Deep Learning approaches for Hate speech detection. In this work we used the two deep learning approaches DCNN and MLP two separate classifier on four publicly available datasets.

1. A Deep Learning Approach For Hate
Speech and Offensive Language
Detection on Twitter
Presented By:
Nasim Alam
M Tech Computer

2. INTRODUCTION
Hate Speech
● Hate speech is speech that attacks a person or group on the basis of attributes
such as race religion, ethnic origin, national origin, gender, disability, sexual
orientation.
● The law of some countries describes hate speech as speech, gesture or
conduct, writing, or display that incites violence or prejudicial action against a
protected group or individual on the basis of their membership of the group.
● Social media platforms like Facebook and twitter has raised concerns about
emerging dubious activity such as the intensity of hate, abusive and offensive
behavior among us.
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3. Motivation
Potential of social media for spreading hate speech
◉ 30% internet penetration in India (World Bank, 2016)
◉ 241 million users of Facebook alone (The Next Web Report, 2017)
◉ 136 million Indians are active social media users (Yral Report, 2016)
◉ 200 million whatsapp users in India (Mashable, 2017)
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4. OBJECTIVE
• The main objective of this work is to develop an automated deep learning
based approach for detecting hate speech and offensive language.
• Automated detection corresponds to automated learning such as machine
learning: supervised and unsupervised learning. We use a supervised learning
method to detect hate and offensive language.
• Classify tweets into three or four classes (like: racist, sexist, none , both) based
on tweet sentiment and other features that a tweet demonstrate.
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5. PROJECT CONTRIBUTION
• An efficient feature extraction and selection.
• A Multi-layer perceptron based model to train and classify tweets
into hate, offensive or none.
• A Dynamic CNN based model for training and GloVe embedding
vector for feature extraction.
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6. Literature survey
Refereance Dataset Techinque Results
Greevy et al
(2004)
PRINCIP Corpus
Size: 3 Million words from tweets
Model: SVM
Feature Extraction:
BOW, Bi-gram
Precision: 92.5%(BOW)
Recall: 87% (BOW)
Precision: 92.5% (Bi-gram)
Recall: 87% (Bi-gram)
Waseem and Hovy
(2016)
Total Annotated tweets: 16,914.
#Sexist tweets 3,383.
#Racist Tweets 1,972.
#tweets Neither racist nor sexist: 11,559.
Model: Char n-grams
Word n-grams
Precision: 73.89%(char n-gram)
Recall: 77.75% (char n-gram)
F1 Score: 72.87 (char n-grams)
Precision: 64.58%(word n-grams)
Recall: 71.93% (word n-grams)
F1 Score: 64.58(word n-grams)
Akshita et al
(2016)
Waseem and Hovy, 2016
Size: 22,142 tweets
Class: Benevolent, Hostile, others
Model: SVM, Seq2Seq
(LSTM), FastText
Classifier(by Facebook
AI Research)
Feature Extraction: TF-
IDF, Bag of n-words
Average F1 score among
classes: 0.723 (SVM),
0.74 (Seq2Seq)
Overall F1 Score: 0.84 (FastText)
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7. Literature survey
Refereance Dataset Techinque Results
Ji Ho et al
(2016)
Waseem and Hovy, 2016
Waseem 2016
Class: Racism,Sexism and None
Size: 25k tweets
Model: Hybrid CNN
Classifier(wordCNN +
CharCNN)
Precision: 0.827
Recall: 0.827
F1 Score: 0.827
Davidson et al
(2017)
CrowdFlower (CF)
Class: Hate,offensive and None
Size: 25k tweets
Model: Linear SVM, Logistic,
Regression
precision: 0.91,
Recall: 0.90,
F1 score: 0.90.
Zhang et al
(2018)
7 publicly available dataset:
DT(24k), RM(2k), WZ-LS(18k), WZ-
L(16k), WZ-S.amt(6k), WZ-S.exp(6k),
WZ-S.gb(6k)
Model:CNN+GRU
Accuracy:
DT: 0.94, RM: 0.92, WZ-L:
0.82,WZ-S.amt: 0.92, WZ-S.exp:
0.92, WZ-S.gb: 0.93
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8. Dataset
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Dataset No of Tweets Classes (%Tweets) Target Class
WZ-LS 18,595 Racism(10.6%),
Sexism(20.2%), None (68.8%)
Racism, Sexism
WZ-L 16,093 Racism(12.01%),
Sexism(19.56%), None
(68.41%)
Racism, Sexism
WZ-S.exp 6,594 Racism(1.2%),
Sexism(11.7%), both(0.53%),
None (84.37%)
Racism, Sexism
Hate (Davidson) 24,783 Hate(11.6%),
offensive(76.6%), Neither
(11.8%)
Hate, Offensive

