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Battling Hateful Content in Indic Languages HASOC '21

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The extensive rise in consumption of online social media (OSMs) by a large number of people poses a critical problem of curbing the spread of hateful content on these platforms. With growing usage of OSMs in multiple languages, the task of detecting and characterizing hate becomes more complex. The subtle variations of code-mixed texts along with switching scripts only adds to the complexity. This paper presents the solution for the HASOC 2021 multilingual hate-speech detection shared task for Twitter. We adopt a multilingual transformer based approach and describe our architecture for all 6 sub-tasks as part of the challenge. Out of the 6 teams that participated in all the sub tasks, our submissions ranks 3rd overall.
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Battling Hateful Content in Indic Languages
HASOC ’21
Mallika Subramaniana,Manvith Reddya,Prashant Kodalia,T.H. Arjuna,
Manish Shrivastavaaand Ponnurangam Kumaragurua
aInternational Institute of Information Technology, Hyderabad, India
bDelhi Technological University, Delhi, India
Abstract
The extensive rise in consumption of online social media (OSMs) by a large number of people poses a
critical problem of curbing the spread of hateful content on these platforms. With growing usage of
OSMs in multiple languages, the task of detecting and characterizing hate becomes more complex. The
subtle variations of code-mixed texts along with switching scripts only adds to the complexity. This paper
presents the solution for the HASOC 2021 multilingual hate-speech detection shared task for Twitter.
We adopt a multilingual transformer based approach and describe our architecture for all 6 sub-tasks as
part of the challenge. Out of the 6 teams that participated in all the sub tasks, our submissions ranks 3rd
overall.
Keywords
Hate Speech, Social Media, Code Mixed, Indic Languages
1. Introduction
Dissemination of hateful content on nearly all social media is increasingly becoming an alarm-
ing concern. In the research community as well, this is a heavily studied research problem.
Misconduct such as bullying, derogatory comments based on gender, race, religion, threatening
remarks etc. are more prevalent today than ever before. The repercussions that such content
can have is profound and can result in increased mental stress, emotional outburst and negative
psychological impacts [
1
]. Hence, curbing the proliferation of this hate speech is imperative.
Furthermore, the massive scale at which online social media platforms function makes it an
even more pressing issue, which needs to be addressed in a robust manner. Most online social
media platforms have imposed strict guidelines
123
to help prevent the spread of hate. Inspite
HASOC (2021) Hate Speech and Oensive Content Identication in English and Indo-Aryan Languages
(J. Jain); jushaan18@gmail.com ( J. S. Kalra); mallika.subramanian@students.iiit.ac.in (M. Subramanian);
manvith.reddy@students.iiit.ac.in (M. Reddy); prashant.kodali@research.iiit.ac.in (P. Kodali);
arjun.thekoot@research.iiit.ac.in (T.H. Arjun); m.shrivastava@iiit.ac.in (M. Shrivastava); pk.guru@iiit.ac.in
(P. Kumaraguru)
© 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
2https://transparency.fb.com/en-gb/policies/community-standards/hate-speech/
of these platform regulations, the dynamics of user-interaction inuence the diusion (and
hence increase in) of hate to a large extent [2].
The problem of hate speech has been addressed by several researchers, but the rise in multilin-
gual content has added to the complexity of identication of hateful content. Majority of these
studies deal with high-resource languages such as English, and only recently have low-resource
languages – such as several Indic Languages – been more deeply explored [
3
]. In a country
like India, with multitude of regional languages, the phenomenon of Code Mixing/Switching
(wherein linguistic units such as phrases/words of two languages occur in a single utterance) is
also pervasive.
In this paper we elucidate our approach in solving the six downstream tasks of hate speech
identication and characterization in Indian languages as a part of the ‘HASOC ’21 Hate Speech
and Oensive Content Identication in English and Indo-Aryan Languages’ challenge. Motivated
by existing architectures, we curate our own pipeline by fusing ne-tuned transformer based
models with additional features to solve this challenge and highlight the dierent methodologies
that were adopted for the three languages – English, Hindi, Marathi, code mixed Hindi - English.
2. Literature Review
Discerning hateful content on social media is an already tricky problem given the challenges
associated with it, for instance disrespectful/abusive words could be censored in text, some
expressions may not be inherently oensive, however they can be so in the right context[
4
].
