SARNET: SARCASM VS TRUE-HATE DETECTION NETWORK
Authors: Harsh Mittal
Kartikeya Singh Chauhan
Anil Singh Parihar
Kavinder Singh
Ashutosh Pandey
Venue: Submitted to ICLR 2023 (2023)
Keywords: Game Theory
Hate Speech
Sarcasm
Nash Equilibrium
Prisoner's Dilemma

Abstract: At times hate speech detection classifiers miss the context of a sentence and flag a sarcastic tweet incorrectly. To tackle this problem by emphasising on the context of a tweet we propose SarNet. SarNet is a two-fold deep learning based model which follows a quasi-ternary labelling strategy and contextually classifies a tweet as hate, sarcastic or neither. The first module of SarNet is an ANN-BiLSTM based Pyramid Network used to calculate the hate and sarcastic probabilities of a sentence. The second module of the SarNet is the Nash Equalizer which stems from the concept of game theory and prisoner’s dilemma. It treats hate and sarcasm as two prisoners. A payoff matrix is constructed to calculate the true hate of the tweet. True hate considers the hate part of a tweet excluding the sarcastic part of the tweet. Thus, this gives a true estimate of the hate content in a tweet thereby decreasing the number of sarcastic tweets being falsely flagged as hate. Our proposed model is trained on state-of-the-art hate speech and sarcasm datasets in the English language. The precision, recall and F1 score of our proposed model is 0.93, 0.84 and 0.88 respectively. Comparison with state-of-the-art architectures demonstrated better performance of SarNet by a significant margin.

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SARNET: SARCASM VS TRUE-HATE DETECTION NET- WORK
Anonymous authors Paper under double-blind review
ABSTRACT
At times hate speech detection classifiers miss the context of sentence and flag a sarcastic tweet incorrectly. To tackle this problem by emphasising on the con- text of tweet we propose SarNet. SarNet is two-fold deep learning based model which follows quasi-ternary labelling strategy and contextually classifies tweet as hate, sarcastic or neither. The first module of SarNet an ANN-BiLSTM, based Pyramid Network used to calculate the hate and sarcastic probabilities of sen- tence. The second module of the SarNet is the Nash Equalizer which stems from the concept of game theory and prisoner's dilemma. It treats hate and sarcasm as two prisoners. A payoff matrix constructed to calculate the true hate of the tweet. True hate considers the hate part of tweet excluding the sarcastic part of the tweet. Thus, this gives true estimate of the hate content a tweet thereby decreasing the number of sarcastic tweets being falsely flagged as hate. Our pro- posed model is trained on state-of-the-art hate speech and sarcasm datasets in the English language. The precision, recall and F1 score of our proposed model is 0.93, 0.84 and 0.88 respectively. Comparison with state-of-the-ar architectures demonstrated better performance of SarNet by significant margin.
INTRODUCTION
Social media has the potential to influence the opinion of the masses. People from all around the world interact and share their perspectives. This has facilitated the rapid exchange of ideas. Un- fortunately, these social media platforms are being used to disseminate hate speech. Hate speech described as aggressive or threatening speech that shows prejudice based on ethnicity, religion, sexual orientation or other factors. This raises the need deploy robust and efficient classifiers to regulate online content.
Citizens social media regularly use sarcasm to convey their emotions in conversation. Sarcasm an effective means of expressing thoughts indirectly that is not easy to notice. is described as an incidental technique of expressing viewpoint in which the written word does not reflect the intended meaning. Most of the hate speech detection algorithms (1 face difficulty in distinguish- ing sarcastic statements. This is because sarcasm reverses the polarity of seemingly positive or negative phrase. This attribute of sarcasm leads hate detection classifiers to falsely flag sentences as hate. Thus, is crucial to reduce the false positives by considering the sarcastic context of sentence while predicting hate.
The labelled datasets suffer from annotators' bias and/or the unavailability of datasets annotated as both hate and sarcasm (5). The above limitations of datasets make it very difficult for deep learning models predict the actual hate content of tweet with high precision. Our proposed SarNet model overcomes these problems by employing semi-supervised learning approach to predict the degree of hate in a sentence that we define as true-hate.
