Clickbait refers to something (such as a headline) designed to make readers want to click on a hyperlink especially when the link leads to content of dubious value or interest111https://www.merriam-webster.com/dictionary/clickbait. With the rise of Twitter, publishers have been adopting various techniques to create the Curiosity Gap  between the information contained in the posted texts and the information the readers really want to know. This gap drives the readers to click on the links contained in the tweets, and visit the publishers’ websites. According to , all the top 20 most prolific publishers on Twitter employed clickbait on a regular basis, and the percentage of clickbait tweets reached an astonishing 26% among all the tweets they published.
The clickbait tweets deliberately omit importance information, as well as include exaggerating and misleading information in the posted texts, as can be seen from the following examples:
If you’ve ever used Google Docs for anything important, you should know about this.
This Tumblr account will nail your personality down in a second.
Here comes (almost) free money.
Clickbait draws negative impacts on readers, publishers, as well as social media websites. First, it wastes the readers’ time, leaves them feel disappointed and annoyed. Second, it damages the publishers’ reputation, as it violates the general codes of ethics of journalism. Third, the traffic of the social media websites will be negatively affected if clogged up with low-quality and formulaic click baiting content.
This paper seeks to employ automatic approach to filter out clickbait tweets in the tweet stream. We present the first attempt of applying self-attentive neural network to evaluate each tweet’s level of click baiting, the effectiveness and efficiency of which has been proven in the Clickbait Challenge 2017 .
2 Related Work
Researchers have been exploring automatic approaches to perform Clickbait Detection. However, most of the attempts focused on news headlines. In , researchers suggested that “clickbait can be identified through a consideration of the existence of certain linguistic patterns, such as the use of suspenseful language, unresolved pronouns, a reversal narrative style, forward referencing, image placement, reader’s behaviour and other important cues.” However, their did not validate their conjecture by constructing corresponding automatic clickbait detector. Researchers in 
extracted content features, textual similarity features between the headline and the body, as well as informality and forward reference features from the news on Yahoo homepage, and trained Gradient Boosted Decision Trees to decide if a news article was a clickbait. Feature engineering was also employed in
, where researchers constructed vector representations for Wikinews headlines by extracting sentence structure features, word pattern features, clickbait language features, as well as N-gram features. They further trained a SVM classifier to perform binary classification on the resulting vector representations. Their results were further improved by researchers in and 
, by using bidirectional Recurrent Neural Network (RNN) and fastText 
on word distributed representations, respectively. Following[6, 7], researchers in  went back to manual feature engineering, to extract various body dependent and body independent features from Chinese news articles; then they applied the co-training algorithm to make use of the unlabelled data set. Besides the texts, researchers in  additionally took the user behaviour information into consideration, to improve the performance of clickbait detection on Chinese news articles.
There are some recent researches targeting at training clickbait detectors on posts from social media websites, such as Twitter. For example, researchers in 
trained a random forest classifier by extracting various features from the post texts, linked webpages and associated meta information of tweets, to decide if a tweet was a clickbait. Researchers in
trained a Convolutional Neural Network (CNN), using the post texts only, to detect clickbait posts in Reddit, Facebook and Twitter. In , researchers analysed the differences in content, sentiment, consumers, etc., between the clickbait tweets and non-clickbait tweets.
All of former researches treat automatic clickbait detection as a binary classification problem. According to , whether a tweet was a clickbait was not a simple black and white problem, thus it was better to associate each tweet with a graded value reflecting its level of click baiting, rather than a binary indicator. Thus, different from former researches, this paper tackles the Clickbait Challenge 2017222http://www.clickbait-challenge.org/ , i.e., rating how click baiting each tweet is.
Since proposed in 
, the attention mechanism has been applied on various text classification-related problems. For example, aspect-based sentiment analysis[26, 30], target-specific stance detection , and fake news detection . The attention mechanism releases the text encoders from the burden of encoding all the information in the text into one fixed length vector, and allows the neural network model to automatically attend to tokens that are important for the prediction. The above researches leveraged extra information to form a context vector, which was further employed to infer the weights of token embeddings in the text, when aggregating them into one task-specific sentence embedding. However, for the Clickbait Challenge 2017, there is no external information to employ, thus the context vector can only be learned from the text itself. In this paper, we present the first attempt to apply the state-of-the-art self-attentive network [32, 14] on Clickbait Detection in tweets.
