# An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet +
## 2 Related Work
### Mixed Code Analysis
In particular, several machine learning techniques, which are able to characterise the text by determining the polarity of a text have been used. The mixed code text used to train the model was initially identified to mark the words as Hindi or English. Such model used an Artificial Neural Network (ANN) to identify the affinity of the text and to mark it with values from \"very positive\" to \"very negative\" (\(-5\) to \(+5\)) so as to provide a sentimental score. The results showed circa 80% accuracy. However, regarding the meaning of polarity, it is not clearly specified whether positivity means happiness or joy and negativity anger or sadness. Another research contribution related to code-mixed text centred on sentiment analysis is described in [30]. It is based on the combination of two models: N-gram probabilistic model, which was called Multinomial Naive Bayes (MNB), and LSTM model centred on Tri-gram characters. In particular, the LSTM model has been used to cache deep sequential patterns contained in the text, whereas the low-level word combination of keywords has been faced with the MNB model, in order to compensate grammatical inconsistencies. The overall achieved accuracy was equal to 70.8%. Whereas in [31], the authors have extended the NLP techniques to detect humoir in a Hindi-English mixed code environment. N-gram, Bag-of-Words (BoW), Common words and hash-tags have been used and experimented on a manually annotated dataset. SVM, Random Forest and Naive Bayes have been experimented with a highest accuracy of 69.3%. In [32], instead, another NLP technique to normalise words in a code-mixed environment--as the spelling tends to vary for different persons--has been proposed. A Skip-gram model, which clusters the words of variation to deliver the base word as output in the normalised text, has been used. The results have shown an accuracy level of 71% and a F1-score equals to 66%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Objective** & **Approach and Contribution** & **Evaluation** & **Dataset** \\ \hline
[24] & Mixed-code translation & Unspecified & Accuracy: 90\% & Custom \\ \hline
[25] & Dataset Hate speech in Mixed code & Textual-based features, SVM & Accuracy: 71.7\% & Custom \\ \hline
[26] & Language detection & Data collection & Unspecified & Custom \\ \hline
[27] & Tweets classification & Data collection, CNN & Accuracy: 83.9\% & Custom \\ \hline
[28] & Hate Speech identification & SVM, LSTM, RF & Accuracy: 70.7\% & Custom \\ \hline
[29] & Sentiment analysis & ANN & Accuracy: 80\% & Custom \\ \hline
[30] & Sentiment analysis & N-Gram, MNB, LSTM & Accuracy: 70.8\% & Custom \\ \hline
[31] & Sentiment analysis & N-Gram, BoW, SVM, RF,NB & Accuracy: 69.3\% & Custom \\ \hline
[32] & Text normalisation & Skip-gram model & Accuracy: 71\% & Custom \\  & & F1-Score: 66\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mixed code related works. A clear aspect that emerged from the above identified related works is that such previous research studies have focused on mixed-code analysis by mainly dealing with the language translation. Such research direction differs from the purpose of this current work, as it focuses on the detection of hidden propaganda in mixed code as _leet text_[33], that is a particular form of expression, which is primarily used on the Internet. The distinguishing characteristic of such textual expression form lies in the combination of characters and symbols of the computer keyboard in order to graphically represent a text. This writing style makes the _leet text_ a powerful way of communicating and spreading propaganda, as it allows for easy bypass of its automatic identification.