## 1 Introduction  
Currently, social networks have become the main environment for communicating, sharing, and posting news on the Internet. Twitter, Facebook, and Instagram are the main social networks that are used to share our opinions and news. With this development, a huge amount of textual data are posted on these media, which increasingly become difficult to process manually. Although the social networks provide an easy way to express our opinions, this platform also can be used to share misinformation in the form of news and advertisements. This is a very serious issue, because this misinformation has the power to influence individuals and sway their opinions. Therefore, finding a way to protect users of social networks from the spread of this misinformation and develop a reliable mechanism to detect it is very important. This misinformation can take the form of clickbait, which aims at enticing the users into clicking a link to news items or advertisements, whose titles (headlines) do not completely reflect the inside contents. According to [PERSON] et al. [1], clickbait is defined as \"Content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page\". The automatic detection of clickbait headlines from the huge volume of news on social networks has become a difficult research issue in the field of data science. Some previous efforts have utilized machine learning to detect clickbait headlines automatically. For instance, [PERSON] et al. [2] applied Gradient Boosted Decision Trees (GBDT) on a dataset drawn from news sites such as Huffington Post, New York Times, CBS, Associated Press and Forbes. The dataset contains 1349 clickbait and 2724 non-clickbait webpages. The best results achieved were an F1-score of 61.9% with five-fold cross-validation for the clickbait class and an F1-score of 84.6% for the non-clickbait category. [PERSON] et al. [3] applied linear regression, Naive Bayes, and random forest methods on a dataset gathered from Twitter. The dataset contained 2992 data points. The results recorded were relatively close, with an approximate precision of 75%. [PERSON] et al. [4] built a browser extension that used support vector machine (SVM), decision tree, and random forest to automatically detect the clickbait headlines. For training purpose, they collected a well-balanced dataset which contains 30,000 headlines (clickbait and non-clickbait) from ViralStories, Upworthy, BuzzFeed, Wikinews, Scoopwhoop, and ViralNova. In addition, for each data point in the dataset, they extracted sentence structure, clickbait language, word patterns, and n-gram features. The results they achieved are as follows: SVM: an accuracy rate of 93% with 95% precision, 90% recall, 93% F1-score, and 97% ROC-AUC values; Decision Tree: 90% accuracy rate with 91% precision, 89% recall, 90% F1-score, and 90% ROC-AUC values; Random Forest: 92% accuracy rate, 94% precision, 91% recall, 92% F1-score, and finally; ROC-AUC values of 97% using a combination of all extracted features. [PERSON] et al. [5] proposed the use of logistic regression and linear SVM. They extracted 28 features from a dataset provided by Bauhaus-Universitat Weimar at the time of a clickbait detection challenge. The most commonly extracted features were Bag of Words (BOW), noun extraction, similarity, readability, and formality. The best results achieved were 79% and 78% precision for logistic regression and linear SVM respectively. Since the methods of the first category require extracting and labeling each feature before feeding the data into the machine learning tool, researchers have found that deep learning techniques are useful to overcome the feature engineering phase. For instance, [PERSON] et al. [6] combined metric learning with a CNN deep learning algorithm by integrating them with case-based reasoning methodology. For feature selection, they used TF-IDF, n-gram, and 300 dimensional Word2 Vect using the dataset provided by [4].