Transformer-driven Multi-task Learning for Fake and Hateful Content Detection (2024)

Abstract: Social media has revolutionized communica-tion these days in addition to facilitating thespread of fake and hate content. While fakecontent is the manipulation of facts by disin-formation, hate content is textual violence ordiscrimination targeting a group or an individ-ual. Fake narratives have the potential to spreadhate content making people aggressive or hurt-ing the sentiments of an individual or a group.Further, false narratives often dominate discus-sions on sensitive topics, amplifying harmfulmessages contributing to the rise of hate speech.Hence, understanding the relationship betweenhate speech driven by fake narratives is cru-cial in this digital age making it necessary todevelop automatic tools to identify fake andhate content. In this direction, Decoding FakeNarratives in Spreading Hateful Stories (Faux-Hate) - a shared task organized at the Inter-national Conference on Natural Language Pro-cessing (ICON) 2024, invites researchers totackle both fake and hate detection in socialmedia comments, with additional emphasis onidentifying the target and severity of hatefulspeech. The shared task consists of two sub-tasks - Task A (Identifying fake and hate con-tent) and Task B (Identifying the target andseverity of hateful speech). In this paper, we -team MUCS, describe the models proposed toaddress the challenges of this shared task. Wepropose two models: i) Hing_MTL - a Multi-task Learning (MTL) model implemented us-ing pre-trained Hinglish Bidirectional EncoderRepresentations from Transformers (Hinglish-BERT), and ii) Ensemble_MTL - a MTL modelimplemented by ensembling two pre-trainedmodels (HinglishBERT, and Multilingual Dis-tiled version of BERT (MDistilBERT)), to de-tect fake and hate content and identify the targetand severity of hateful speech. Ensemble_MTLmodel outperformed Hing_MTL model withmacro F1 scores of 0.7589 and 0.5746 for TaskA and Task B respectively, securing 6th placein both subtasks.

## introduction internet has become an integral part of modern society, serving as a primary means of communication and a platform for political and social engagement @xcite . however, the rise of digital communication has also posed significant challenges, particularly the spread of fake news, hate speech, and extremist content, misusing the anonymity of the users @xcite . extremist groups have increasingly used the internet to disseminate their propaganda, deepening societal divides and polarizing communities. recent studies @xcite have highlighted the connection between fake narratives and the spread of hate content on social media platforms. from this report, it is clear that a large portion of the hateful discourse circulating online is based on false information. this trend becomes especially pronounced during crises like disease outbreaks, communal violence, or public protests, where misinformation can rapidly escalate the situation. a careful analysis of fake narratives and hate speech reveals the possibility of fake stories spreading hatred reactions which has the potential to exert a profound impact on society @xcite . this warrants the pressing need for researchers to examine the relationship between fake narratives and hate speech. the research attempts in this direction can prevent the spread of hate speech driven by fake narratives keeping the social media ecosystem healthy. to address this issue, "decoding fake narratives in spreading hateful stories (faux-hate)" shared task foot_0 organized at icon 2024, focuses on tackling fake and hate detection in social media comments, with additional emphasis on identifying the target and severity of hate speech @xcite . the organizers have provided hinglish dataset to assist in the analysis and identification of fake narratives that spread hatred on social media platforms. this shared task includes two sub-tasks: i) task a -"binary faux-hate detection," involves identifying fake news and hate content simultaneously and ii) task b -"target and severity prediction" -involves identifying target of the content (individual (i), organization (o), and religion (r)) and also severity of the content (low (l), medium (m), and high (h)), in the given hinglish social media text. the objective of this shared task is to encourage researchers to develop a single model for each task. hence, we model these subtasks as mtl problems, where a single model is capable of performing more than one task simultaneously. with this view, weteam mucs, proposed two models: i) hing_mtl -a mtl system implemented using pre-trained hinglishbert model and ii) ensemble_mtl -a mtl model implemented by ensembling two pretrained models (hinglishbert and mdistlbert), to accomplish the objectives of the shared task. rest of the paper is organized as follows: related works are described in section 2 and the proposed methodology is discussed in section 3. experiments and results are discussed in section 4 and the paper concludes in section 5 with future scopes for mtl models. ## related work until the last decade, fake news and hate speech detection were typically approached as separate binary or multi-class classification tasks @xcite . developing multiple single task models was expensive in low-resource settings. further, training single task models with small data often lead to overfitting resulting in limited generalization. this has led the researchers to develop mtl -a single model capable of addressing multiple problems simultaneously @xcite . few of the relevant works in this field are described below: mtl models for fake news detection - @xcite presented sifted mtl for stance and fake news detection tasks using keras embeddings and selected sharing of long short term memory (lstm) and gated recurrent unit (gru) layers. the selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flow between the tasks respectively. exper-imenting on publicly available