# Automatic Detection of Clickbait Headlines Using Semantic Analysis and Machine Learning Techniques
## 1 Introduction
The titles and contents are stored by different parties whose relationships must be exploited for clickbait detection. In comparison to other cutting-edge methods, their proposed approach demonstrated high efficacy. [PERSON] et al. [10] introduced compiled clickbait corpus and proposed a model for detecting clickbait using convolutional neural networks (CNN). The corpus was built using various social media platforms and deep learning for learning features. The model outperformed other models in detecting clickbait. [PERSON] et al. [11] presented a semi-supervised classification-based approach utilizing attentions sampled from a Gumbel-Softmax distribution. An additional loss over the attention weights was applied to encode prior knowledge. The authors also presented a confidence network, which enables learning over weak labels and improves resiliency to noisy labels. According to the results, the model achieved over 97% accuracy with only 30% of strongly labeled samples. [PERSON] et al. [12] proposed a based neural network classifier with a pre-trained language multilingual bidirectional encoder representations from transformers (M-BERT) model to classify clickbait and non-clickbait headlines. The model was evaluated on a dataset of 6632 headlines using the five-fold cross-validation approach achieving an f1-score of 0.914. [PERSON] et al. [13] presented a system based on the fusion of neural networks, which incorporates various forms of available data. The proposed system requires no linguistic preprocessing and generalizes to new domains and languages more quickly. The model achieves an f1 score of 0.564. [PERSON] et al. [14] proposed a bidirectional LSTM with an attention mechanism to learn the extent to which a word contributes to the clickbait score of a social media post in a different way. They also used a Siamese net to detect similarities between the source and target data. To add another layer of complexity to the model, they also use CNN to learn image embeddings from large amounts of data. Their experiments were carried out on a test corpus of 19538 social media posts and they achieved an F1 score of 0.65. [PERSON] et al. [15] used a random forest regression algorithm to create a computational clickbait detection system. A dataset of over 21,000 headlines/titles was used and the 60 most relevant features were extracted. On the clickbait class, the model achieved an f1 score of 0.61. While previous studies have attempted to address this issue, they have limitations. For example, some studies rely solely on lexical analysis or shallow features, which may not fully capture the semantic meaning of headlines. Other studies do not consider the impact of different machine learning techniques on classification accuracy. To address these limitations, this paper presents an effective method to categorize clickbait and non-clickbait headlines using semantic analysis and machine learning techniques. Thirty unique semantic features were investigated and six different machine learning classification algorithms were explored individually and as ensembles. The classification algorithms utilized are decision tree, logistic regression, naive Bayes, support vector machine, k-nearest neighbor and gradient-boosted decision tree. These algorithms were selected because they are widely used in the field of text classification and have been shown to produce good results. The selection of these algorithms was based on the available literature and past studies that have used these algorithms to perform text classification tasks. Additionally, these algorithms represent a diverse range of techniques and approaches, which allows us to evaluate the effectiveness of different methods and to identify the best approach for categorizing clickbait and non-clickbait headlines. To train, test and validate the six algorithms, a large dataset of 32,000 sample headlines collected from different news websites was used. The dataset contained a 50/50 mix of clickbait and non-clickbait headlines.