Rejected Cookies @ Decoding Faux-Hate: Predicting Fake Narratives and Hateful Content (2024)

Abstract: This paper reports the results of our team for theICON 2024 shared task Decoding Fake Narra-tives in Spreading Hateful Stories (Faux-Hate).The task aims at classifying tweets in a multi-label and multi-class framework. It comprisestwo subtasks: (A) Binary Faux-Hate Detec-tion, which involves predicting whether a tweetis fake (1/0) and/or hate speech (1/0, and (B)Target and Severity Prediction, which cate-gorizes tweets based on their target (Individ-ual, Organization, Religion) and severity (Low,Medium, High). We evaluated Machine Learn-ing (ML) approaches, including Logistic Re-gression, Support Vector Machines (SVM), andRandom Forest; Deep Learning (DL) methods,such as Artificial Neural Networks (ANN) andBidirectional Encoder Representations fromTransformers (BERT); and innovative quantumhybrid models, like Hybrid Quantum NeuralNetworks (HQNN), for identifying and classi-fying tweets across these subtasks. Our exper-iments trained and compared multiple modelarchitectures to assess their comparative per-formance and detection capabilities in these di-verse modeling strategies.The best-performingmodels achieved F1 scores of 0.72, 0.76, 0.64,and 0.54 for the respective labels Hate, Fake,Target and Severity. We have open-sourced ourimplementation code for both tasks on Github1 .

## introduction the digital era has revolutionized communication through social networks, enabling unprecedented connectivity and information sharing. however, this transformation has also brought significant challenges, notably the rise of hate speech and fake news, which threaten the health of online discourse and social cohesion. hate speech, which targets individuals or groups based on characteristics like race, religion, or gender, promotes discrimination, causes emotional harm, and can even lead to violence. the anonymity provided by online platforms worsens the problem, spreading harmful messages, marginalizing vulnerable groups, and fostering hostile environments. similarly, fake news, which involves false or misleading information, erodes trust, deepens divisions, and disrupts democratic systems. amplified by the viral nature of social networks, misinformation can lead to social unrest and erode public confidence in institutions. addressing these issues requires advanced detection methods, including natural language processing, machine learning, and collaborative efforts between tech companies, researchers, and civil organizations. detection mechanisms must also balance free speech with harm mitigation, incorporating cultural and linguistic nuances. there have been various approaches to utilising machine learning and deep learning to detect hate speech and fake news. these include machine learning models such as support vector machine (svm), random forest and logistic regression @xcite @xcite and deep learning models such as artificial neural networks, long-short-term memory networks (lstm) @xcite , bidirectional encoder representations from transformers (bert) @xcite @xcite @xcite @xcite . this study aims to test these machine learning and deep learning models for the faux-hate @xcite ) shared task to predict hate speech, fake news (task-a), hate target, and severity prediction (task-b). we further explore quantum-hybrid neural networks for these tasks. 2 task description and dataset overview 2.1 task description the faux-hate shared task @xcite aims to address the dual challenge of detecting fake and hate content in social media comments, while also identifying the target and severity of hateful speech. the task is divided into two distinct subtasks: task a -binary faux-hate detection this subtask requires participants to develop a multi-tasking model capable of simultaneously predicting two binary labels for each text sample: (1) fake, which indicates whether the content is fake (1) or real (0), and (2) hate, which determines whether the content constitutes hate speech (1) or not (0). the objective is to enhance the efficiency and accuracy of models by addressing these related tasks jointly. ## methodology we followed the traditional stages of a machine learning experiment illustrated in fig. 1 which includes preprocessing, model training, and evaluation. ## results and discussions in our experiments, we compared various methods for generating word representations, each with different strengths in capturing semantic relationships within textual data. word2vec embeddings outperformed tf-idf in generating meaningful word representations. bert (bidirectional encoder representations from transformers) demonstrated superior performance compared to both word2vec based ml models as shown in ## conclusion this paper describes various approaches used in our approach for the shared task on decoding fake narratives in spreading hateful stories (faux-hate) dataset. we investigated the effectiveness of different embedding techniques, demonstrating that word2vec embeddings outperformed tf-idf in capturing semantic relationships. furthermore, bert-based models achieved superior performance, leveraging their contextual understanding to outperform other approaches. it is interesting to note that even though ann has good learning capabilities, it could not outperform the machine learning models. similarly, hybrid quantum neural networks (hqnn) showed capabilities similar to ann, they could not surpass the results obtained by the best-performing bert-based models. future work will focus on refining these models and exploring their applicability to similar tasks.