Researchers have presented multiple approaches for tackling hate speech in the past decade. Kapil and Ekbal (14) proposed a deep neural network-based multi-task learning approach for hate speech detection. Corazza et al. (4) proposed neural network classifier for multilingual online hate speech detection in three languages and investigated the impact of each feature on the respective outcomes. While it is critical to identify hate social media promptly, it is equally important to avoid false positives. Arango et al. (5) point to methodological flaws as well as significant dataset biases while tackling hate speech. (5) stated that many state-of-the-art performance claims had become vastly

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overstated and suggested more realistic view of the state-of-the-ie methodologies. Meriem et al. (6) presented a fuzzy sareasm detection method that uses social knowledge and multiplies it by a degree of relevance. Kumar al. (7) presented multi-head attention-ben based BiLSTM network for detecting sarcastic remarks in a corpus. The aforementioned works do not consider the sarcastic and hate context in the same tweet while prediction.
Most of the methods 8 10) fail to consider the sarcastic context of the sentence while detecting hate speech, which leads misclassification of sarcastic sentence hate. Thus, propose the SarNet model to tackle the misclassification problem in hate speech detection by considering the sarcastic context of the sentence.
This paper presents SarNet, two-fold learning method wherein we employ the concept of multiple games based on game theory (Prisoners' Dilemma) and Nash equilibrium. SarNet detects true-hate by considering the sarcastic context of the sentence and gives a pooled output. Hate and sarcasm are a perspective-dependers sentiment. Our proposed SarNet model as shown in Figure analyzes the contextual information of sentence. The major contributions of this work are summarized as follows:
Introduce a novel model, SarNet, by integrating the ANN, LSTM network for calculating probabilities Nash Equalizer which plays games to identify the label of the sentence, using a quasi-ternary labeling technique..
Perform comparative analysis of SarNet to establish its supremacy.
Investigate the impact of different hyper-parameterrs values the efficacy of SarNet, and the effectiveness of the Nash Equalizer.
The paper is structured as follows: Section is a review of existing works. Section III describes Sar- Net at intuitive level and presents the experimental and hyperparameter settings and results, com- pares them with the current state-of-the thet--rrrr- and discusses the model. Section IV draws a conclusion our research work and Section V presents the limitations of our work, giving future directions.
2 RELATED WORK
This section summarizes existing research in hate speech detection and sarcasm detection in social media. The current strategy is split into four; namely, machine learning based approaches, deep learning based approaches, sarcasm detection and hate detection. We discuss all the sub-categories the following subsections.
2.1 MACHINE LEARNING BASED APPROACHES
Dave and Desai (II. attempt to identify various supervised classification techniques like SVM, Naïve Bayes using basic Bag-of-words features with TF-IDF as the feature's frequency measure. The researchers studied the outcomes of these classification approaches using textual data accessible in multiple languages on review sites, social networking sites, and microblogging sites, as well as the data set construction and feature selection processes. A pilot experiment is carried out by the researchers to detect sarcastic statements in "Hindi."
Koushik al. (12) propose machine learning technique for automatically detecting hate tweets utilising a bag of words and the TFIDF approach on an cxisting Twitter data set. The researchers utilised logistic regression classifier for binary categorisation of the tweets. The researchers ob- tained 94.1 per cent accuracy using bag of words feature, and 94.62 per cent accuracy using the TFIDF feature.
2.2 DEEP LEARNING BASED APPROACUESS
Patro et al. 13) present a deep learning framework for detecting sarcasm targets in pre-defined sarcastic messages by conducting empirical research of socio-linguistic variables and identify those that are statistically significant in predicting sarcastic targets. Finally, they provide a deep learning L.TM based architecture enhanced with sociolinguistic features dataset released by Joshi al.
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Kapil and Ekbal (4) propose a deep multi-task learning framework to harness beneficial information from numerous related categorization tasks to improve the performance of the individual task. The multi-task model is based on shared-prie shared-private architecture in which shared and private layers are assigned to capture shared and task-specific characteristics. Experiments on five benchmark binary datasets suggest multi-task model performs better than the single-tasking framework and indicate that separate data sets classified in different sub-classes do indeed aid each other in categorization.
An automated system is proposed by Roy et al. (15) using the Deep Convolutional Neural Network (DCNN) which utilises the tweet text in English language with Ge embedding vector to capture the tweets' semantics with the help of convolution operation. Besides this the researchers also ex- perimented on state of the art machine learning classifiers. The researchers claim that they achieved the precision, recall and Fi-score values as 0.97, 0.88, and 0.92 respectively on the DCNN network.
Plaza-del-Arco et al. (16) address the task of identifying Spanish hate speech on social media. They specifically compare Deep Learning methods to recently pre-trained language models based on Transfer Learning as well classic machine learning models. The researchers claim that their findings produced with the TL models surpass the other ML models. They evaluate the performance of two multilingual pre-trained models (mBERT and XLM) that are currently accessible with monolingual model (BETO) that has been particularly trained on Spanish. The results demonstrate that the monolingual pre-trained LM (BETO) outperformed mBERT and XLM.
