@inproceedings{singh-etal-2023-standardizing,
title = "Standardizing Distress Analysis: Emotion-Driven Distress Identification and Cause Extraction ({DICE}) in Multimodal Online Posts",
author = "Singh, Gopendra and
Ghosh, Soumitra and
Verma, Atul and
Painkra, Chetna and
Ekbal, Asif",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.275",
doi = "10.18653/v1/2023.emnlp-main.275",
pages = "4517--4532",
abstract = "Due to its growing impact on public opinion, hate speech on social media has garnered increased attention. While automated methods for identifying hate speech have been presented in the past, they have mostly been limited to analyzing textual content. The interpretability of such models has received very little attention, despite the social and legal consequences of erroneous predictions. In this work, we present a novel problem of \textit{Distress Identification and Cause Extraction (DICE)} from multimodal online posts. We develop a multi-task deep framework for the simultaneous detection of distress content and identify connected causal phrases from the text using emotional information. The emotional information is incorporated into the training process using a zero-shot strategy, and a novel mechanism is devised to fuse the features from the multimodal inputs. Furthermore, we introduce the first-of-its-kind \textit{Distress and Cause annotated Multimodal (DCaM)} dataset of 20,764 social media posts. We thoroughly evaluate our proposed method by comparing it to several existing benchmarks. Empirical assessment and comprehensive qualitative analysis demonstrate that our proposed method works well on distress detection and cause extraction tasks, improving F1 and ROS scores by 1.95{\%} and 3{\%}, respectively, relative to the best-performing baseline. The code and the dataset can be accessed from the following link: \url{https://www.iitp.ac.in/~ai-nlp-ml/resources.html\#DICE}.",
}
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<abstract>Due to its growing impact on public opinion, hate speech on social media has garnered increased attention. While automated methods for identifying hate speech have been presented in the past, they have mostly been limited to analyzing textual content. The interpretability of such models has received very little attention, despite the social and legal consequences of erroneous predictions. In this work, we present a novel problem of Distress Identification and Cause Extraction (DICE) from multimodal online posts. We develop a multi-task deep framework for the simultaneous detection of distress content and identify connected causal phrases from the text using emotional information. The emotional information is incorporated into the training process using a zero-shot strategy, and a novel mechanism is devised to fuse the features from the multimodal inputs. Furthermore, we introduce the first-of-its-kind Distress and Cause annotated Multimodal (DCaM) dataset of 20,764 social media posts. We thoroughly evaluate our proposed method by comparing it to several existing benchmarks. Empirical assessment and comprehensive qualitative analysis demonstrate that our proposed method works well on distress detection and cause extraction tasks, improving F1 and ROS scores by 1.95% and 3%, respectively, relative to the best-performing baseline. The code and the dataset can be accessed from the following link: https://www.iitp.ac.in/ ai-nlp-ml/resources.html#DICE.</abstract>
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%0 Conference Proceedings
%T Standardizing Distress Analysis: Emotion-Driven Distress Identification and Cause Extraction (DICE) in Multimodal Online Posts
%A Singh, Gopendra
%A Ghosh, Soumitra
%A Verma, Atul
%A Painkra, Chetna
%A Ekbal, Asif
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F singh-etal-2023-standardizing
%X Due to its growing impact on public opinion, hate speech on social media has garnered increased attention. While automated methods for identifying hate speech have been presented in the past, they have mostly been limited to analyzing textual content. The interpretability of such models has received very little attention, despite the social and legal consequences of erroneous predictions. In this work, we present a novel problem of Distress Identification and Cause Extraction (DICE) from multimodal online posts. We develop a multi-task deep framework for the simultaneous detection of distress content and identify connected causal phrases from the text using emotional information. The emotional information is incorporated into the training process using a zero-shot strategy, and a novel mechanism is devised to fuse the features from the multimodal inputs. Furthermore, we introduce the first-of-its-kind Distress and Cause annotated Multimodal (DCaM) dataset of 20,764 social media posts. We thoroughly evaluate our proposed method by comparing it to several existing benchmarks. Empirical assessment and comprehensive qualitative analysis demonstrate that our proposed method works well on distress detection and cause extraction tasks, improving F1 and ROS scores by 1.95% and 3%, respectively, relative to the best-performing baseline. The code and the dataset can be accessed from the following link: https://www.iitp.ac.in/ ai-nlp-ml/resources.html#DICE.
%R 10.18653/v1/2023.emnlp-main.275
%U https://aclanthology.org/2023.emnlp-main.275
%U https://doi.org/10.18653/v1/2023.emnlp-main.275
%P 4517-4532
Markdown (Informal)
[Standardizing Distress Analysis: Emotion-Driven Distress Identification and Cause Extraction (DICE) in Multimodal Online Posts](https://aclanthology.org/2023.emnlp-main.275) (Singh et al., EMNLP 2023)
ACL