# Automatic Detection of Online Recruitment Frauds: Characteristics, Methods, and a Public Dataset
## 4 Employment Scam Detection & Relevant Problems
### Discussion  
Having evaluated all the above, let us analyze our initial observations of employment scam. To begin with, one can immediately grasp that employment scam detection is a non-trivial, primarily text-based problem that is closely affiliated with the aforementioned problems, but still presents several peculiarities. Most of them derive from the limited context surrounding a job ad, the brief user interaction with the ATS, and most importantly the fact that the malicious content aims by definition to be as indistinguishable as possible from the legitimate one.  
As a matter of fact, employment scam lacks strong contextual information. Furthermore, the activity of the composer of a post within an ATS through time is limited, that is, the user may generate a single advertisement, broadcast it and then not further interact with the ATS. In some cases, assailants impersonate existing businesses or recruiting agencies, which makes it harder to deduce the real origin of the job posting. On the contrary, in problems such as trolling or cyber bullying detection, the analyst is able to compose additional contextual and temporal information, regarding the reputation of the misbehaving user, their sequence of actions, and their online footprint mined from multiple open social platforms.  
At the same time, ATS are offered as web applications over HTTP, which typically do not entail any dedicated network communication protocol as for example in email spam. As in phishing or wikipedia vandalism, this fact alone makes it impossible to rely on multiple protocol layers for additional indications. As for the application layer, structural anomalies (e.g., invalid HTML markup or CSS rules), visual contrivances, or incomplete company profiles are in most cases products of low-skilled practitioners and serious attackers can easily circumvent them. Moreover, information such as the location of a job or uploading the corporate logo are often neglected even by expert users.  
As with opinion fraud detection discussed in Section 4.6, relying just on the raw content often proves to be insufficient. Added to that, our experimentation presented in Section 6 also confirmed that unilateral classifiers will mislabel at least one out of ten job ads. More precisely, opinion fraud heavily relies on detecting the outliers among a large number of available reviews about the same product on the same or similar websites. In other words, the cardinality and the coherence of legitimate reviews are of the essence, whereas in employment scam maintaining a consistent hiring history for legitimate companies is neither straightforward nor practical. Lastly, it is questionable whether alternate approaches such as sentiment analysis could be effectively applied to employment scam, as compared to biased reviews trying to hype or defame products or businesses, the content of a job ad is usually written in neutral language.  
In summary, Table 1 presents the feature categories used for detecting malicious content in all six relevant problems discussed in Section 4. Employment scam detection features used in Section 6 are also added. In future work, we would like to experiment with more feature categories.