2.3 SARCASM DETECTION
Researchers are continuously working on detecting sarcasm in sentences. II). developed a method to identify sarcasm using various supervised classification techniques like SVM, Näive Bayes using simple BoW features and TF-IDF as the feature's measure of frequency. The researchers examined these classification techniques' results on textual data available in various languages on various websites along with the analysis of dataset generation and feature extraction process.
Researchers have used deep learning-based classifiers for the detection of sarcasm. Patro et al. (3) provide a deep learning framework for recognising sarcasm targets in predefined sarcastic messages by conducting empirical study socio-linguisee characteristies that are statistically important in predicting sarcastic targets. Husain et al. (17) present a methodology based on the hypothesis that tweets with negative sentiment and sarcastic content more likely contain offensive con- tent; thus, fine-tuning the classification model using a large corpus of offensive language which aids the model's 's learning process for detecting sentiment and sarcastic content, and investigate transfer learning in the contexts of foul language, sarcasm detection, and sentiment analysis. Using the math- ematical formalisms of quantum theory and fuzzy logic, Zhang el al. (18) suggest complex-valued fuzzy network. Verma et al. (19) focus on new research on sarcasm detection, including several strategies and problems based on deep hybrid learning. Nayel et al. (20) propose an SVM-based classifier for sarcasm detection in Arabic language. The researchers perform model comparison with other state-of-the-an thettrr techniques.
2.4 HATE DETECTION
The NLP community has constantly been working on the problem of hate speech classification. Pitsilis et al. (2I) describe hate speech detection strategy that integrates several user-related char- acteristics, such as the users' tendency for racism or sexism, with an ensemble of RNN classifiers. Pachalides el al. (22) proposed MANDOLA. This big-data processing system uses big-data method- ologies to monitor, detect, display, and report the expansion and penetration of online hate-related discourse. Teh et al. (I) looked into how profanity was used on Twitter by people of different groups to mark how successful it was in detecting hate speech. To rationalize the differences in profanity usage across the three nations, statistical hypothesis tests are conducted, and Bayes theorem-based probability estimation process is developed to quantify the efficacy of blasphemy-based detection systems for hate speech. Senarath et al. (23) claim that semantic information can aid machine learn- ing algorithms in augmenting the word senses that are represented in context to social media posts and present a new empirical study of various semantic variables such as word embedding represen- tation for distributional semantics, corpus- based semantics and declarative knowledge patterns for hate speech categorization tasks on social media posts. Mozafari et al. (24) presented a transfer learning strategy based BERT by evaluating the potential of BERT to capture hateful context
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inside social media content using various fine-tuning approaches. Caselli al. (25) proposed Hate- BERT, a re- re-trained BERT model trained on publicly available Reddit comments data set for hate language detection in English.
3 METHODOLOGY
SarNet is proposed for true-hate detection which includes data pre-processing, and an explanation of our proposed two-fold deep learning based method.
3.1 DATA PRE- PRE-PROCESSING
Useless information is filtered by pre-processing the datasets. In the pre- pre-processing, SarNet per- forms the following steps: The tweets converted into lower and the words were split for removal of stop words using the NLTK library. Using regex white spaces, URLs, hashtags, twitter handles, emoticons, dots, punctuation marks and non-ASCII characters etc. were removed. The words were then stemmed using Snow Ball Stemmer.
3.2 PROPOSED SARNET MODEL
O
P(H
POH (1S
BAYES
()S)
((H
THEOREST
P(H' 0 5)
P(H" S'S')
PRISONERS' DILEMMA
O
QUASITERNARY LABELLING
Figure 1: Detailed Architecture of SarNet
Figure shows the SarNet model. SarNet is two fold deep learning model. is used for quasi ternary labeling of tweets as hate, sarcasm or neither. The SarNet model is built using two parallel Pyramid Networks. They are trained on a hate speech dataset and sarcasm dataset respectively. These parallel pipelines converge into the Nash Equalizer.
The Pyramid Network of model built using an input layer followed by an embedding layer which passes the data ANN layers which consist of regular dropouts. This helps in faster feature extraction. These layers are integrated with stacked LSTM layers followed by a sigmoid activation which helps in predicting the probability of given tweet. These probabilities then fed into the Nash Equalizer for the final labelling. The model is discussed at an intuitive level in the following subsections.
