Clickbait spoiling is a new task to help social media users who do not want to be manipulated into falling for clickbait links. Unlike clickbait detection, which often involves filtering out clickbait posts from users' timelines, clickbait spoiling subverts the curiosity triggered by clickbait, presenting users with the withheld punchline in advance. We compile the first large resource for clickbait with associated spoilers. By interpreting clickbait spoiling as either a question answering task or a passage retrieval task, many possible approaches are available to extract from the linked document of a clickbait post the phrase or passage that spoils it. We have explored the effectiveness of a number of state-of-the-art solutions for both tasks in a large-scale experiment, including fine-tuning the respective models on our resource to determine their effectiveness for type-specific clickbait spoiling. Our experimental setup considers type-specific spoiling on the one hand, but on the other hand it also includes an end-to-end configuration for comparison. Overall, our results show that type-agnostic question answering-based spoiling is the most effective yet, but also that spoiler type-specific solutions have the potential to outperform them. In addition to the possibilities explored, there might also be other approaches to clickbait spoiling: for example, paraphrasing technology could be used to directly transform a clickbait post into a version that contains its own spoiler. With respect to multipart spoilers, the use of summarization models could be an interesting direction to select the different parts of the linked document of a clickbait post that make up its multipart spoiler. We thank Tim Gollub, and our students Jana Puschmann and Bagrat Ter-Akopyan, who helped to create earlier versions of the dataset. The spread of clickbait on social media by news publishers to promote click-through to their web-sites has been empirically found to decrease their perceived credibility in readers (Molyneux and Coddington, 2020). There is, of course, nothing wrong with monitoring and optimizing the effectiveness of marketing a newly published news article, especially in cases where the editors make an honest effort to reach and inform their target audience. But the clickbait in our corpus mostly spreads trivial facts that could have been easily fitted into the length limits of a social media post, which is why we consider these posts to fall short of the journalistic ideal. However, it is as of yet unclear, in terms of journalism ethics, whether clickbait is an acceptable means to an end for publishers (i.e., whether it is necessary in driving audiences to the journalism they need by giving them the journalism they seem to want.), or whether it serves to crowding out real journalism by reducing quality in favor of the need for a click-through at whatever cost (Harte, 2021). Facebook intervened twice with algorithmic fil-ters to reduce the amount of clickbait that people are exposed to in their timelineseven though this probably also lowered Facebook's user engagement metrics. Our technology demonstrates another, complementary way of relatively simply circumventing the purported exploitation of the curiosity gap by giving the audience a choice on whether or not they wish their cognitive loopholes to be exploited. If a sufficiently large portion of people decide to adopt spoiling tools, that would send a clear message to publishers and social media platforms alike. Spoiling clickbait, as opposed to removing it, however, still gives publishers the ben-efit of the doubt, since, as the publishers claim, there are people who enjoy these kinds of trivia.
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