9. A Multi-Layer perceptron (MLP) based model
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● Raw text in the form of tweets in csv file crawled from twitter using
Tweepy API.
● A lots of preprocessing done to get cleaned text.
● Feature Extraction:
○ Convert it into TF-IDF feature matrix.
○ POS TF feature matrix.
○ Other Features like: No_of_syllales, avg_syl_per_word,
no_of_unique_words,num_mentions,is_retweets,VaderSentime
nt:pos,neg,neutral, compound).
● Concatenated these feature matrices into one matrix.
● We used logistic regression with L1 regularization to select most
important features and then passed this selected feature vector to an
MLP network for classification.
● MLP consists of an input layer, three hidden layer and an output layer
or softmax layer.
○ Input layer Size: Size of Input feature matrix, Activation:
Sigmoid.
○ Number of nodes: 200, 140, 70 and Activation Function: Relu.
○ Softmax Layer: Output class: 3 or 4, Activation function:
Softmax.
MLP based Proposed model

10. 10
A simple single layer CNN
● A Sentence (a single tweet): X1:n = X1 ⊕ X2 ⊕ …………..Xn
● All possible widow of length h: {X1:h, X2:h+1, …………Xn-h+1:n }
● We can have multiple filter or window of different length like h=1 for unigram, h=2 for bigram , h=3
for trigram and so on.
● This filter is consist of random weight which is convolved over sentence matrix in overlapped
fashion and a sum of multiplication of filter and X is calculated as feature map.
● A feature map C = [c1,c2,……………………cn+h-1] ∈ ℝn-h+1, for multiple filter we may have multiple
feature map as Ci = [C1, C2, …………Cm] where m is number of filters.
● pooling: pooling is a process of selecting only interested region from the convolution feature vector.
The result of pooling is Ĉ = max{ C } and Ĉi can be pooled feature vector for ith filter.
● All the pooled vectors are concatenated into single feature vector Z = [Ĉ1, Ĉ2, ……, Ĉm ]
● Finally Z feature vector is passed through a softmax function for final classification.

11. Word2Vec Word Embedding
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Word2vec
• Word2vec is a predictive model, which uses an ANN based model to
predict the real valued vector of a target word with respect to the
another context word.
• Mikolov et al used continuous bag of words and skipgram models
that are trained over millions of words and represent each in a
vector space.
GloVe
• GloVe is a semantic vector space models of language represent
each word with a real valued vector.
• GloVe model uses word frequency and global co-occurance count
matrix.
• Count-based models learn their vectors by essentially doing
dimensionality reduction on the co-occurrence counts matri.
• These vectors can be used as features in a variety of applications
such as information retrieval, document classification, question
answering, NER, and Parsing.
Representation of word in vector space

12. Text based CNN
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Text based Convolutional Neural Network operation (Source: Kim 2014)