The pre- prepesceeeeed tweet is passed to the input layer based on the token generated by indexing the dictionary values. As result, the input layer transforms the incoming text into vector.
The embedding layer semantically correlates the relevant terms with equivalent vector representa- tions. We pre-trained (GloVe) (26) embedding for word representation.

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3.2.1 PYRAMID NETWORK
The Pyramid Network exploits successive ANN layers to extract features from the pre-pre pee-processd input tweet vectors. We do not use CNN layers as the words in tweet do not have spatial correla- tion. There are multiple successive layers of ANN and dropout for downsizing the initial number of features extracted by the ANN layer. The dropout between successive layers is by factor of either 0.2 or 0.4 (refer Section 3.5 Hyperparameter Settings). Finally six most relevant features (caleu- lated empirically to produce best results) per tweet passed onto the LSTM layers for identifying sequential correlation between them. The ANN layers help in faster and accurate feature extraction which helps in reducing the training time significantly when compared to other feature extractors like RNN. Equation [ represents the nth feature sequence 2n. which is a summation of the dot product between the matrix of weights w" and input tweet row vector x and the bias 6. f(.) is ReLU (non linear activation function). d gives the factor of dropout which is either 0.2 or 0.4.
2n = d * f(w x + b); d = (0022
(1)
BiLSTM employs a memory cell that is made up of an input gate, a forget gate, an output gate, and a node that is linked to itself. The memory cell in a certain layer utilises the previous layer's hidden state at the current time and the current layer's 's hidden state from the prior time. The forget gate determines which information in the cell state should be discarded, while the input gate and tanh layer determine which information is stored in the cell state. The best features extracted from the ANN layers are passed onto multiple successive BiLSTM cells to map the selected features sequentially. BiLSTMs enable additional training by traversing the input data in the forward and the backward directions thus reinforcing the contextual information extracted. The extracted infor- mation passed through a sigmoid activation layer for calculating the probabilities of prediction which are then passed onto the Nash Equalizer. BiLSTM alone is not self sufficient to gather the context of hate and sarcasm of a given tweet. To overcome this ambiguity a payoff matrix using Nash Equilibrium is constructed which along with probabilistic terms quantify the tweet as hate or sarcasm.
3.2.2 NASH EQUALIZER
As discussed above, it is crucial to consider the context of the sentence, that is its intent and estimate degree of hate sarcasm of that sentence before classifying it. Many times it is difficult to determine whether a sentence is hate or not as its meaning cannot be understood at its face value. This is especially true when sarcasm is used, as the true intent of the sentence is hidden behind words. At its face value the sentence might seem to spur hate but in reality the intention behind might be the opposite. Thus to get an estimate of the hatefulness of a sentence taking into account the sarcastic part of the sentence we propose parameter true-hate. This proposed parameter gives a measure of the hate content of given tweet excluding the sarcastic part. It will greatly help in assessing the degree of hate content in a tweet and also in reducing false flagging of not hate tweets. Thus we propose our Nash Equalizer which predicts the degree of hate of tweet.
The pyramid network being trained on the hate speech dataset learns the hate content features of a sentence and the pyramid network being trained on the sarcasm dataset learns the sarcasm content features of a sentence. Thus, prior to testing, we acquire two pyramid networks that have learned two distinct semantics of a sentence, namely hate and sarcasm. Following the initial training of both pyramid networks, the test split ( refer Section 3.3 Datasets of the paper for detailed explanation of the test split) of the hate speech dataset is simultancously passed through both pipelines. Let's s assume tweet / labelled hate from the hate speech dataset's 's test split. It is simultaneously passed through both pipelines of the pyramid network. The hate pyramid network will calculate the hate probability of tweet t. The sareasm pyramid network returns the sarcasm probability of the given tweet t. As a result, for the tweet f, we will get the probabilities of hate and sarcasm.
Since both the probabilities are obtained from two different pipelines, they are independent from each other. Bayes Theorem of conditional probability is now used to calculate four probabilities which are as follows:
Probability of tweet being sarcastic given hate, (Ps )
Probability of tweet being not sarcastic given hate, (P))
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Sarcasm
Not Sarcasm
Hate
PSIh
Ps
Not Hate
PaTH
Table I: Payoff Matrix
Probability of tweet being sarcastic given not hate, (Ps)ør).
Probability of tweet being not sarcastic given not hate, (P
The following equation is used to obtain four cases of probabilistic relationships.
