mlsanityData to 14 Oct 2026

MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes

arXiv 2512.25015 ↗First posted 31 Dec 2025cs.CLcited by 1

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Abstract (arXiv)

Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive symptoms exhibited by memes shared by users of online social media platforms. We introduce RESTOREx as a vital resource for detecting depressive symptoms in memes on social media through the Large Language Model (LLM) generated and human-annotated explanations. We introduce MAMAMemeia, a collaborative multi-agent multi-aspect discussion framework grounded in the clinical psychology method of Cognitive Analytic Therapy (CAT) Competencies. MAMAMemeia improves upon the current state-of-the-art by 7.55% in macro-F1 and is established as the new benchmark compared to over 30 methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 31 Dec 2025.

Builds on

23 of 47 references.

References · 23

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  11. 2020The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memescited by 490
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11 more
  1. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 161
  2. 2017Depression and Self-Harm Risk Assessment in Online Forumscited by 84
  3. 2020SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!cited by 78
  4. 2021Detecting Harmful Memes and Their Targetscited by 78
  5. 2017Multi-Task Learning for Mental Health using Social Media Textcited by 25
  6. 2020Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Frameworkcited by 25
  7. 2022What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memescited by 18
  8. 2024Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanationscited by 13
  9. 2023Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memescited by 12
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  11. 2020"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extractioncited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitismcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification
  2. 2025DepressLLM: Interpretable domain-adapted language model for depression detection from real-world narratives
  3. 2026SynSym: A Synthetic Data Generation Framework for Psychiatric Symptom Identification
  4. 2026Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement
  5. 2025DS@GT at eRisk 2025: From prompts to predictions, benchmarking early depression detection with conversational agent based assessments and temporal attention models
  6. 2025Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
  7. 2024They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models
  8. 2025Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents