# Automatic Detection of Clickbait Headlines Using Semantic Analysis and Machine Learning Techniques
## 3 Feature Formulation  
Related works in the area of clickbait classification offer insight into semantic features that occur more frequently in clickbait headlines compared to non-clickbait headlines. This section leverages these related works to identify 30 key semantic features linked to clickbait headlines for use in classification modeling. Semantic features associated with clickbait include sentence structure, parts-of-speech, forward referencing, punctuation, common clickbait words and informality. The classification approach used in this project focuses on analyzing the semantic styles of the text in headlines and not the content of the linked pages. [PERSON] et al. [1] found that clickbait headlines typically have a greater word count than conventional non-clickbait headlines. In addition, they determined that even though clickbait headlines have more words, the average word length is shorter. They also recognized that stop words, the most common English words, occur more frequently in clickbait headlines. In addition, they concluded clickbait headlines often employ determiners and contractions. Determiners are comprised of articles (_a/an, the_ ), demonstratives (_this, that, these, those_), possesses (_my, your, his, her, its, our, their_) and quantifiers (_many, much, more, most, some_). Similarly, [PERSON] et al. [17] postulate that forward referencing is another key semantic headline style used to create anticipation and curiosity to lure readers to click. Forward referencing refers to referencing forthcoming parts of the headline upfront or using a word that gets its meaning from a subsequent word or phrase. Forward referencing can be identified by the presence of demonstrative pronouns (_this, that, these, those_), personal pronouns (_I, you, he, she, it, we, they, me, him, her, us and them_), superlative adverbs (_-est, -ly_) and definite articles (_the_). [PERSON] et al. [18] describe how clickbait headlines are made more attention grabbing with the use of acronyms, numbers, upper case letters, questions, quotes, exclamations and other punctuation patterns. They determined that clickbait headlines are more likely to begin with 5W1H words (what, why, when, who, which, how) than non-clickbait headlines. They also observed that the language of clickbait headlines tends to be less formal than that of conventional non-clickbait headlines. To capture this difference in informality [PERSON] et al. utilized four indices that measure the readability/informality level of text. The Coleman Liau Index [19] for readability is based on the number of letters per word and words per sentence. The \(CL\ Index\) is computed by:  
\[CL\ Index=0.0588L-0.296S-15.8, \tag{1}\]  
where \(L\) is the average number of letters per 100 words and \(S\) is the average number of sentences per 100 words. [PERSON]'s RIX Readability Index, \(RIX\ Index\), is a simplified version of [PERSON]'s LIX Readability Index [20], \(LIX\ Index\). Both indices are based on the number of words per sentence. The indices are computed by:  
\[LIX\ Index=\frac{W}{S}+\frac{100 LW}{W}, \tag{2}\]  
and  
\[RIX\ Index=\frac{LW}{S}, \tag{3}\]  
where \(W\) is the number of words, \(LW\) is the number of long words (7 or more letters) and \(S\) is the number of sentences. The formality measure index (**F-Score**) developed by [PERSON] and [PERSON] [21] provides a measure for formality based on the frequencies of different parts of speech of words in the text. They found nouns, adjectives, articles and prepositions are more frequent in formal styles; pronouns, adverbs, verbs and interjections are more frequent in informal styles. The F-Score is computed by:\[ \begin{split}\textbf{F-Score}&=\big{(}noun\ freq+adjective\ freq\\ &+\textit{preposition\ freq+article\ freq\\ &\textit{pronoun\ freq-verb\ freq\ adverb\ freq}\\ &\textit{interjection\ freq+100}\big{)}/2.\end{split}  \tag{4}\]  
[PERSON] et al. also determined the key words: \"reason\", \"why\", \"just\", \"this\" and \"one\" have a high frequency of occurrence in clickbait headlines.