P()NS) P(H)
PEI
(2)
Finally the Nash Equalizer constructs a Prisoners' dilemma considering sarcasm and hate as the two prisoners as shown in [able The pooled output of the table constructed using the dilemma used to calculate the label for the final tweet as follows:
Case I: The maximum probability of tweet comes out to be not sarcastic given not hate. This case is clearly indicative of the fact that the given tweet is neither hate nor sarcastic. Thus this case of tweet is labelled as neither hate nor sarcastic.
Case II: The maximum probability of tweet comes out be not sarcastic given hate. This case clearly indicative of the fact that the tweet is hate and not sarcastic. Thus this case of tweet is labelled hate.
Case III: The maximum probability of tweet comes out be sarcastic given not hate. This case is clearly indicative of the fact that the tweet is not hate but is only sarcastic. Thus this case of tweet labelled sarcastic.
Case IV: The maximum probability of tweet comes out to be sarcastic given hate. This case is clearly indicative of the fact that the tweet is hate as well as sarcastic. For this case we consider true hate factor instead of hate.
As mentioned above for cases I, II and III do not consider the true hate of any tweet. But if the tweet belongs to category IV, that means that the given tweet contains hate content well as sarcasm content. We then proceed to evaluate the true hate parameter of the tweet. This is the case wherein most classifiers fail to flag the nature of the tweet correctly. Thus to tackle this propose the true hate parameter. The true hate factor considers effective hate by excluding the sarcastic component of the given tweet. These include cases where the sentence looks like hate but is sarcastic. For calculating the true hate factor we start with simple hypothesis and move towards complex relations. A simple relationship calculates true hate by subtracting the probability of sarcasm from hate and if the probability of hate is greater than sarcasm then the given tweet is flagged as hate and vice-versa. The hypothesis used to calculate hate is given as follows:
P(Hrrue) P(H)- [(S)]
Here n, m 6 N This hypothesis is used to evaluate the probability of the true hate (HTrse) parameter of tweet in case where the tweet is sarcastic given hate. As seen in the equation for such a case we revert back to the original hate and sareasm probabilities of the tweet and from those we obtain the true hate of such tweet. If we obtain the probability of true hate to be greater than 0.5, we classify the tweet as hate otherwise we classify the tweet as sarcastic.
3.3 DATASETS
We evaluate our proposed SarNet model on 2 benchmark datasets.
Hate Speech Dataset:

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Davidson et al. (27 utilised Twitter API and collected 84.4M tweets from 33.458K users. The CrowdFlower crowdsoureing platform annotated the randomly sampled 25k tweets. Since the dataset was unbalanced we had to deploy an oversampling technique to balance the classes. We then binarised the classes as hate and not hate.
Basile et al. (29) outlined the structure of the SemEval-'19 19 Task 5 identifies hate speech against hate speech against immigrants and women in Spanish and English. The data set composed of 6,600 spanish and 13,000 english tweets. We utilised the english corpus for our hate speech training purposes.
For the task of training one pipeline of the pyramid network hate speech we binarised both the datasets (27) and (29) and merged them into one dataset. This was done to increase the size of the dataset. The total number of hate tweets were 37,521 and the total number of not hate tweets were 45,045. This new combined hate speech dataset was then split into train, validation and test. The train, validation and test split ratio is 70, 20 and 10 percent respectively. Fig 2(a) is a bar graph which shows the number of hate and not hate tweets.
We tested our true hate detection task on another hate speech dataset (30) The dataset was created using Twitter data. The text is divided into three categories: hate speech, offensive language, and neither. For our task we binarised the class labels as hate and not hate by merging the hate speech and offensive language class labels into one class label; i.e. hate. The dataset consisted of 2,242 hate tweets and 29,720 not hate tweets. Due to such imbalance of the dataset we applied SMOTE to upsample the hate tweets to 29,720 hate tweets. Figure 2(c) is bar graph which shows the number of hate and not hate tweets after SMOTE has been applied.
Sarcasm Dataset:
Arora et al. (28) proposed a binary labelled sarcasm detection dataset in english. The sarcastic statements extracted from 2 news websites: the Onion, which publishes satirical renditions of critical events, and all of the headlines from the News in Brief and News Photos categories. HuflPost provided the non-sarcastic news headlines. The advantage of using this dataset was its high quality labels with little noise, formal usage of english language and absence of spelling mistakes.
For the task of training one pipeline of the pyramid network on sarcasm detection we used (28) dataset. It consisted of 13,624 sarcastic tweets and 14,985 not sarcasm tweets. The train, validation split for this dataset is 80 and 20 percent respectively. Fig 2(b) is bar graph which shows the number of sarcasm and not sarcasm tweets.
