Hate Speech Detection in Conventional Language on Social Media by using Machine Learning

DOI : 10.17577/IJERTV11IS060348

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Hate Speech Detection in Conventional Language on Social Media by using Machine Learning

Resmi Reghunathan

PG student, VLSI & Embedded Systems, ECE Department

TKM Institute of Technology, Karuvelil P.O, Kollam, Kerala-691505, India

Asha A S Assistant Professor, ECE Department

TKM Institute of Technology, Karuvelil P.O, Kollam, Kerala-691505, India

Abstract- Social media networks (SMNs) are the fastest means of communication as messages are sent and received almost instantaneously. The rise of social media platforms has signicantly changed the way our world communicates, and part of those changes includes a rise in inappropriate behaviors, such as the use of aggressive and hateful language online. Giving opinions which are harsh or rude to someone directly on face is a difficult task. People feel it is safe over internet to abuse or post something offensive to others. Hate Speech can hurt a person or a community. Detecting such content is crucial to ltering or blocking inappropriate content on the Web. It is time-consuming and difcult to manually process and classify massive quantities of text data. However, due to the huge amount of data posted every day, automatic methods are essential for identifying such type of content. I am using three machine learning models such as SVM, Logistic Regression, Random Forest. Because of the hasty growth of social media such as blogs and social net-working sites, where individuals put in freely their perspectives on different themes. Researchers prove that people find it comfortable to be opinionated in their mother tongue, be it verbal or written. So, I am developing an automatic hate speech detection technique on English by collecting a huge amount of data set from online platforms. After checking the automatic hate speech algorithm then go for Malayalam (conventional language) and compare the performance of the models with the help of a confusion matrix.

Keywords: Support Vector Machine (SVM), Social media networks (SMNs)

  1. INTRODUCTION

    Social media networks (SMNs) are the fastest approach of communique as messages are sent and obtained nearly straight away. SMNs are the primary media for perpetrating hate speeches these days. In line with this, cyber-hate crime has grown signicantly in the previous couple of years. More research is being conducted to cut down on the rising cases of hate speeches in social media (SM). Different calls had been made to SM companies to lter every comment before allowing it into the public domain . The impacts of hate crimes are already overwhelming due to sizable adoption of SM and the anonymity enjoyed via the online users . In this period of huge information, it is time- consuming and difcult to manually process and classify huge quantities of textual content records. Besides, the precision of the categorization of manual text can without difficulty be inuenced by human elements, inclusive of exhaustion and competence. To attain extra accurate and much less subjective results, it's miles benecial to apply

    machine learning (ML) techniques to automate the textual content classication procedures ..

    Social networks encourage the interactions between people to be more indirect and anonymous as a result presenting anonymity for some people making them feel more secure despite the fact that they express hate speech. It Can easily lead to disruptive anti-social outcomes if it remains unregulated and uncontrolled. Hate speech is therefore taken into consideration as a severe hassle internationally, and many countries and organizations resolutely resist it. The polarity detection of speech on structures is the rst step and is essential to government departments, social protection services, law enforcement and social media companies which expect to remove offensive content from their websites. Compared with guide ltering which is very time consuming, computerized identication of hate speech will enable the platform to hit upon the hate speech and cast off them a great deal greater quickly and efciently. The problem of on-line hate speech detection has raised a hobby in each the scientic community and the business world. There had been many studies efforts aimed toward automating the technique which is usually modeled as a supervised classication problem. Recently, device getting to know technique that may study the extraordinary institutions between pieces of textual content, and that a specific output is anticipated for a selected input by using pre-categorized examples as schooling statistics is popular in scientic studies for hate speech detection. Among various device learning methods, deep learning which is a subset of machine getting to know, could be very prominent in Natural Language Processing (NLP) to tackle the problem of textual content classication.

    In recent times a potential and intense research awareness because of the hasty growth of social media which include blogs and social internet working websites, in which individuals installed freely their perspectives on one of a kind topics. Researchers prove that people find it snug to opinionated in their mother tongue, be it verbal or written. Given that now almost all social systems help most of the famous languages, the requirement to mine the emotions in numerous dialects is at the rise. However, not all statistics can be applicable; some may not have any impact on the end result and a few may have comparable meanings. A preprocessing phase is for this reason required to help make the dataset concise. Malayalam just like the different languages in the Dravidian own family exhibits the traits of

    an agglutinative language. The preprocessing manner consists of cleansing the statistics, tokenization, stop word elimination, etc. .In India, in which there are 22 officially diagnosed languages and more than 132 crore human beings out of which a good sized percentage are active Internet customers, the amount of records generated on a daily foundation is big.