rumoureval foot_1 and pheme foot_2 datasets, they obtained macro f1 scores of 0.7865 and 0.8009 for stance and fake news detection tasks respectively for rumoureval. further, they achieved macro f1 scores of 0.8148 and 0.8127 for stance and fake news detection tasks respectively using pheme dataset. an integrated mtl model to perform fake news detection and news topic classification is proposed by @xcite . to extract semantic and contextual relationships among news and news topics, the authors proposed a novel news graph that dynamically balances the tasks with a dynamic weighting strategy for the classification. they used liar foot_3 dataset to train their proposed model and obtained macro f1 scores of 0.516 and 0.645 for fake news detection and news topic classification tasks respectively. cui and yang (2022) introduced a multi-modal mtl model for fake news detection task considering evidence veracity classification as an auxiliary task. their proposed approach makes use of a convolutional neural network (cnn) to extract image features and a bert encoder for textual claims and their associated evidence. evidence veracity classification is incorporated as an auxiliary task to enhance the primary fake news detection task, sharing learned evidence representations across both the tasks. a co-attention mechanism is employed to fuse visual and textual evidence representations. using ccmr foot_4 dataset for their experiments, they obtained macro f1 scores of 0.942 and 0.822 for fake news detection and evidence veracity tasks receptively. mtl models for hate speech detection -a deep mtl model is discussed by @xcite to perform multiple related tasks -hate speech, racism, sexism, and offensive language classification. in order to leverage the benefits of multiple related tasks, they employed a shared private mtl framework and implemented a deep mtl model stacking cnn, cnn with attention, and gru layers and conducted experiments on five datasets. further, they concatenated word2vec 6 embeddings and character embeddings to train their proposed models. these models consistently performed well over the single task models with macro f1 scores of 0.8916, 0.9115, 0.8612, 0.9241, and 0.8535, for the detection of hate speech, offensive language, racism, communal language, and harassment content respectively. plaza-del-arco et al. ( 2021 ) presented a novel study on a transformerbased mtl approach that leverages the shared affective knowledge to detect hate speech in spanish tweets, integrating sentiment analysis and emotion analysis. the authors used transformer-based beto model -a spanish version of bert model, and obtained macro f1 scores of 0.8551, 0.8603, and 0.8679, for hate speech detection, sentiment analysis, and emotion analysis tasks respectively. a hindi hate speech detection dataset is created by @xcite , employing a novel hierarchical four-layer annotation scheme. the first layer addresses the binary classification of hate or not-hate and the second layer is for explicit and implicit hate. while the third layer performs multi-label classification task, the fourth layer is for named entity tagging, for the given text. using this dataset, the authors implemented mtl models using multilingual representations for indian languages (muril) and multilingual bert pretrained transformer models for hate speech detection. their mtl frame work with muril outperformed the single-task model with a weighted f1 score of 0.912 for hate speech detection. in summary, models trained within the mtl framework outperform single-task models, with transformer-based architectures showing promising results. this highlights the suitability of transformer models for building effective mtl systems. further, the limited exploration of mtl models for fake news and hate speech detection in hinglish text underscores a significant opportunity for future research in this area. ## methodology mtl is a machine learning (ml) technique in which a model is trained to perform multiple related tasks simultaneously, sharing certain layers and parameters in the network across different tasks. this approach improves model performance, especially when there is insufficient data for individual but related tasks @xcite . mtl leverages shared lower-level features across tasks while allowing the model to learn higher-level features specific to each task. using a unified framework, the model benefits from shared representations, which enhances its ability to generalize and make accurate predictions @xcite . the objectives of this study are to perform two tasks: i) task a -binary classification using a single mtl model to identify fake news and hate speech content simulataneously, and ii) task bmulti-class classification using a single mtl model for target and severity detection. to achieve these two subtasks, two distinct models -hing_mtl and ensemble_mtl, are proposed and descriptions of these models are given in the following subsections. ## conclusion and future work in this paper, we -team mucs describe the two distinct models submitted to 'faux-hate' shared task organized at icon 2024. experiments are carried out with: i) hing_mtl -a mtl model implemented using the pre-trained hinglishbert model, and ii) ensemble_mtl -a mtl model that combines hinglishbert and mdistilbert, to detect fake and hate content with additional emphasis on identifying the target and severity of hate speech. the proposed ensemble_mtl model outperformed the hing_mtl model, achieving macro f1 scores of 0.7589 and 0.5746 for task a and task b, respectively, securing 6 th rank for both subtasks in the shared task. these results demonstrate the effectiveness of the ensemble approach in improving model performance by leveraging the strengths of multiple pre-trained models. optimization of the ensemble method by integrating additional models and utilizing mtl framework for better generalization across diverse datasets and languages will be explored further.