3.4 EXPERIMENTAL SETTINGS
SarNet is implemented using Python 3. All the experiments were performed on Google Collabora- tory The GPU is Tesla K80 with a memory of 12 GB. Tensorflow API was used.
3.5 HYPERPARAMETER SETTINGS
The hate dataset is split into training, validation and testing 70, 20 and 10 per cent respectively while the sarcastic dataset is split into train and valid of 80 and 20 respectively. The hate pipeline of the Pyramid Net trains and fine-tunes on the train and validation splits of the hate dataset and the sarcastic pipeline of the Pyramid Net trains and fine-tunes on the train and validation split of the Sarcastic dataset. Both the pipelines hate dataset. Furthermore the total number of ANN layers in the Pyramid Net are 7 with neurons decreasing from 100. 80. 60, 40, 20, 10 6. Dropout layers are present with factor of 0.2 from ANN layers of 100 to 10 and factor of 0.4 from 10 to 6. The output vector of the ANN layer of neurons transposed into
used calculate the probabilities on the test split of the
dimensional matrix using a repeatvector layer. The 7 BiLSTM layers contains 100 cells each. ANN layers use ReLU activation function. Adam optimizer and the bininary cross-entropy loss function are used. Total trainable parameters of the Pyramid Network are 1,697.177.
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3.6 COMPARATIVE ANALYSIS
In this section we give detailed comparative analysis of our proposed SarNet model with 14 base- line models and 5 state-of-the-art Ao a result, we recreate the comparison techniques from scratch using the instructions provided in those publications. Our proposed model SarNet achieved the best results. achieved a precision, recall and an Fl score of 0.93, 0.84, 0.88 respectively.
Comparison with Baseline Models:
SarNet was compared with 14 baseline models. this comparative analysis various combination of feature extraction techniques followed by classifiers were tested for the hate speech detection task on new combined hate speech dataset. The formation of the new combined dataset is described above in subsection 3.3. The following techniques were used for feature extraction:
TF-IDF- The relevance of term is represented by (Term Frequency- -nnbeers--I Inverse Document Frequency). is the result of calculating the dot product of TFF and IDF.
GloVe- GloVe (26) is an abbreviation for Global Vectors for Word Representation. It is an unsupervised learning approach that seeks to construct word embeddings from a given corpus by aggregating global word co-oceurrence matrices. The primary idea behind the GloVe word embedding is to use statistics to determine the link between words.
Small BERT- BERT
32
base consists of 12 layers, 768 hidden size and 12 attention heads.
BERT- BERT large
33
consists of 24 layers, 1024 hidden size and 16 attention heads.
35 is a "Lite" version of BERT with much less parameters. ALBERT (like BERT) uses deep neural network using the Transformer architecture calculate dense vector representations for natural language.
ALBERT- ALBERT
ELECTRA- ELECTRA 34 is BERT-like model that has been pre-trained as discrimi- nator in a generative adversarial network setup (GAN). ELECTRA (like BERT) uses a deep neural network using the Transformer architecture to caleulate dense vector representations for natural language.
The baseline classifiers used for the hate speech detection task are SVM, XGB, MLP, CNN and Bi- LSTM. The combination of feature extraction techniques and classifiers deployed can be seen in the bar graphs given in the appendix section. The best performance in terms of precision was obtained by BERT+CNN combination in hate speech detection where BERT was used for feature extraction and CNN was used for classification. It achieved a precision of 0.78. In terms of recall Small BERT+CNN achieved a best recall of 0.81. The best Fl score was achieved for ELECTRA+MLP and BERT+CNN. The Fl score achieved was 0.76.
Comparison with State-of-the-at Models:
5 state-of of- of-theeart models were trained on the new combined dataset for hate speech detection. They are follows:
Siino et al. (8) presented a deep learning model based on I-D CNN. Their model achieved a precision, recall and an Fl score of 0.81, 0.49 and 0.79 respectively when trained and tested on the new combined hate speech dataset.
Pereira-Kohatee et al. (37) proposed HaterNet wherein they deployed multiple deep learn- ing based models. We trained their best performing model which used Tf-Idf for feature extraction followed by a combination of MLP neural networks for classification. When trained and tested on our new combined hate speech dataset their model achieved a preci- sion, recall and F1 score of 0.66, 0.49 and 0.64 respectively.
Roy et al. (15) proposed a deep CNN based model using GLoVe embeddings. Their model achieved a precision, recall and Fl of 0.61, 0.42 and 0.48 respectively.