Owing to the conversation design of social media wherein users can reply to a given comment
(either support, refute or irrelevant to the original message), the build-up of threads in response
to a hateful message can also intensify hate even if the reply is not hateful on its own. The
evolution of such hate intensity has shown diverse patterns and no direct correlation to the
parent tweet which makes the task of hate speech detection more dicult [5].
Signicant amount of research has been conducted to evaluate traditional NLP approaches
such as character level CNNs, word embedding based approaches and the myriad of variations
with LSTMs (sub-word level, hierarchical, BiLSTMs) [
6
]. Likewise, Machine Learning algorithms
including SVMs, K-Nearest Neighbours, Multinomial Naive Bayes (MNB) and their respective
performances in multilingual text settings have also been explored [
7
,
8
,
9
]. Investigating
categories of profane words that are commonly used in hate speech is another non-trivial
sub-task under the hate detection umbrella, primarily because of the dierent interpretations of
words in dierent cultures/demographics, adaptation of slangs in newer generations etc [10].
In recent times however, with the introduction of Transformer based models and their
performance in Natural Language Understanding (NLU) tasks, signicant work has been done
in order to adapt these for multilingual texts as well to leverage transfer between languages.
Models such as XLMR, mBERT, MuRIL, RemBERT have gained much popularity and have shown
promising results [
11
,
12
,
13
]. Transfer learning based approaches that leverage performance of
high resource languages accompanied with CNN classication heads have also shown signicant
improvements in capturing hateful content on social media platforms [
14
,
15
]. Sharing and
re-utilizing the model weights learnt whilst training on a corpus for a high resource language
can aid the process of training for languages that are still under explored [16].
3. Dataset
3.1. Dataset & Task Description
consisted of 3 languages, namely – English, Hindi and Marathi. For English and
Hindi, the task was further subdivided into 2 sub-parts:
a)
Identication of hateful v/s non-
hateful content and
b)
Characterizing the kind of hate present in a tweet – either Profane,
Hateful, Oensive or None. The distribution of the dierent data classes for each of the three
languages is shown in Table 1.
Language
Number of Tweets
Non-Hateful Hateful None Oensive Hate Profane
English 1342 2501 1342 1196 683 622
Hindi 3161 1433 3161 654 566 213
Marathi 1205 669 - - - -
Sub Task 2 2899 2841 - - - -
Table 1
Distribution of the HASOC 2021 dataset for Subtask 1, for the three languages. For each language and
task, the corresponding number of tweets per class is shown above.
Focus of
was binary classication : Hate & Oensive or Non Hate-Oensive, but
with following additions :
Tweets are English - Hindi Code Mix sentences, and
Classication should be based on context + tweet, and not just tweet alone.
For example : Consider that in a tweet thread, tweet A was reply to tweet B. For classifying
tweet A, the model can leverage the information from the parent tweet - tweet B.
Figure 3b demonstrates the relationship between the tweets to be classied and their contexts.
3.2. Preprocessing Data
As a precursor to applying any NLP models on text data, we pre-processed the dataset with
standard techniques. Given that the data fom Twitter is bound to have certain amount of noise
and unwanted elements such as – URLs, mentions etc, these were removed from the tweet texts.
Hashtags have a slightly dierent contribution to analysis of the tweet since they may or may
not contribute positively in the classication task. Through the results from our experiments,
we observed that omitting the hashtags proved to work better, and hence they were cleaned
from the tweet as well.
Since the data is code mixed, not only in terms of the combination of languages but also
with respect to scripts (some English text is written in Roman script, whereas some Hindi text
is written in Devanagari apart from Roman), we also normalize the Indic language scripts for
Marathi and Hindi. In addition to that, we removed stop words for the Marathi dataset using
this list4. Finally, punctuations were also removed from the dataset texts.
4https://github.com/stopwords-iso/stopwords-mr
(a)
Using BerTweet model with a MLP classier
(b)
Combining CNN features over XLM-R output
and manually generated feature vectors.
Figure 1:
Architecture and pipeline for the models used for the downstream task of hate detection and
classification for the English language.