    MACHINE LEARNING

    Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model Based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system.

    SUPERVISED LEARNING

    Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labeled. Which means some data is already tagged with the correct answer. The supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data. Supervised learning is classified into two categories of algorithms:

    Classification-It is the process of finding or discovering a model or function which helps in separating the data into multiple categorical classes i.e. discrete values. In classification, data is categorized under different labels according to some parameters given in iput and then the labels are predicted for the data. The classification process deals with the problems where the data can be divided into binary or multiple discrete labels. Regression -It is the process of finding a model or function for distinguishing the data into continuous real values instead of using classes or discrete values. It can also identify the distribution movement depending on the historical data. Because a regression predictive model predicts a quantity, therefore, the skill of the model must be reported as an error in those predictions

    HATE SPEECH

    In antique instances, Hate Speech changed into restricted face to face conversations. But now because of the boom in social media systems the usage of hate speech is increasing. As human beings feel they're hidden on the net. Due to this, people feel secure to apply hate speech and it's human computive undertaking to identify hate speech on social

    media so we need some automatic strategies to come across hate speech. On the other hand, people are more likely to share their views online, thereby leading to the dissemination of hate speech. Given that this type of prejudiced contact can be particularly un favorable to society, policymakers and social networking sites may also profit from monitoring and prevention gadgets Hate speech is typically described as any touch that distorts a character or network on the basis of traits such as coloration, ethnicity, gender, sexual preference, nationality or faith. According to Paula Fortuna and Sergia Nunes Hate speech is language that assaults or diminishes, that incites violence or hate towards companies, primarily based on precise traits consisting of physical look, faith, descent, national or ethnic starting place, sexual orientation, gender identity or other. To penalize misclassification on minority classes weighted F1-rating is recommended as an evaluation measure. Nowadays with improvement in deep gaining knowledge of, CNN can be used for hate speech detection. Word-vector additionally acknowledged as phrase embedding may be educated on relevant corpus of the area. These pretrained phrase-vectors are used in CNN. Most gadget studying fashions makes use of bag-of phrases which fails to seize styles and sequences. Right here each phrase can be taken into consideration as hate speech but it's most possible that this sentence is hate speech. This sort of capabilities can't be treated with the aid of a bag of words which degrades the overall performance of traditional system learning algorithms.

    NATURAL LANGUAGE PROCESSING

    Natural Language Processing or NLP (also referred to as Computational Linguistics) may be defined as the automated processing of human languages. As NLP is a huge and multidisciplinary field, but relatively a new location, there are many definitions accessible and practiced by way of one- of-a-kind human beings. One definition that could be part of any informed characters definition is Natural Language Processing is a theoretically motivated range of computational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications.Thus, NLP is a discipline of computer technology and linguistics concerned with the interplay between computer systems and human (natural) languages. Moreover, it's miles pushed through advances in Machine Learning (ML) and also has an quintessential part of Artificial Intelligence (AI). The strategies of NLP are advanced in the sort of way that the instructions given in herbal language can be understood by the PC and additionally be capable of performing in step with it. It has to be mentioned that herbal language processing can be divided into two components, namely written and spoken language. Written languages play a less crucial position than speech in maximum activities, as the largest part of human linguistic communication occurs as speech. However, written language can be understood less complicated than spoken language, as spoken languages address a number of noise and ambiguities of the audio sign. Because there is a lot of ambiguity discovered in language,

    NLP is seen as a difficult problem in computer science. Research in herbal language processing has been going on since the overdue 1940s. Machine translation (MT) was one of the first laptop-based software related to herbal language. Cambria and White (2014) feedback that NLP research has evolved from the technology of punch cards and batch processing, wherein the evaluation of a sentence may want to absorb to 7 mins, to the generation of Google and the likes of it, wherein thousands and thousands of web pages can be processed in less than a 2d. The most explanatory technique for offering what honestly happens inside a Natural Language Processing gadget is by means of the levels of language approach. These ranges are utilized by humans to extract the meaning from text or spoken languages. This is because language processing particularly is predicated on formal models or representation of know-how related to those stages. Moreover, language processing packages distinguish themselves from fact processing systems with the aid of the usage of the expertise of language. The analysis of herbal language processing has the subsequent stages: Phonology, Morphology, Lexical, Syntactic, Semantic, Discourse and Pragmatic.