Ding et al. 388 proposed a stacked BiGRU model based on a capsule network architecture. They used fastText to transform tweets into vector representations. Their model achieved a precision, recall and score of 0.67, 0.64 and 0.62 respectively when trained and tested the combined hate speech dataset.
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Khan et al. (36) proposed HCovBi-Caps convolutional, BiGRU, and capsule network- based deep learning model using Glo Ve embeddings. Their model when trained and tested combined hate speech dataset achieved precision, recall and Fl score of 0.84, 0.75 and 0.76 respectively.
4 CONCLUSION
In this research we proposed SarNet, a two fold deep learning model to predict the true hate of a given tweet. Our main goal was to extract the degree of hate and sarcasm from tweet get more realistic comprehension of any given tweet. For this task we proposed the pyramid network to extract the probabilities of hate and sareasm. Two parallel pipelines of the pyramid network were trained on sarcasm and hate speech datasets to get their probability. Using Bayes' Theorem we made four cases of probabilities of hate and sareasm for any given tweet. Then our proposed Nash Equalizer used the concept of game theory (Prisoner's Dilemma) to construct payoff matrix to give one of the four cases of degree of hate and sarcasm. From here we calculated the true hate parameter for particular case (P(H|S) obtained from the Nash Equalizer. The rest of the three cases were directly classified. the end tweets were classified as hate, sarcastic or neither. We used one Kaggle sarcasm dataset for learning the sarcasm features of the tweets. We trained and tested our hate speech task on the new combined dataset which was formed by merging the T.Davidson dataset and the SemEval-2001 and Fl score of 0.93, 0.84 and 0.88 respectively. We trained and implemented 14 baseline classifiers and 5 state-of-the-art architectures Our model outperformed all other classifiers. We studied the impact of various hyperparameters on the efficacy of our proposed SarNet model.
Task 5 english hate speech dataset. Our model achieved a precision, recall
the new combined dataset for comparison with our model.
LIMITATIONS
The main drawback the non availability of robust datasets. Datasets suffer from multiple problems such as class imbalance, annotator bias, difference in the definition of hate speech and varying forms and intentions of expressions. The availability of robust datasets would increase the validity of our model's 's prediction. We have researched only on english. Research work can further be extended to other languages, as well as considering other parameters, alongside sarcasm like sexism etc.
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APPENDIX
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Figure 2: F1 Score of SarNet vs state-of-the-ar art on T.Davidson + SemEval Dataset
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Figure 3: Recall Score of SarNet vs state-of- otate-of-the-art on T.Davidson + SemEval Dataset
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Figure Precision Score of SarNet vs state-of-the-art on T.Davidson + SemEval Dataset
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Figure 5: F1 Score of SarNet vs state-of-the-ar art on T.Davidson + SemEval Dataset
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Figure 6: Recall Score of SarNet vs state-of- otate-of-the-art on T.Davidson + SemEval Dataset
+16
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Figure Precision Score of SarNet vs state-of-the-art on T.Davidson + SemEval Dataset
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Figure 8: F1 Score of SarNet state-of-the-an on Kaggle Dataset
Figure 9: Recall Score of SarNet vs state-of-the ahetthe-rt on Kaggle Dataset
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Figure 10: Precision Score of SarNet vs state-of-the-art on Kaggle Dataset
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2:00
L000
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(b)
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Figure 11: Dataset for (a) Hate Tweets (b) Sarcastic Tweets (c) Kaggle Hate after SMOTE
15


Reviews:
[{"review_id": "sLHnA8f08ey", "reviewer": "Reviewer_xKDB", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper proposes a model for detecting true hate (vs. sarcasm) speech in texts, i.e., hate speech detection taking into account sarcasm.  The proposed model uses game theory (Prisoners’ Dilemma) and Nash equilibrium. The experiments show that the proposed model outperforms baselines and state-of-the-art models. ", "summary_of_the_review": "The paper tackles the problem of sarcasm in hate speech, and the proposed model outperforms other baselines and state-of-the-art. However, some analysis or discussion to show the efficacy of the model will be helpful.", "strength_and_weaknesses": "strength:\n- sarcasm in hate speech seems important, and this paper tackles that problem.\n- the proposed model outperforms baselines and state-of-the-art models for hate speech detection\n\nweaknesses:\n- I am not familiar with game theory (Prisoners’ Dilemma) and Nash equilibrium, etc. presented in this paper, and had difficulty to truly understand the proposed model.\n- Although the experiment results suggest that the detection performance of the proposed model is superior to other models, more analysis/discussion (especially regarding sarcasm) will be helpful to show whether the proposed model works in an intended way or not.