# An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet +
## 6 Conclusions  
The paper dealt with the identification of mixed-code text for the detection of hidden propaganda. First a possible categorisation of different types of existing mixed-code on the basis of two parameters, related to the _language_ and the _graphical writing style_ has been provided. The study has been focused on the analysis of one type of _Text as Art Form_ written on a single row (called \"Special Characters for Alphabets\"), by adopting a methodological approach centred on two main aspects: (i) mixed code text analysis and (ii) hidden propaganda detection. In particular, regarding the mixed code text analysis, a four-step algorithm (called _Text Segregation_, _Character Transformation_, _Word Selection_ and _Sentence Reconstruction_) that supports both the identification of mixed code in text along with its normalisation into natural language has been proposed.  
Whereas, regarding the hidden propaganda detection, a Convolutional Neural Network classifier has been chosen among other ML algorithms, as it provided the best performance in the detection of propaganda. The overall performance of the method has been experimented on a public available dataset containing a collection of 15,847 textual propaganda and nonpropaganda related items. The results showed good performances, by achieving 92% Accuracy and Precision, whereas 91% F1-Score and 90% Recall, which are on average better in comparison to the related work.  
The impact of this solution lies in the ability to automate and consequently speed up the identification of sources and individuals, who use mixed-code to dissemination propaganda content, linked to extremist behaviour, so as to support the Law Enforcement Agencies (LEAs) and Police Forces (PFs) the fight against this phenomenon. Future works will focus on: (i) improving the performance of the current algorithm by defining a more efficient heuristic related to the Text Segregation step, in term of computational time. The current version generates all possible combination of symbols that works fine as long as the input is \"reasonably contained\". A smarter way to segregate the text needs be investigated to make it work also in larger context; (i) extending and experimenting the proposed method for supporting the detection of hidden propaganda by considering other types of mixed-code.  
Software, G.M.; Supervision, M.M.; Research, Writing--original draft, A.T. The authors contributed equally in all parts of the article in terms of literature review, adopted methodology, feature identification, model definition, experimentation and results analysis. All authors have read and agreed to the published version of the manuscript.  
This work was partially performed in the context of the CHAMPIONs research project, which receives funding from the European Union's Internal Security Fund--Police, grant agreement no. 823705.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors want to thanks [PERSON], Project Manager at Valencia Local Police (Spain), for supporting this research activity.  
The authors declare no conflicts of interest.