# Automatic Detection of Clickbait Headlines Using Semantic Analysis and Machine Learning Techniques
## 1 Introduction  
As a result of the proliferation of information online, we are now subjected to a barrage of advertisements and news headlines on virtually every page we access. Since so many people have access to the internet, websites and news outlets are constantly competing for viewers. As a result, they are under pressure to create ever more appealing, catchy and provocative article headlines, regardless of their accuracy. Because of this, there has been a rise in recent years of sensationalist \"news\" headlines that do not tell readers anything valuable about the story but are meant to grab their attention [1]. The term 'clickbait' refers to misleading links with sensationalized headlines that intend to attract the viewers' attention and entice them to click on the link [2]. In a broad sense, clickbait headlines meet two main criteria: (1) they mislead readers about the article's contents, and (2) they take advantage of the so-called \"curiosity gap\" by not explaining the entire article's contents. To put it more simply, the text included in these headlines either makes the reader curious about the rest of the article's contents or discusses topics that are not addressed in the body of the article itself. It is imperative that we make a clear distinction between clickbait and fake news, a topic that has been receiving a growing amount of attention as of late. The difference lies in the fact that fake news purposefully presents its audience with information that they should know is false in order to gain their trust. On the other hand, clickbait almost always just contains \"junk\" news that lacks any real journalistic integrity and is not designed to trick the reader into believing false claims. Clickbait can pose a threat to Internet users and has become more prevalent across the web, not just on less reputable sites [3]. Recent research from Stanford University highlights how clickbait is making its way onto more reputable journalism sites [4]. Clickbait can have an even more malicious purpose such as phishing for personal information, or even worse, hosting malware. For these reasons, clickbait is a serious issue that must be addressed. The first step in addressing this problem is to distinguish clickbait headlines from true headline links. On the surface, clickbait headlines can be hard to recognize, as they aredesigned to fool the user, but there are key semantic features that can help identify clickbait headlines. AI tools based on machine learning algorithms can detect and block clickbait in a systematic manner. Machine learning unlocks the power of data in novel ways [5]. This technology assists computer systems in learning from and improving on their experiences by creating computer programs that can automatically access data and perform tasks through predictions and detections. As you feed more data into a machine, the algorithms learn more about the machine, which improves the results [6]. Several works based on machine learning techniques have been proposed for clickbait classification. [PERSON] et al. [7] developed ClickBaitSecurity to distinguish between legitimate and illegitimate links by accurately using a recurrent neural network (RNN). In comparison to existing solutions, the test results showed that their proposed model has high accuracy in detecting malicious and safe links. [PERSON] et al. [8] introduced a content-agnostic scheme, Online Video Clickbait Protector (OVCP), to effectively detect clickbait videos by analyzing the comments left by viewers of the video. Unlike other solutions, OVCP does not directly analyze the video's content and pre-click data. As a result, it is resistant to sophisticated content creators who frequently create clickbait videos that can evade current clickbait detectors. Their experiments proved that OVCP could accurately identify clickbait videos. Using social media datasets, [PERSON] et al. [9] proposed federated hierarchical hybrid networks to build clickbait detection models.