\n- the study about the impact of different hyperparameter settings? Where can I find them? ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "rZRghfNKPHp", "reviewer": "Reviewer_jETC", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "In the paper, the authors propose SarNet, a method to tackle the hate speech detection task. The authors argue that previous methods tend to judge satirical speech as hate speech, resulting in false positives. Their main goal was to extract the degree of hate and sarcasm from a tweet to get a more realistic comprehension of any given tweet. They propose the pyramid network to extract the probabilities of hate and sarcasm.", "summary_of_the_review": "This paper can achieve a certain performance improvement on hate speech detection task, but the logic and expression of the article have many areas worth improving.", "strength_and_weaknesses": "Strengths\n1. The author constructs a prisoner's dilemma via a Nash equalizer, treating sarcasm and hate as two prisoners, and finally uses the output of the dilemma to calculate the label of the final tweet.\n2. Improved performance compared to baseline methods and state-of-the-art.\n\nWeaknesses:\n1. The content of the article needs to be further refined.\n- Introduction mentions \"Our proposed SarNet model as shown in Figure 10 analyzes the contextual information of a sentence.\", but what Figure 10 expresses is \"Precision Score of SarNet vs state-of-the-art on Kaggle Dataset\", which does not fit the context. \n- There is an extra space in \"94.11 per cent\" and \"94.62 per cent\" in Sec 2.1.\n- The semantics of the two sentences in \"SarNet is proposed for true-hate detection which includes data pre-processing, and an explanation of our proposed two-fold deep learning based method.\" mentioned in Methodology are discontinuous.\n- The picture of Figure 1 is so blurry that I can't even make out the text in the blue box on the left.\n- In the experimental part, the authors did not analyze the experimental results.\n- There is no table or picture in the experimental part of the text, and the related work part is too long.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "K0tsiy50XB", "reviewer": "Reviewer_cP5A", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper presents two deep learning methods, SarNeis, that address the nuisance of hate speech detection in a given text or sentence and incorrectly flag a sarcastic tweet. First, the author employed ANN-BiLSTM, a pyramid network, to compute a sentence's hate and sarcastic probabilities. Secondly, the author used the nash equalizer from the game theory concept and prisoner's dilemma. In summary, SarNet integrates the ANN and LSTM network for calculating probabilities to Nash Equalizer, which plays games to identify the label of the sentence, using a quasi-ternary labeling process. The proposed method treats hate and sarcasm as two prisoners.", "summary_of_the_review": "An interesting method is presented, focusing on using ANN-BiLSTM as a pyramid network to calculate a sentence's hate and sarcastic probabilities. The presented results show competitive performance with the SOTA method during training.  The paper is missing an error analysis section to investigate the registers of misclassified texts critically. Please provide examples of correctly/misclassified text with critical reflection.", "strength_and_weaknesses": "Still, the ANN layers in the pyramid network pull aesthetic elements from the processed input tweet vectors. What would the model do with input texts that are often short primary samples with confusing words like idioms, onomatopoeias, homophones, phonemes, synonyms, acronyms, anaphora, and polysemy?\n\nANN can solve various problems, including virtually any problem reduceable to functions, yet, sharing is challenging after training an ANN; overfitting and convergence cannot be guaranteed. So, how will the SarNeis model handle these challenges? The addition of\nnash equilibrium to quantify probabilistic representations of the tweet as hate or sarcasm is excellent, but what type of stylometric features did ANN extract and improve with the BiLSTM layer? \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "2a2Gfj8-gDV", "reviewer": "Reviewer_ioZ4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "In this paper authors propose to use two separate networks, one that is trained to detect hate speech and  the other that is trained to detect sarcasm. The final sarcasm detection score is then a combination of the outputs of both networks. ", "summary_of_the_review": "Quality is not up to the standard. ", "strength_and_weaknesses": "Strengths:\n\n- Good idea to try to separate out hate from sarcasm via separate network. \n\nWeaknesses:\n\n- Paper is clearly not in a finalized state, it needs a thorough efforts in polishing. From Figures to how literature is compared in bullet point fashion, without trying to synthesize it. \n- Ideas also are too simple. Of course simple ideas can also work and those would definitely be valuable, but then authors need a bullet proof experiments. To really show that ideas work. \n- Experiments are basically non-existent. Authors should note that main paper needs to have the main story supported by the experiments, anything that is in appendices are not necessarily read at all. \n- But most important point is that of course we can always fit model to any dataset with even randomly assigned labels. How you can validate that your trained method really can detect sarcasm? ", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject