Standardizing Distress Analysis: Emotion-Driven Distress Identification and Cause Extraction ( DICE ) in Multimodal Online Posts (2023)

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 .

## introduction the exponential expansion of microblogging sites and social media not only empowers free expression and individual voices, but also allows individuals to exhibit anti-social conduct @xcite , such as cyberbullying, online rumours, and [*] these authors contributed equally to this work and are the joint first authors. spreading hate remarks @xcite . abusive speech based on race, religion, and sexual orientation is becoming more common @xcite . automatic identification of hate speech and raising public awareness are critical tasks @xcite . manually evaluating and validating a large volume of web information, on the other hand, is time-consuming and labor-intensive. modern language models excel over traditional machine learning and neural network-based approaches but lack transparency in output transformation, posing limitations in domains, such as the military, medical research, and internet content monitoring. robust models for monitoring distressed content online require multimodal inputs. in our "dcam" dataset, figure 1 highlights the significance of multimodality and span annotations in comprehending distress content. while both posts are labeled as "distressed," the first post may not offer sufficient information based on textual content alone. however, the second post, with both picture and text, provides clarity, and the span annotation aids in analyzing the manifestation of distress. this necessitates a shift in viewpoint away from performance-based models and toward interpretable models. we address model explainability by jointly learning the target classification of a multimodal social media post as distressed or non-distressed and extracting the reasons for the classification decision (for the distressed class) from the textual input. the prime focus of this study is to comprehend the causes associated with any form of offensive content @xcite . we club all the connotations of offensive content under the category distressed. the main contributions are summarized below: ## related work several approaches have been suggested to identify online hate speech @xcite @xcite . the current interest in hate speech research has led to the availability of datasets in several languages @xcite and different computational ways to counteract online hate @xcite . text-, user-, and network-based traits and characteristics that identify bullies have been extracted in @xcite . deep learning lundberg and lee (2017); @xcite has been used extensively to identify hate speech keyword identification, sexism, bullying, trolling, and racism. recent research on identifying hate speech has made use of deep learning techniques, including neural networks @xcite and word embedding techniques @xcite . recent models based on transformers @xcite have had extraordinary success. since this is essentially a classification problem, bert (bidirectional encoder representations from transformers) @xcite has found widespread use in the field of hate speech identification. @xcite showed that a bert-based model performed better than models based on recurrent neural networks (rnns). @xcite first proposed the use of rationales, where human annotators highlight text that supports their classification decision. this work was enhanced by @xcite to provide self-generating rationales. an encodergenerator system for quality rationales without annotations was presented in @xcite . @xcite used dataset rationales to fine-tune bert to address bias and explainability. recent research has shifted towards accommodating multimodal content, with a focus on detecting hate speech and objectionable material in various media. @xcite developed a computer vision-based technique for identifying offensive and non-offensive images in large datasets. @xcite introduced a novel challenge for multimodal hate speech detection in facebook memes. @xcite employed the hate speech recognition video dataset to identify emotion-based hate speech in a multimodal context. @xcite presented a dataset for detecting hate speech in bengali memes and text. @xcite discussed semeval-2022 task 5, focusing on identifying misogynous memes through text and images, including sub-tasks for recognizing misogynous content and categorizing types of misogyny. hee et al. (2022) investigated multimodal hateful meme detection models and their ability to capture derogatory references in both images and text. additionally, @xcite introduced prompthate, a model that leverages pretrained language models with specific prompts and examples for hateful meme classification. ## dataset we discuss the data collection and annotation details in the following subsections. ## methodology in this section, we illustrate our proposed dice framework, which is a multitask system for depression identification and cause extraction from multimodal social media posts. the system employs a zero-shot strategy to dynamically incorporate emotional information into training and presents a novel fusion mechanism to infuse the features from the multimodal inputs. the overall architecture of the proposed method is shown in figure 3a . ## experiments and results this section discusses the results and the analysis. due to space constraints, we discuss the experimental setup in section a.3 and the evaluation metrics in section a.5.1 in the appendix. ## conclusion in this work, we present a novel problem of distress identification and cause extraction (dice) from multimodal online posts. we develop a multitask, deep framework for detecting distress content and identifying associated causal phrases from text using emotional information. we devise a zero-shot strategy to dynamically incorporate emotional information into training and propose a novel fusion mechanism to infuse the features of multimodal inputs. furthermore, we introduce the first distress and cause annotated multimodal (dcam) corpus, consisting of over 20,764 social media posts. we illustrate the effectiveness of our method by comparing it to several state-of-the-art baselines. when compared to human performance, the present stateof-the-art models perform poorly, which serves to emphasize the difficulty of the task at hand. we believe our work will advance multimodal reasoning and comprehension while also assisting in the resolution of a significant real-world problem.