3.1 Problem Definition
The Clickbait Challenge 2017 was defined as a regression problem, asking the competition teams to automatically evaluate the clickbait score of each tweet in the test dataset, indicating the its level of click baiting. Instead of performing regression directly, we performed a detailed analysis of the annotation scheme to understand how the clickbait score in the training datasets was generated, and reformatted the problem. When crowdsourcing the dataset, for each tweet, four categories were provided to five annotators, which were “not click baiting”, “slightly click baiting”, “considerably click baiting” and “heavily click baiting” [21, 23]. The above four categories were assigned with values 0, , and 1.0, respectively, to demonstrate the tweet’s level of click baiting. As a result, each annotated tweet in the training datasets were associated with five fields, which were “truthJudgements”, “truthMean”, “truthMedian”, “truthMode” and “truthClass”. The “truthJudgements” fields demonstrated the choices made by the five different annotators; the “truthMean”, “truthMedian” and “truthMode” fields were the mean, median and mode of the five annotators’ choices, respectively, after mapping the choices into their corresponding values. For one tweet, if the number of annotators saw it as “not click baiting” or “slightly click baiting”, was larger than the number of annotators saw it as “considerably click baiting” and “heavily click baiting”, then the “truthClass” of this tweet would be “clickbait”. Otherwise, its “truthClass” would be “no-clickbait”.
The clickbait detectors were asked to automatically infer the “truthMean” for each tweet in the test dataset. Our Zingel Clickbait Detector reproduced the above process by generating the predicted annotation distribution for each tweet on , where . Based on the annotation distribution, the following calculations were performed:
Similar strategy was applied by  to infer the semantic relatedness of sentence pairs.
3.2 Self-attentive Network
We applied the self-attentive network [32, 14], to tackle the annotation distribution prediction problem. Self-attentive RNN applied a token level attention mechanism over the hidden states generated by the RNN, to infer the levels of importance of the tweet tokens in predicting the annotation distribution. Different from regular external-attentive network [4, 26, 30, 34, 10], self-attentive network does not need any external information to learn the context vector. The inferred information can be used as the tokens’ weights, when aggregating their hidden states into the vector representation of the tweet.
Figure 1 demonstrates the architecture of the self-attentive network. Given a tweet whose “postText” contains tokens, we first maped each token , where , to its corresponding word embedding , through an word embedding matrix , where denotes the dimensionality of the word embedding, denotes the size of the vocabulary. After that, we used a bi-directional GRU  to encode the contextual information from both directions of the token into its hidden state:
where . The resulting hidden state of biGRU for each token was the concatenation of its forward hidden state and backward hidden states, i.e.,
The token level attention vector , which represents the weights of tokens in “postText” when predicting the annotation distribution, can be calculated as:
where , and ; , are the parameters to train; the function guarantees that .
The vector representation of the tweet’s “postText” can be calculated as:
The predicted annotation distribution of this tweet on four categories can be calculated as:
where and the bias term are the parameters to train.
Only the “postText” field of the each labelled sample was employed in our approach. Because the human annotators made the judgements only based on the tweet’s plain text and image . As a large proportion of tweets were not associated with images, we did not employ images when training the detector, so that the same detector can be applied to annotated all the tweets.
4 Experiments and Results
4.1 Dataset Description
In this challenge, three datasets were provided to the competition teams, which were Dataset A, Dataset B (Unlabelled), and Dataset C. The statistics of these datasets are shown in Table 1.
|Dataset||# tweets||# clickbait||# no-clickbait|
4.2 Model Training
We inferred the annotation distribution on four categories from the “truthJudgments” field, for all the labelled samples. The self-attentive network was trained by optimising the cross-entropy loss between the true annotation distribution and the predicted annotation distribution. We used the Adam optimiser  to train the parameters in the self-attentive network, which had the best overall performances on sparse training data, according to .
To make the trained models less prone to overfitting, we applied dropout on the outputs of the word embedding layer, on the outputs of the biGRU encoding layer, as well as on the outputs of the self-attentive layer [28, 19]. To reduce the space of hyper-parameter searching, we set to 100, set to 64, set a dropout rate of 0.2 on the outputs of the word embedding layer, set a dropout rate of 0.3 on the outputs of the biGRU encoding layer, based on our former neural network training experiences; because we observed these hyper-parameters had very limited influence on the performance of the clickbait detector. We initialised the word embedding matrix with the 100-dimension pre-trained Glove embeddings  of Wikipedia data. We performed additional experiments by initialising with 100-dimension pre-trained Glove embeddings of tweets, as well as 100-dimension pre-trained Word2Vec embeddings  of Data B, but their performances were not comparable with our initial choice. The word embedding matrix was updated during the training process, to be adjusted for the clickbait detection scenario.
The same preprocessing step was adopted as the one applied by Pennington et al.  to generate pre-trained embeddings of tweets333https://nlp.stanford.edu/projects/glove/preprocess-twitter.rb. The preprocessed tweets was tokenised by the TweetTokenizer in NLTK444http://www.nltk.org/. We used the maximum of “postText” token numbers in the training dataset(s) as the input length
, thus all the “postText”s of tweets were either zero-padded or truncated to the same length.
