# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 6 Analysis and Experimentation
### Empirical Analysis
#### 6.2.2 Text Analysis  
Scammers are less likely to compose a meticulous job ad for the company they claim to be hiring for. As a matter of fact, fraudulent job and company descriptions tend to be short and sketchy. Furthermore, while job requirements and benefits are present in most legitimate job ads, scammers tend to leave them blank. Figure 3 depicts the term counts histograms for both classes for each of the HTML attributes contained in the dataset (see Table 2). To reduce noise in the results, all the HTML  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Correctly** \\ **Classified \%** \\ \end{tabular} & \begin{tabular}{c} **Incorrectly** \\ **Classified \%** \\ \end{tabular} & \begin{tabular}{c} **TP** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **FP** \\ **Rate** \\ \end{tabular} & **Precision** & **Recall** & **F-measure** &
\begin{tabular}{c} **ROC** \\ **Area** \\ \end{tabular} & **Time (s)** \\ \hline ZeroR & 50.000 & 50.000 & 0.500 & 0.500 & 0.250 & 0.500 & 0.333 & 0.500 & 0.000 \\ Logistic regression & 77.222 & 22.778 & 0.772 & 0.228 & 0.783 & 0.772 & 0.770 & 0.810 & 30.860 \\ Onek & 77.333 & 22.667 & 0.773 & 0.227 & 0.777 & 0.773 & 0.773 & 0.773 & 0.270 \\ J48 & 84.778 & 15.222 & 0.848 & 0.152 & 0.848 & 0.848 & 0.848 & 5.620 \\ Naive Bayes & 86.333 & 13.667 & 0.863 & 0.137 & 0.869 & 0.863 & 0.863 & 0.901 & 0.620 \\ Random forest & 91.222 & 8.778 & 0.912 & 0.088 & 0.914 & 0.912 & 0.912 & 0.970 & 4.040 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification evaluation of the bag of words model.  
Figure 2: Geographical distribution. (**a**) Top four counties with fraudulent job postings (US, Australia, United Kingdom, Canada); (**b**) Number of telecommuting job ads in fraudulent (red) and non fraudulent (blue) dataset entries. Notice that the amount of malicious job ads that contain the characteristic is over two times greater than the benign one.  
tags were stripped before computing the term counts. As shown in Figure 3a, more than half of the fraudulent job postings on the examined sample have job descriptions that do not extend beyond 100 terms. This contradicts the majority of normal postings, only 25% of which are short. According to Figure 3b, 88% of fraudulent postings (around 400), have very short descriptions. The same applies to job requirements and benefits where as observed from Figure 3c,d, around 240 fraudulent job ads have these fields blank.  
According to human experts, another common method fraudsters adopt, to capture job seekers' attention is to capitalize words in the title or use consecutive punctuation to make it stand out. In EMSCAD, 17% of fraudulent job titles and 15% of legitimate job titles contain capitalized words. Added to that, 3% of fraudulent titles contain consecutive punctuation such as multiple exclamations marks.  
Moreover, token sequences such as 'work from home', 'be your own boss', 'income', 'easy money', 'no experience', inside a job ad indicate alarming scam content. Figure 4 illustrates occurences of common spamwords in both classes. For instance, tokens like 'online', 'home' are more likely to be present in fraudulent job ads, whereas words such as 'business', 'opportunity' are more probable to coexist in legitimate contexts.  
Figure 4: Spamword occurences in legitimate and fraudulent classes.  
Figure 3: Term count (TC) histogram computed on the HTML attributes contained in Table 2. (**a**) Job description TC; (**b**) Company description TC; (**c**) Job requirements TC; (**d**) Job benefits TC.