An interesting observation was that for the task of hate detection, the presence of emojis
converted to text in the tweets did not improve the performance of our models signicantly
(rather it reduced the scores by some margin). However, including emojis along with text while
classifying hate did have a positive impact since the emoji-text conversion was able to capture
hints of sentiment and indirect oensive/profane content.
4. Methodology
4.1. Sub Task 1: Identifying Hate, oensive and profane content from the
post
4.1.1. English Classifiers
For the English Sub Task, the architecture that resulted in the best performance is an ensemble
of the following models:
Fine-tuned BERTweet model [17]
Fine-tuned XLM-Roberta [18] with CNN Head
We use XLM-R, a multilingual model, along with the monolingual model in the ensemble
as we found that some of the text in the training set has transliterated Hindi along with some
Devanagari text. We extracted textual features such as distribution of ‘?’, ‘!’, capital letters etc.
We also use the percentage of profane words and sentiment of the text as a feature. We use
profane words list curated from various sources such as words/cuss
5
, zacanger/profane-words
6
,
t-davison/lexicons.
7
For sentiment analysis we use the TweetEval[
19
] model and use its somax
output as a feature to our models.
Inspired by [
20
] we pass the embedding (concatenated last 4 hidden layers) to a CNN and
max-pool convolution layers of various widths to a fully connected layer of size 128 with
dropout. We concatenate this 128 dimensional vector with our feature vector. We pass this
output onto a dense output layer with somax activation and cross entropy loss as shown in
Figure 1.
Along with the previous models, we ne-tune BERTweet, a pre-trained language model for
English tweets. BERTweet has the same architecture as BERT and is trained on the pre-training
procedure of RoBERTa, but it is trained solely on tweets, thus, making it a viable alternative and
suitable for our task. This model has shown state-of-the-art results on tasks based on tweets
[
17
]. We use the encoder architecture and pass the pooled output through a linear layer for the
classication which uses somax activation and cross-entropy loss as shown in the Figure 1.
We also train the models on the previous years datasets but notice that this does not increase
the performance of the models but actually degrades the performance in Task 1B due to skewed
distribution of classes. Transliteration of emojis didn’t improve the performance. The class
imbalance in task 1B degraded the performance of our models hence we tried to improve upon
it by using a weighted loss function but we notice that this decreases the performance and
that the domain specic distribution is actually helping the models. We also perform K-Fold
Validation and use early stopping to avoid over-tting. We average the probabilities of each
class across folds and the two models in our ensemble.
4.1.2. Hindi & Marathi Classifier
For both the Hindi and Marathi language, the architecture that performed the best utilized the
XLM-R
transformer model. This model was able to capture the code-mixed and multilingual
nature of the tweets dataset. To amplify the results, we leveraged intermediary representations of
the language model as well as textual features that were extracted from the tweets. In particular,
we utilized the Multilingual MiniLM language model for netuning on Hindi Subtask B. We
observed that MiniLM with Focal Loss instead of Cross Entropy Loss performed better than
other baselines in the imbalanced multi-class setting of Hindi Subtask B. Focal Loss compensates
for class imbalance with a factor that increases the network’s sensitivity towards misclassied
samples.
Inspired by [
14
] we use the pre-trained representations of the text from 12 hidden layer of
XLM-R model (each of 768 dimensions) and then apply a CNN layer with a kernel size of 3. The
output is then passed through a so-max following which the cross-entropy loss is computed
whilst training. This model architecture is represented in Figure 2
We further augment the model features, with two kinds of textual features – fraction of
profane words and sentiment of the tweet. Due to lack of resources for Marathi we catalogue
8
5https://github.com/words/cuss
6https://github.com/zacanger/profane-words
7https://github.com/t-davidson/hate-speech-and-offensive- language/tree/master/lexicons
(a)
The base architecture for Hindi & Marathi sub-
tasks using XLM-R with CNN augmented with
textual features vector followed by a somax
layer.
(b)
Multilingual MiniLM architechture adopted to
overcome class imbalance while characterizing
hate for the Hindi subtask.
Figure 2:
Architecture and pipeline for the models used for the downstream task of hate detection and
classification for the Hindi & Marathi language.
a list of profane words in Marathi and use this to nd the fraction of profane words in a tweet.