All the tokens that were not included in the pre-trained Glove embeddings of Wikipedia data were replaced by “
For the rest hyper-parameters, we performed more rigid hyper-parameter optimisation, using Hyperopt , through 5-fold cross-validation on Dataset C only. Specifically, we selected the batch size from ; we selected the dropout rate on the outputs of the self-attentive layer from ; we selected the learning rate of the Adam optimiser from
; we also applied gradient clipping to avoid gradient explosion, and the clipping cutoff threshold was selected from
. For each round of cross-validation, we trained the neural network for a maximum number of 20 epochs on 80% samples from Dataset C (15630 samples); and used the detector’s Mean Squared Error (MSE) on the remaining samples from Dataset C to determine the number of epochs. We averaged the least MSEs for all rounds of cross-validation for each hyper-parameter set, and used it as the criterion to select the best combination of hyper-parameters, which were demonstrated above as bold numbers among various choices.
We did not employ Dataset A during the hyper-parameter optimisation step, so that we can get an idea of the detector’s performance on unseen data.
In our final submission, we combined Dataset A and Dataset C together, to leverage all the labelled data for training. Since the number of epochs was essential for the detector’s performance, we applied the same strategy as the hyper-parameter optimisation step. Specifically, we trained 5 self-attentive neural networks, with each using 80% of the labelled samples from the combined dataset for training, and the remaining 20% for validation to decide the number of epochs. All the hyper-parameters of the 5 neural networks were the same, which were selected in the hyper-parameter optimisation step. For each sample in the test dataset, the annotation distributions generated by the 5 neural networks were averaged to generate the final “truthMean” and “truthClass” prediction, as described in Section 3.1. The evaluation was performed on a Ubuntu 16.04 server provided by the TIRA platform . The Zingel Clickbait Detector achieved an MSE of 0.033 (ranked ) in the final evaluation, which substantially outperformed the baseline approach reported in 
, with an MSE of 0.044. Besides that, our detector also outperformed all the other detectors in term of F1 score (0.683), Accuracy (0.856) and Running Time (00:03:27). Thus, the Zingel Clickbait Detector achieved consistent and competitive performance across multiple evaluation metrics, with very low computational cost.
In this paper, we have presented the solution for the Zingel Clickbait Detector. We tackled the clickbait score prediction problem in Clickbait Challenge 2017 by performing multi-classification using the self-attentive neural network. The Zingel Clickbait Detector has achieved the state-of-the-art performance on the Twitter Clickbait Score Prediction dataset provided by Clickbait Challenge 2017, in terms of MSE, F1 score, Accuracy and Running time.
This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1.
Abadi et al. 
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado,
A. Davis, J. Dean, M. Devin, et al.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2015.
Clickbait detection using deep learning.In Proceedings of the 2nd International Conference on Next Generation Computing Technologies, pages 268–272. IEEE, 2016.
- Anand et al.  A. Anand, T. Chakraborty, and N. Park. We used neural networks to detect clickbaits: You won’t believe what happened next! In Proceedings of the 39th European Conference on Information Retrieval, pages 541–547. Springer, 2017.
- Bahdanau et al.  D. Bahdanau, K. Cho, and Y. Bengio. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations, 2015.
Bergstra et al. 
J. Bergstra, D. Yamins, and D. D. Cox.
Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms.In Proceedings of the 12th Python in Science Conference, pages 13–20, 2013.
Biyani et al. 
P. Biyani, K. Tsioutsiouliklis, and J. Blackmer.
“8 amazing secrets for getting more clicks”: Detecting clickbaits
in news streams using article informality.
Proceedings of the 30th AAAI Conference on Artificial Intelligence, pages 94–100. AAAI, 2016.
- Chakraborty et al.  A. Chakraborty, B. Paranjape, S. Kakarla, and N. Ganguly. Stop clickbait: Detecting and preventing clickbaits in online news media. In Proceedings of the 2016 International Conference on Advances in Social Networks Analysis and Mining, pages 9–16. IEEE, 2016.
- Chakraborty et al.  A. Chakraborty, R. Sarkar, A. Mrigen, and N. Ganguly. Tabloids in the era of social media? understanding the production and consumption of clickbaits in twitter. In Proceedings of the 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing. ACM, 2018.
- Chen et al.  Y. Chen, N. J. Conroy, and V. L. Rubin. Misleading online content: Recognizing clickbait as false news. In Proceedings of the 2015 ACM Workshop on Multimodal Deception Detection, pages 15–19. ACM, 2015.