  2. RELATED WORK

    There are many approaches for detection of hate speech. But they differ from each other based on the output they obtained in Ref. 8 hate speech was classified into three classes race, nationality and religion. Ref. 8 uses sentiment analysis technique for detection of hate speech but just not detecting but they also classified into one of the three classes and also rate the polarity of speech. We found two survey papers for automatic hate speech detection [6],[14]. In Ref. 6 motivation for hate speech detection is shown and why it became necessary to develop more robust and accurate models for automatic hate speech detection. The problem of hate speech detection is more often researcher keep data private while collecting it and there are less open source code available which make it difficult for comparative study [6] . This degrades the progress in this field. Different features related to hate speech are described in Ref. 14, like simple surface feature which includes bag of words, unigrams or n-grams. Both training set and testing set need to have same predictive word but it is problem as detection of hate speech is applied on very small piece of text so to overcome this issue word generalization is applied [14]. Knowledge of annotator for hate speech was examined in Ref. 15. Authors produce some very good results in amateur annotation in comparison to expert annotations. Also, Waseem provide its own dataset and its evaluation. To penalize misclassification on minority classes weighted F1- score is suggested as an evaluation measure. Nowadays with development in deep learning, CNN can be used for hate speech detection [2],[1]. Word-vector also known as word embedding can be trained on relevant corpus of the domain. This pre trained word-vectors are used in CNN [2] . Most of machine learning models uses bag-of words which fails to capture patterns and sequences. It can be understood by the example in Ref. 2. if a tweet ends saying'' if you know what I mean here each word can be considered as hate speech but it is most likely that this sentence is hate speech. This type

    of features cannot be handled by a bag of words which degrades the performance of traditional machine learning algorithms.

  3. METHODOLOGY

    Methodology explains the proposed gadget which has been hired to classify speech into two special classes particularly, hate speech, clean speech. Fig. 1 suggests the complete studies method. As proven on this figure, the studies method is contained in six key steps particularly, Data set collection, preprocessing, Feaure extraction, Model Training, Perform evaluation, and Model testing..

    Figure1: Block diagram

    DATA SET COLLECTION

    Most machine learning algorithms require data to be formatted in a very specific way, so datasets generally require some amount of preparation before they can yield useful insights. Some datasets have values that are missing, invalid, or otherwise difficult for an algorithm to process. If data is missing, the algorithm cant use it. If data is invalid, the algorithm produces less accurate or even misleading outcomes. Some datasets are relatively clean but need to be shaped (e.g., aggregated or pivoted) and many datasets are just lacking useful business context (e.g., poorly defined ID values), hence the need for feature enrichment. Good data preparation produces clean and well-curated data which leads to more practical, accurate model outcomes. Mainly using two types of data set 1) English 2) Malayalam , the English data set can be downloaded from online social media platforms like GitHub. The Malayalam data set is created. The data set mainly consists of two classes a) Class0

    b) Class1 where class0 indicates hate speech and class 1 indicates clean speech. In this 30% of data were used for testing purposes and the remaining 70% used for training purposes. Following images showing some examples of data sets. For improving the accuracy data balancing operations are to be done.

    Fig2: English data set sample

    Fig3: Malayalam data set sample

    PRE-PROCESSING

    The process of converting data to something a computer can understand is referred to as pre-processing. One of the major forms of pre-processing is to filter out useless data. Stop Words: A stop word is a commonly used word (such as the, a, an, in) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query. In the dataset, carried out different preprocessing- techniques to clear out noisy and non-informative functions. In preprocessing, change the given data into a lower case. Also, removed all the URLs, usernames, white areas, hashtags, punctuations and stop-words using sample matching strategies from the collected speech. Besides this, also accomplished tokenization and stemming from preprocessed speech. The tokenization, converts every single speech into tokens or words, then the porter stemmer converts phrases to their root forms, such as angry to offend using porter stemmer. Various methods for machine learning models are used to achieve higher evaluation measures. Methods Used are explained below.

    TOKENIZING

    Tokenizing is the process in which each sentence is divided into words. It can be done to either separate words or sentences. If the text is split into words using some separation technique it is called word tokenization and the same separation done for sentences is called sentence tokenization. It is breaking the raw text into small chunks. Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words. For example, the text It is raining can be tokenized into It, is, raining. This method is used to create vocabulary for a dataset. This vocabulary is used to represent each speech in the dataset representation based on a choice of method like TF-IDF or bag of words.