13. 13
Dynamic Convolutional Neural Network
• Wide Convolution: Given an input sentence, to obtain the first
layer of the DCNN we take the embedding Wi ∈ ℝd for each word
in the sentence and construct the sentence matrix s ∈ ℝd × s .
• A convolutional layer in the network is obtained by convolving a
matrix of weights m ∈ ℝd × m with the matrix of activations at the
layer below.
• A dynamic k-max pooling operation is a k-max pooling
operation where we let k be a function of the length of the
sentence and the depth of the network, we simply model the
pooling parameter as follows:
Where i is ith conv-layer in which k max-pooling is
applied. L is the total number of convolutional layers
in the network.S is input sentence length.
• Folding is used just to sum every two rows in feature map
component wise. For the feature map of d rows folding returns
d/2 rows.
A DCNN Architecture (Source: Kalchbrenner et al. (2014) )

14. A DCNN based Model for Hate speech detection
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● Tweets: Crawled tweets using tweet-id, saved as csv file having tweets and label.
● Preprocessing of tweets:
○ Convert to lowercase, Stop words removal.
○ Remove unwanted symbols and retweets.
○ Normalize the words to make it meaningful.
○ Remove tokens having document frequency less than 7 which removes
sparse features which is less informative.
● Word2vec conversion:
○ A 300-dimensional word embedding GloVe model, which is pre- trained on
the 4-billion-word Wikipedia text corpus by researcher from Stanford
University.
○ Embedding dimension: 100*300.
● Passed to DCNN model for classification:
○ Four conv1d layer of having 300 filters of each of window size 1,2,3 and 4.
○ K-max pooling performed corresponding to each conv1d and merged into
one single vector.
○ Further passed through Dropout, dense layer and softmax layer for
classification.
A DCNN based proposed model

15. Results and Discussion
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Datasets SVM MLP CNN* DCNN
WZ-LS 0.73 0.83 0.82 0.83
WZ-L 0.74 0.83 0.82 0.83
WZ-S.exp 0.89 0.93 0.90 0.9283
Hate 0.87 0.92 0.91 0.92
Table 1: shows testing accuracy of 4 different model on 4 publicly available Hate & offensive
language datasets.

16. Results and Discussion
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(a) (b)
(c) (d)

17. Performance of MLP based Model
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WZ-LS
class Precision Recall F1
Racist 0.73 0.73 0.73
Sexism 0.77 0.56 0.65
None 0.85 0.92 0.88
Both 1.0 0.33 0.50
Overall 0.83 0.83 0.82
WZ-L
class Precision Recall F1
Racist 0.81 0.68 0.74
Sexism 0.85 0.61 0.71
None 0.83 0.93 0.88
Overall 0.83 0.83 0.82
WZ-S.exp
class Precision Recall F1
Racist 1.0 0.05 0.2
Sexism 0.85 0.77 0.81
None 0.95 0.99 0.97
Both 0.0 0.0 0.0
Overall 0.93 0.92 0.93
DT
class Precision Recall F1
Hate 0.60 0.52 0.56
Offensive 0.95 0.80 0.87
Neither 0.87 0.91 0.89
Overall 0.92 0.91 0.92
(a) (b)
(c)
(d)

18. Conclusion
The propagation of hate speech on social media has been increasing
significantly in recent years and it is recognised that effective counter-measures
rely on automated data mining techniques. Our work made several contributions
to this problem. First, we introduced a method for automatically classifying hate
speech on Twitter using a deep neural network model (DCNN and MLP) that
empirically improve classification accuracy. Second we did comparative analysis
of our model on four publicly available datasets.
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19. Future Work
We will explore future work in numerous ways, such as first, further fine tuning of
hyperparameter can improve accuracy, second we will use metadata along with
tweets such as number of followers, the location, account age, total number of
(posted/favorited/liked) tweets, etc., of a user. We will make a hybrid model
(DCNN + MLP), all tweets are passed through DCNN model and metadata to MLP
in parallel then the result of these two can be combined and then it will be passed
through dense layer and softmax layer for final classification.
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20. THANKS!
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21. References
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