Chopra et al. 
S. Chopra, S. Jain, and J. M. Sholar.
Towards automatic identification of fake news: Headline-article stance detection with lstm attention models, 2017.
- Grave et al.  E. Grave, T. Mikolov, A. Joulin, and P. Bojanowski. Bag of tricks for efficient text classification. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 427–431, 2017.
Convolutional neural networks for sentence classification.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1746–1751. ACL, 2014.
- Kingma and Ba  D. Kingma and J. Ba. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations : Poster Session, 2015.
- Lin et al.  Z. Lin, M. Feng, C. N. d. Santos, M. Yu, B. Xiang, B. Zhou, and Y. Bengio. A structured self-attentive sentence embedding. In Proceedings of the 5th International Conference on Learning Representations, 2017.
- Loewenstein  G. Loewenstein. The psychology of curiosity: A review and reinterpretation. Psychological bulletin, 116(1):75, 1994.
Mikolov et al. 
T. Mikolov, K. Chen, G. Corrado, and J. Dean.
Efficient estimation of word representations in vector space.In Proceedings of the 1st International Conference on Learning Representations : Workshop Track, 2013.
- Pascanu et al.  R. Pascanu, T. Mikolov, and Y. Bengio. On the difficulty of training recurrent neural networks. In Proceedings of the 30th International Conference on Machine Learning, volume 28, pages 1310–1318, 2013.
- Pennington et al.  J. Pennington, R. Socher, and C. D. Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on Empirical Methods in Natural Language Processing, volume 14, pages 1532–1543. ACL, 2014.
- Pham et al.  V. Pham, T. Bluche, C. Kermorvant, and J. Louradour. Dropout improves recurrent neural networks for handwriting recognition. In Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition, pages 285–290, 2014.
- Potthast et al.  M. Potthast, T. Gollub, F. Rangel, P. Rosso, E. Stamatatos, and B. Stein. Improving the Reproducibility of PAN’s Shared Tasks: Plagiarism Detection, Author Identification, and Author Profiling. In Proceedings of CLEF 2014 Conference and Labs of the Evaluation Forum, pages 268–299. Springer, 2014.
- Potthast et al.  M. Potthast, S. Köpsel, B. Stein, and M. Hagen. Clickbait Detection. In Proceedings of the 38th European Conference on Information Retrieval, volume 9626, pages 810–817. Springer, 2016.
- Potthast et al. [2017a] M. Potthast, T. Gollub, M. Hagen, and B. Stein. The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength. In Proceedings of the Clickbait Challenge, 2017a.
- Potthast et al. [2017b] M. Potthast, T. Gollub, K. Komlossy, S. Schuster, M. Wiegmann, E. Garces, M. Hagen, and B. Stein. Crowdsourcing a Large Corpus of Clickbait on Twitter. In (to appear), 2017b.
- Rony et al.  M. M. U. Rony, N. Hassan, and M. Yousuf. Diving deep into clickbaits: Who use them to what extents in which topics with what effects? arXiv preprint arXiv:1703.09400, 2017.
- Ruder  S. Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016.
- Ruder et al.  S. Ruder, P. Ghaffari, and J. G. Breslin. A hierarchical model of reviews for aspect-based sentiment analysis. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 999–1005. ACL, 2016.
- Schuster and Paliwal  M. Schuster and K. K. Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681, 1997.
- Srivastava et al.  N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958, 2014.
Tai et al. 
K. S. Tai, R. Socher, and C. D. Manning.
Improved semantic representations from tree-structured long short-term memory networks.In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, pages 1556–1566. ACL, 2015.
- Wang et al.  Y. Wang, M. Huang, X. Zhu, and L. Zhao. Attention-based lstm for aspect-level sentiment classification. In Proceedings of the 2016 conference on Empirical Methods in Natural Language Processing, pages 606–615. ACL, 2016.
- Wei and Wan  W. Wei and X. Wan. Learning to identify ambiguous and misleading news headlines. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 4172–4178. AAAI, 2017.
- Yang et al.  Z. Yang, D. Yang, C. Dyer, X. He, A. J. Smola, and E. H. Hovy. Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies, pages 1480–1489. ACL, 2016.
- Zheng et al.  H.-T. Zheng, X. Yao, Y. Jiang, S.-T. Xia, and X. Xiao. Boost clickbait detection based on user behavior analysis. In Proceedings of the 1st Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, pages 73–80. Springer, 2017.
- Zhou and Cristea  Y. Zhou and A. I. Cristea. Connecting targets to tweets: Semantic attention-based model for target-specific stance detection. In Proceedings of the 18th International Conference on Web Information Systems Engineering, pages 18–32. Springer, 2017.