| Challenge: | Mental health disorders are a major economic burden for society and are projected to rise to a staggering US $6 trillion by 2030. |
| Approach: | They propose to use memes to identify fine-grained depression symptoms from memes . they benchmark RESTORE on 20 strong monomodal and multimodal methods . |
| Outcome: | The proposed method can predict fine-grained depression symptoms better than existing models that overlook implicit connections between visual and textual elements of a meme. |
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| Challenge: | Currently, most studies focus on a binary classification setup or on pre-established resources. |
| Approach: | They evaluated machine learning techniques to model 21 depression signs in social media posts from Brazilian undergraduate students. |
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Detecting Depression in Social Media using Fine-Grained Emotions (N19-1)
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| Challenge: | Mental disorders affect millions of people around the world and depression is among the most common. |
| Approach: | They propose a representation of social media documents by a set of emotions generated by lexical resources and subword embeddings. |
| Outcome: | The proposed representation improves the results of the evaluation based on the core emotions and the state-of-the-art representations compared to the current methods. |
Inferring Social Media Users’ Mental Health Status from Multimodal Information (2020.lrec-1)
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| Challenge: | In the United States alone, one in every four adults suffers from a mental health condition, making mental health a pressing concern. |
| Approach: | They propose to use multimodal cues present in social media posts to predict mental health status by analyzing language, visual, and metadata cue data. |
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FigSIM: A Dataset for Fine-grained Suicide Severity and Figurative Language in Suicide Memes (2026.findings-acl)
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| Challenge: | Suicide memes are increasingly common on social media, yet remain poorly understood and potentially harmful. |
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Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study (2024.lrec-main)
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| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
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Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion . |
| Approach: | They propose to use large language models to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning. |
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MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
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Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
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Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
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| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
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Do Models of Mental Health Based on Social Media Data Generalize? (2020.findings-emnlp)
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| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
| Approach: | They explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. |
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Detecting Harmful Memes and Their Targets (2021.findings-acl)
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Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing body of research on meme analysis has focused on detecting harmful memes and their social entities . a meme is a form of content that is often harmless and designed to look funny . but its multimodal nature and camouflaged semantics make its analysis challenging . |
| Approach: | They propose to use multimodal models to detect harmful memes and identify social entities that harmful meme targets. |
| Outcome: | The proposed model can detect harmful memes and the social entities they target . the proposed model lacks the appropriate contexts and is poorly validated . |