@inproceedings{d-joy-srivastava-2024-rejected,
title = "Rejected Cookies @ Decoding Faux-Hate: Predicting Fake Narratives and Hateful Content",
author = "D Joy, Joel and
Srivastava, Naman",
editor = "Biradar, Shankar and
Reddy, Kasu Sai Kartheek and
Saumya, Sunil and
Akhtar, Md. Shad",
booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)",
month = dec,
year = "2024",
address = "AU-KBC Research Centre, Chennai, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2024.icon-fauxhate.7/",
pages = "36--39",
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 ."
}
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<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 .</abstract>
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%0 Conference Proceedings
%T Rejected Cookies @ Decoding Faux-Hate: Predicting Fake Narratives and Hateful Content
%A D Joy, Joel
%A Srivastava, Naman
%Y Biradar, Shankar
%Y Reddy, Kasu Sai Kartheek
%Y Saumya, Sunil
%Y Akhtar, Md. Shad
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F d-joy-srivastava-2024-rejected
%X 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 .
%U https://aclanthology.org/2024.icon-fauxhate.7/
%P 36-39
Markdown (Informal)
[Rejected Cookies @ Decoding Faux-Hate: Predicting Fake Narratives and Hateful Content](https://aclanthology.org/2024.icon-fauxhate.7/) (D Joy & Srivastava, ICON 2024)
ACL