    STOP WORDS

    Stop words are those words which have less meaning or are useless for e.g, a,the,of which often occurs in most sentences. We would not want these words to take up space in our database, or take up valuable processing time. So it is required to remove these stop words otherwise it will cause misclassification. For this, we can remove them easily, by storing a list of words that you consider to stop words. NLTK(Natural Language Toolkit) in python has a list of stop words stored in 16 different languages.

    STEMMING

    Stemming is the process of producing morphological variants of a root/base word. It removes the prefix or suffix of a word. Stemming programs are commonly referred to as stemming algorithms or stemmers. A stemming algorithm reduces the words chocolates, chocolatey, choco to the root word, chocolate and retrieval, retrieved, retrieves reduce to the stem retrieve. Stemming is an important part of the pipelining process in Natural language processing. The input to the stemmer is tokenized words.

    LEMMATIZATION

    It is the process of grouping together the different inflected forms of a word so they can be analyzed as a single item. It usually refers to doing things properly with the use of a vocabulary and morphological analysis of words. It normally aims to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma , which is a root word rather than root stem, the output of stemming. After lemmatization I got a valid word that means the same thing. Basically it is similar to stemming but it brings context to the words. So it links words with similar meanings to one word. Text preprocessing includes both Stemming as well as Lemmatization. Many times people find these two terms confusing. Some treat these two as the same. Actually, lemmatization is preferred over Stemming because lemmatization does morphological analysis of the words. Applications of lemmatization are: Used in comprehensive retrieval systems like search engines, used in compact indexing.

    FEATURE EXTRACTION

    The ML algorithms cannot understand the classification rules from the raw text. These algorithms need numerical features to understand classification rules. Hence, in text classification one of the key steps is feature engineering. This step is used for extracting the key features from raw text and representing the extracted features in numerical form. In this I have performed TFIDF technique.

    MODEL TRAINING

    According to the no free lunch theorem, there is no single classifier which best performs on all kinds of datasets. Therefore, it is recommended to apply several different classifiers on a master feature vector to observe which one achieves better results. Here use three different machine learning models: Support Vector machine (SVM), Logistic regression (LR), Random forest (RF).

    SUPPORT VECTOR MACHINE (SVM)

    Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. Support vector machine is extremely favored by many as it produces notable correctness with less computation power .The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put

    the new data point in the correct category in the future. This best decision boundary is called a hyperplane. In two- dimensional space, this hyperplane is a line splitting a plane into two parts where each class lies on either side.

    Fig4: SVM

    LOGISTIC REGRESSION (LR)

    Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used for predicting the categorical dependent variable using a given set of independent variables. It predicts the output of a categorical dependent variable. Therefore the outcome must be a categorical or discrete value. It can be either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact value as 0 and 1, it gives the probabilistic values which lie between 0 and 1.

    Fig5: Sigmoid function

    It is the probability of the datapoint belonging to any class. Then cutoff point (mostly 0.5) is used to divide between the two classes. Cutoff point is not fixed; it can be changed according to the dataset.

    RANDOM FOREST (RF)

    A series of trees is reerred to as forest. Similarly, Random forest is a collection of decision trees and it's far called random due to the fact it's far from a group of tremendously uncorrelated bushes working as a single version. Random forest is a supervised learning algorithm which is used for both classification as well as regression. But it is mainly used for classification problems. As we know that a forest is made up of trees and more trees means more robust forest. Similarly, a random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by means of voting. It is an ensemble method which is better than a single decision tree because it reduces the over-fitting by averaging the result

    PERFORMANCE EVALUATION

    In this step, the constructed classifier predicts the elegance of unlabeled textual content (i.e. hate speech, clean speech) using a test set. The classifier performance is evaluated with the aid of calculating true negatives (TN), false positives (FP), false negatives (FN) and true positives (TP). These four numbers constitute a confusion matrix. Different performance metrics are used to assess the overall performance of the built classifier. Some not unusual overall performance measures in textual content categorization are mentioned briefly underneath.

    Precision: Precision is also known as the positive predicted value. It is the proportion of predictive positives which are actually positive. Or it can be defined as the number of correct outputs provided by the model or out of all positive classes that have predicted correctly by the model, how many of them were actually true.

    =/(+) (4)

    Recall: It is the proportion of actual positives which are

    predicted positive.

