# Automatic Detection of Clickbait Headlines Using Semantic Analysis and Machine Learning Techniques
## 4 Feature Analysis  
The statistical analysis results in Table 2 show that all 30 features exhibit definitive different occurrence rates between clickbait and non-clickbait confirming their usefulness for classifying headlines. Several feature statistical differences stand out more than others signifying potential top classifiers. The features _\"Ratio words begin w/uppercase\", \"Ratio of stop-words \", \"Begins w/ Number\", \"Contains possessive/pronoun\" and \"Contains demonstrative\"_ have significant occurrence rate differences between clickbait and non-clickbait headlines. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Count Features** & **Forward Referencing Features** & **Readability/Informality** \\ \hline Word count & Begins w/determiner/superlative & CL Index \\ Aver word length & Contains determiner/superlative & LIX index \\ Ratio of stop-words & Contains demonstrative & RIX index \\ Ratio words begin w/uppercase & Contains possessive/pronoun & F-Score \\ \hline
**Misc. features** & **Key Word features** & **Punctuation features** \\ \hline Begins w/ number & Begins w/SW1H & Multi quotes (“”) \\ Contains a number & Contains ”reason” & Exclamation pt. (?) \\ Contains an acronym & Contains ”why” & Parenthesis (?) \\ Contains contraction & Contains ”just” & Asterisk (*) \\  & Contains “this” & Question mark (?) \\  & Contains ”one” & Colon (?) \\  & & Semicolon (?) \\  & & Money ($) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key semantic classification features. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & & & & & & & & & \\ \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Word Count**} & \multirow{2}{*}{**Word Length**} & \multicolumn{2}{c}{**Upper**} & \multicolumn{2}{c}{**Stop**} & \multirow{2}{*}{**F Score**} & \multirow{2}{*}{**LIX**} & \multirow{2}{*}{**RIX**} & \multicolumn{2}{c}{**CL Score**} \\  & & & & & & & & & & \\ \multirow{2}{*}{} & Clickbait & 10.0 & 4.7 & 1.0 & 0.0 & 61.7 & 29.4 & 1.9 & 8.4 \\ Non-Clickbait & 8.4 & 5.3 & 0.5 & 0.2 & 78.5 & 40.5 & 2.6 & 11.7 \\ \hline \hline \multicolumn{8}{c}{**Percent Begin w/**} & \multicolumn{4}{c}{**Percent Begin w/**} & \multicolumn{4}{c}{**Percent**} & \multicolumn{4}{c}{**Percent**} & \multicolumn{4}{c}{**of Headlines Containing**} & \multirow{2}{*}{**Contains**} \\  & **SW1H** & **Number** & **Det/Sep** & **Number** & **Det/Sup** & **Pronoun** & **Contract** & **Demons** \\ \hline Clickbait & 7.9\% & 37.4\% & 19.3\% & 46.0\% & 65.4\% & 58.7\% & 23.2\% & 25.9\% \\ Non-Clickbait & 0.4\% & 4.9\% & 3.2\% & 24.0\% & 20.8\% & 8.8\% & 7.2\% & 1.1\% \\ \hline \hline \multicolumn{8}{c}{**Percent of Headlines Containing**} \\ \hline
**Type** & **”** & 1 & **()** & **()** & **()** & **()** & **()** & **()** & **()** & **()** \\ \hline Clickbait & 4.2\% & 0.2\% & 0.4\% & 0.1\% & 0.3\% & 2.3\% & 0.0\% & 0.3\% \\ Non-Clickbait & 0.6\% & 0.1\% & 0.2\% & 0.0\% & 0.7\% & 4.4\% & 1.4\% & 1.3\% \\ \hline \hline \multicolumn{8}{c}{**Percent of Headlines Containing**} \\ \hline
**Type** & **“reason”** & **“why”** & **“just”** & **“this”** & **“one”** & **Acronym** & \\ \hline Clickbait & 0.2\% & 1.3\% & 2.4\% & 11.2\% & 1.9\% & 6.3\% & \\ Non-Clickbait & 0.0\% & 0.0\% & 0.2\% & 0.3\% & 1.2\% & 15.5\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature statistics. The stacked bar chart in Figure 1 shows the normalized occurrence rate of the 22 binary features in clickbait and non-clickbait headlines. Features on the right with normalized rates above 0.5 are more associated with clickbait headlines. The higher the rate the higher the association with clickbait headlines. The opposite is true on the left. Features on the left with normalized rates below 0.5 are more associated with non-clickbait headlines. The lower the rate the higher the association with non-clickbait headlines. The eight continuous numeric value features (ratio and count features) were transformed into binary values using discretization.