For Hindi, we curate a list of profane words by collating and appending to existing lists
9
, and
use this to score each tweet. As for the sentiment of the tweet, we incorporated o-the-shelf
HuggingFace models to obtain the positive, negative and neutral scores for a tweet 10 11.
Although the textual features improved the performance for Hindi only by a small margin,
for Marathi, manually extracted textual features helped in achieving a signicant boost.
For the Marathi Sub Task, we experimented with a voting ensemble of the
XLM-Roberta
with CNN Head using the following features:
Word Embedding + Fraction of Profane Words + Sentiment Polarity
Word Embedding + Sentiment Polarity
Word Embedding
However we noticed that the base model with the embedding and the textual features performed
better on the leaderboard.
9https://bit.ly/3tEQVQQ
10https://huggingface.co/l3cube-pune/MarathiSentiment
(a)
Model pipeline for hate detection in conversa-
(b)
Hierarchy of a conversation thread and its as-
Figure 3: Model Pipeline and tweet conversation thread example for sub task 2.
4.2. Sub Task 2 : Identification of Conversational Hate-Speech in
Code-Mixed Languages (ICHCL)
The tweets for Sub task 2 are code mixed. While the Transformer based encoder models have
performed well on various monolingual NLU tasks, their performance does not reach the same
level on code mixed sentences. Multilingual transformer based models, have been applied
for various code mixed NLU tasks, and have performed better than monolingual transformer
based models [
21
]. For this task, we use XLM-RoBERTa [
18
]. To capture the context and the
tweet itself, we modify the input in the following manner, where [CLS] , [SEP] are part of the
vocabulary of model, and are used to classify and take multiple sentences as input, respectively.
[CLS] <Tweet text to be classied> [SEP] <context of parent tweet> [SEP]
<Tweet text to be classied> is the text of the tweet/comment/reply that is being classied,
while <Context of parent tweet> is either just the parent tweet or concatenation of parent tweet
and comment, depending on weather the text to be classied is a tweet or a comment or a
reply. While classifying a standalone tweet, the context is le empty. Hindi corpus used to train
XLM-Roberta is in Devanagari script, while there is only a small portion of corpus which is in
Romanised form. With the hypothesis that the performance of model will improve if the Hindi
tokens are in Devanagari script, we used CSNLI tool
12
to converting the Romanised tokens
to Devanagari script. However, this normalisation only had a marginal impact on the nal
performance of the model.
4.3. Experiments
We used Huggingface Transformers [
22
] library for implementing the classiers. For hyper
parameter tuning we use Optuna Framework
13
library. Exploring multiple architectures
simultaneously, we also tried ensembling an odd number of models following a majority rule
based selection. For English subtask we also did ensembling with averaged somax probabilities.
However, the increase in complexity of the classication pipeline did not necessarily improve
performance scores, considering the size and the distribution of the dataset for Hindi and
Marathi but helped in English. Table 2captures the Accuracy of all our models for each of the
Language Sub Task Method Accuracy
English A
XLM-R + CNN 62.30%
Ensemble 79.94%
XLM-R + CNN + Sentiment Scores 81.03%
English B XLM-R + CNN + Weighted loss 60.5%
Ensemble 65.183%
Hindi A
MuRIL 68.9%
XLM-R Base 74%
XLM-R + CNN 80.087%
Hindi B MiniLM with Focal Loss 72.64%
Marathi A
XLM-R Base 84.16%
Ensemble 88.48%
XLM-R + CNN 88.64%
Hi-En Code mix 2 XLM-R without norm 67.58%
XLM-R with norm 69.36%
Table 2
We can see performance scores for each of the six subtasks in terms of their test accuracy. All the
architectures that were experimented and tested out are tabulated here. We can observe form the results
that XLM-R combined with CNN classifier head works best across the languages of Sub Task 1, while
for Subtask 2, XLM-R with normalised input text performs the best in our experiments.
5. Conclusion
In this paper, we presented our approaches for Hate Speech detection on Indian Languages
and code mix between Hindi-English using multilingual transformer based encoder models.
Although, in this work we have employed dierent models to address individual language
specic sub-tasks, a multi-task single model based approach, which performs well across all
the language pairs, would be an interesting challenge, which we leave it for future work . In
addition to this, as part of future work, we would like to improve the performance by carrying
out an additional step of domain adaptive pre-training of the encoder models, and an ecient
ensemble of multilingual encoder models.
