# An Algorithm for the Detection of Hidden Propaganda in Mixed-Code Text over the Internet +
## 2 Related Work
### Mixed Code Analysis  
A contribution centred on machine translation of the so called _Hinglish_ text is proposed in [24], where Hinglish represents a communication way that uses a mix of Hindi and English terms within a conversation. The authors proposed a system centred on machine learning in order to translate Hinglish script. They discussed how the constraints of mixed code should be considered when defining a model in order to obtain an accurate interpretation of the mixed-code. They claimed that acceptable results were achieved in more than 90% of cases; however, any metrics and the quantitative results related to the evaluation approach have not been presented. Furthermore, the application context in which such experimentation was conducted has not been clearly specified. In [25], the hate speech detection problem in code-mixed texts on social media has been faced, by exploiting a Hindi-English code-mixed dataset consisting of online posts on Twitter. Different types of textual-based features have been used to define a detection model: n-grams, word n-grams, punctuations, negation words and hate lexicon. The annotation consisted of two Factor-World Level and Hate/Normal speech, which were used to indicate which words of the text were English and which words were Hindi. N-gram was adopted to support the feature extraction (both at character and word level) in order to detect relevant words. Furthermore, punctuation and negative words were considered as a benchmark to determine potential hate speech or not. The experiment showed that the best result was achieved by combining all such features to train a SVM, thus achieving 71.7% accuracy. In [26], an approach for language identification on social media has been elaborated. A dataset with Facebook posts and comments that contains code mixing between Bengali, English and Hindi has been built. The authors considered a text as mixed-code not only when the script was different but also either when the words of different languages were used randomly or sentences switch languages within a single text or paragraph. Different analysis techniques have been experimented, some based on a unsupervised dictionary approach and others on supervised word-level classification. However, no clear evaluation has been provided. Instead, the work proposed in [27] focused on the classification of offensive tweets written in Hinglish language. A Twitter dataset containing tweets in Hindi-English code switched language has been collected. The dataset has been organised into three main classes: nonoffensive, abusive and hate-speech. Convolutional Neural Networks (CNNs) combined with transfer learning have been used in the classification process by reaching 83.9% accuracy. In [28], the identification of hate speech from code-mixed text on social media has been faced, by using a Hindi-English mixed code corpus as the initial baseline. A Long ShortTerm Memory (LSTM) technique, based on subword level, has been mainly considered. The idea behind the subword level selection is to identify the root words and to match their variations. This is because in a mixed code setting the variation may differ for each writer. The hierarchical LSTM model as well as Random Forest (RF) and Support Vector Machine (SVM) have been experimented by using subwords phonetic. As Hindi is a phonetically accurate for writing, a phonetic input of the text has been also provided. The words were divided into consonant-vowel sequences and these sequences form the base of their attention model. This enabled the model to determine both the words that make up the vocabulary and to match the phonetic to such words as well. The results showed that the hierarchical LSTM reached the highest Recall and F1-score, whereas the SVM reached the highest accuracy, equal to 70.7%. In [29], different analysis techniques for supporting sentiment analysis of textual information, after having normalised the influence of multiple languages in mixed-code script, have been experimented.