    () = 1

    1+_

    (1)

    =/(+) (5)

    F-Measure: It is the harmonic mean of precision and recall

    .The standard F-measure gives equal importance to

    =_+_ _(^+)+_ "." _ (2)

    is the random value it may be 1._(1^ ) _2,_ will

    be the each TF-IDF value in row wise. The output value

    will be in range [0,1].Based on the range we can classify the speech.

    F(n)= 1/1 + (0 + 11+. . . . . . ) (3)

    precision and recall. If two models have low precision and

    high recall or vice versa, it is difficult to compare these models. So, for this purpose, we can use an F-score. This score helps us to evaluate the recall and precision at the same time. The F-score is maximum if the recall is equal to the precision

    =2(+)/(+ ) (6)

    Accuracy: It is one of the important parameters to determine

    the accuracy of the classification problems. It defines how

    often the model predicts the correct output. It can be calculated as the ratio of the number of correct predictions made by the classifier to all number of predictions made by the classifiers. It is the number of correctly classified instances (true positives and true negatives).

    =(+)/(+++) (7)

    TP is True Positive. FP is False Positive. This refers to non-

    hate speeches that were classied as hate speech. FN is False Negative .This refers to those hate speeches that were not identied by the model as hate speech. It identified as non hate speech.TN is True Negative

  4. RESULT AND DISCUSSION

    Most machine learning algorithms require data to be formatted in a very specific way, so datasets generally require some amount of preparation before they can yield useful insights. Good data preparation produces clean and well-curated data which leads to more practical, accurate model outcomes. Mainly using two types of data set 1) English 2) Malayalam , the English data set downloaded from online social media platforms like GitHub. The Malayalam data set is created. In this 30% of data were used for testing purposes and the remaining 70% used for training purposes. Performed the preprocessing operations for the data. The ML algorithms cannot understand the classification rules from the raw text. These algorithms need numerical features to understand classification rules . Hence, in text classification one of the key steps is feature engineering. This step is used for extracting the key features from raw text and representing the extracted features in numerical form. In this I have performed TFIDF technique. SVM, logistic regression and random forest are different machine learning algorithms that are used. Confusion matrix helps to find the performance evaluation parameters of different machine learning models. A confusion matrix is a matrix used to determine the performance of the classification models for a given set of test data . The order of the matrix depends upon the number of classes. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa . It compares the actual class of data and the predicted output of each model of the same data Confusion matrix of each model is shown below.

    Fig 6: Confusion matrix of SVM for English dataset

    Fig 7: Confusion matrix of random forest for English dataset

    Fig 8 : Confusion matrix of Logistic regression for English dataset

    Fig 9 : Confusion matrix of Random forest for Malayalam dataset

    Fig 10 : Confusion matrix of Logistic regression for Malayalam dataset

    Fig 11 : Confusion matrix of SVM for Malayalam dataset

    By analyzing the confusion matrix obtained the performance evaluation characters of different model

    :

    Table4.1: Different models result

  5. CONCLUSION

In routine life, as the usage of social media is increased everyone seems to think like they can speak or write anything they want. Due to this thinking, hate speech has increased. Hate speech can hurt a person or a community. But it is very difficult to identify the hate speech manually. So it becomes necessary to automate the process of classifying the hate speech data. To simplify the process of classifying hate speech I have used a machine learning approach to detect hate speech from the speech. Most machine learning algorithms require data to be formatted in a very specific way, so datasets generally require some amount of preparation before they can yield useful insights. Good data preparation produces clean and well-curated data which leads to more practical, accurate model outcomes. Mainly using two types of data set 1)English 2) Malayalam, the English data set downloaded from online social media platforms like GitHub. The Malayalam data set is created. In this 30% of data were used for testing purposes and the remaining 70% used for training purposes. The ML algorithms cannot understand the classification rules from the raw text. These algorithms need numerical features to understand classification rules .For this use TF-IDF and bag of words methods to extract features from the speech. To classify hate speech implemented machine learning algorithms like SVM, Logistic Regression and Random

Forest. After preprocessing with TF-IDF calculated the performance evaluation characters of different machine learning models such as SVM, Logistic Regression, Random Forest with the help of confusion matrix.. Found that SVM gives best performance with 90% Accuracy Score for English dataset and 94% accuracy for Malayalam dataset. Developed automatic hate speech detection techniques in English, Malayalam.

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