13https://optuna.org/
Acknowledgments
We would like to thank the organisers of HASOC’21 Shared task for addressing a crucial problem
of hate speech in Indian languages by releasing data resources, and for the smooth conduct of
the competition. We would also like to specially thank all members of our research lab, Precog,
for the constructive suggestions during the whole process.
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v1/2020.emnlp-demos.6
.
Article
The spread of Hate Speech on online platforms is a severe issue for societies and requires the identification of offensive content by platforms. Research has modeled Hate Speech recognition as a text classification problem that predicts the class of a message based on the text of the message only. However, context plays a huge role in communication. In particular, for short messages, the text of the preceding tweets can completely change the interpretation of a message within a discourse. This work extends previous efforts to classify Hate Speech by considering the current and previous tweets jointly. In particular, we introduce a clearly defined way of extracting context. We present the development of the first dataset for conversational-based Hate Speech classification with an approach for collecting context from long conversations for code-mixed Hindi (ICHCL dataset). Overall, our benchmark experiments show that the inclusion of context can improve classification performance over a baseline. Furthermore, we develop a novel processing pipeline for processing the context. The best-performing pipeline uses a fine-tuned SentBERT paired with an LSTM as a classifier. This pipeline achieves a macro F1 score of 0.892 on the ICHCL test dataset. Another KNN, SentBERT, and ABC weighting-based pipeline yields an F1 Macro of 0.807, which gives the best results among traditional classifiers. So even a KNN model gives better results with an optimized BERT than a vanilla BERT model.
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Curbing hate speech is undoubtedly a major challenge for online microblogging platforms like Twitter. While there have been studies around hate speech detection, it is not clear how hate speech finds its way into an online discussion. It is important for a content moderator to not only identify which tweet is hateful, but also to predict which tweet will be responsible for accumulating hate speech. This would help in prioritizing tweets that need constant monitoring. Our analysis reveals that for hate speech to manifest in an ongoing discussion, the source tweet may not necessarily be {\em hateful}; rather, there are plenty of such non-hateful tweets which gradually invoke hateful replies, resulting in the entire reply threads becoming provocative. In this paper, we define a novel problem -- {\em given a source tweet and a few of its initial replies, the task is to forecast the hate intensity of upcoming replies}. To this end, we curate a novel dataset constituting $\sim 4.5k$ contemporary tweets and their entire reply threads. Our preliminary analysis confirms that the evolution patterns along time of hate intensity among reply threads have highly diverse patterns, and there is no significant correlation between the hate intensity of the source tweets and that of their reply threads. We employ seven state-of-the-art dynamic models (either statistical signal processing or deep learning based) and show that they fail badly to forecast the hate intensity. We then propose \name, a novel deep state-space model that leverages the function approximation capability of deep neural networks with the capacity to quantify the uncertainty of statistical signal processing models. Exhaustive experiments and ablation study show that \name\ outperforms all the baselines substantially. Further, its deployment in an advanced AI platform designed to monitor real-world problematic hateful content has improved the aggregated insights extracted for countering the spread of online harms.
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The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags), and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context-dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model—combining convolutional and recurrent layers—for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC). For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.
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Hate speech is considered to be one of the major issues currently plaguing the online social media. With online hate speech culminating in gruesome scenarios like the Rohingya genocide in Myanmar, anti-Muslim mob violence in Sri Lanka, and the Pittsburgh synagogue shooting, there is a dire need to understand the dynamics of user interaction that facilitate the spread of such hateful content. In this paper, we perform the first study that looks into the diffusion dynamics of the posts made by hateful and non-hateful users on Gab (Gab.com). We collect a massive dataset of 341K users with 21M posts and investigate the diffusion of the posts generated by hateful and non-hateful users. We observe that the content generated by the hateful users tend to spread faster, farther and reach a much wider audience as compared to the content generated by normal users. We further analyze the hateful and non-hateful users on the basis of their account and network characteristics. An important finding is that the hateful users are far more densely connected among themselves. Overall, our study provides the first cross-sectional view of how hateful users diffuse hate content in online social media.
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