# An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet +
## 2 Related Work
### Online Propaganda Detection  
Online propaganda is a modern way of conducting campaign centred on IT-based tool and in particular online social networking. Twitter represents one of the most used medium for propaganda and it is heavily utilised by extremist and terrorist groups to reach the mass. The recent interest in detecting online propaganda is documented by the different research efforts conducted in this field (see Table 2).  
In [34], for example, the authors focused on the detection of extremist ISIS groups, by proposing a method to actively identify tweets containing propaganda related content. The method aimed to recognise not only patterns containing specific Hashtags but also user accounts by profiling them so as to enable their potential predictions. The method was centred on the Term Frequency (TF) of the suspected words in order to derive relevant information, whereas a Regression-based Neural Network (RBNN) has been used as classification algorithm, without quantifying the achievements.  
Similarly, the detection of radical content was faced in [35], through the definition of a model. It was used to analyse the text produced by the users from the psychological perspective by considering specific behavioural patterns. In particular, the authors utilised TF-IDF to identify suspicious terms related to radical behaviour, which were used to train a Random Forest (RF), a Support Vector Machine (SVM) and a K-Nearest Neighbor (KNN) classifier by obtaining 94% accuracy, as the highest result.  
A deep learning model for supporting the identification of Sunni extremist propaganda via text analysis is presented in [36]. Here, word2 vec and doc2 vec techniques have been adopted to detect specific relationships among extremist-related terms which in turn were used to identify and classify new text as propaganda or not. Among the conducted tests, Artificial Neural Networks (ANNs) showed the best classification results by providing accuracy and precision of about 90%.  
In [37], the authors focused on the creation of a free available dataset as a benchmark to support the propaganda detection research activities, that has been used in different research competitions related to propaganda detection. It contains sentences that are annotated from experts as propaganda and not. In addition, the paper presents preliminary classification results by achieving about 63% of accuracy by using the Neural Network classification. Using the same database, in [38] the authors exploited the BERT classification to identify single nonpropaganda rows by employing ELMo, BERT and RoBERTa approach, by achieving 79% recall, 66% precision and 60% f1-score.  
Whereas in [39], the authors focused on the assessment of radical and extremist online propaganda, by proposing a pyramidal conceptual model that enables to distinguish propaganda related content at different level of radicalisation. The model is centred on three sociological human aspects which characterise traits of radicals and terrorists. A preliminary experimentation was carried out in order to illustrate how to allocate propaganda items to the pyramid model.  
From the above reviewed papers, it resulted that online propaganda detection is an active research field towards analysis of extremist related content. However, it emerged that all previous research effort are limited on the analysis of natural language of a plain-text. Our scope is instead to deal with a form of expression which is not directly attributable to plain-text but which is made up to \"hide\" the real message, by composing different symbols to graphically resemble the standard alphabet.