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arxiv: 2606.02523 · v1 · pith:M4WEUQVUnew · submitted 2026-06-01 · 💻 cs.CL · cs.CV· cs.CY

FigSIM: A Dataset for Fine-grained Suicide Severity and Figurative Language in Suicide Memes

Liuliu Chen , Elise R. Carrotte , Brian E. Chapman , Jo Robinson , Mike Conway This is my paper

Pith reviewed 2026-06-28 15:01 UTC · model grok-4.3

classification 💻 cs.CL cs.CVcs.CY
keywords suicide memesfigurative languageseverity annotationcontent moderationmultimodal modelsdataset creationsocial media analysis
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The pith

FigSIM is the first dataset of 1049 suicide memes annotated for fine-grained severity levels, figurative phenomena, and related content.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces FigSIM to fill the gap in resources for studying suicide memes, which are increasingly common yet poorly understood on social media. Each meme receives labels for suicide severity, figurative elements such as metaphors, and specific suicide-related depictions. Benchmarking 16 models on detection tasks shows that current systems underpredict higher severity levels, with the problem more pronounced for figurative memes. A sympathetic reader would care because the work points to concrete difficulties in building moderation tools that limit exposure to potentially harmful online content.

Core claim

The authors introduce FigSIM, consisting of 1049 memes annotated for fine-grained suicide severity levels, figurative phenomena, and suicide-related content. They benchmark unimodal and multimodal models on three tasks and find that suicide memes pose unique challenges, with analysis revealing biases such as underprediction of higher suicide severity levels, especially for figurative memes.

What carries the argument

The FigSIM dataset with its triple annotations for severity levels, figurative language, and content type, serving as the basis for model benchmarking.

If this is right

  • Automated content moderation systems can now be trained and tested on a dedicated resource for suicide memes.
  • Model performance gaps are larger on figurative memes at higher severity, indicating a need for better handling of non-literal language.
  • The public release of the dataset and its splits supports standardized evaluation of future detection methods.
  • Suicide memes exhibit distinct patterns that require specialized strategies beyond general harmful-content classifiers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Platforms could use severity-stratified thresholds to prioritize review of high-risk figurative examples.
  • Annotation schemes like those in FigSIM might extend to other ambiguous online expressions tied to mental health.
  • Longer-term collection efforts could track whether model biases persist as meme styles evolve.

Load-bearing premise

Human annotations for suicide severity levels and figurative phenomena are reliable and consistent, and the 1049 memes adequately represent the diversity and distribution of suicide memes on social media.

What would settle it

A larger independently collected and annotated collection of suicide memes that produces model predictions without systematic underprediction of high-severity figurative cases would falsify the reported biases.

Figures

Figures reproduced from arXiv: 2606.02523 by Brian E. Chapman, Elise R. Carrotte, Jo Robinson, Liuliu Chen, Mike Conway.

Figure 1
Figure 1. Figure 1: Examples of memes from each annotation category: Figurative Phenomenon (F1–F3), Suicide Severity [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of annotations across all categories in the [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Azure moderation outcomes by suicide sever [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Counts of over- and underprediction for sui [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of memes with image description [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cross-category heatmaps (normalized by row; darker colors indicate higher within-row proportions). [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Over- and underprediction by figurative phe [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Over- and underprediction by suicide-related [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Examples of misclassified cases for suicide severity detection, 7/15 for figurative phenomenon detection, and 8/15 for suicide-related content detection. Additionally, 3/6 image-only memes are misclassified for suicide severity detec￾tion. These results are reported descriptively and suggest increased difficulty when contextual infor￾mation or multimodal cues are limited, however, conclusions are constrain… view at source ↗
read the original abstract

Suicide memes are memes used to express suicide-related thoughts or comment on suicide-related issues. Suicide memes are increasingly common on social media, yet remain poorly understood and potentially harmful. There is an urgent need to better understand their characteristics and to develop appropriate content moderation strategies that limits users' exposure to potentially harmful content. Currently, the absence of annotated datasets of suicide memes remains a key barrier to developing and evaluating automated moderation approaches. In this paper, we introduce FigSIM, the first dataset designed for fine-grained analysis of suicide memes. The dataset consists of 1049 memes, each annotated for (1) fine-grained suicide severity levels, (2) figurative phenomena (e.g., metaphors), and (3) suicide-related content (e.g., suicide method depiction). We benchmark 16 unimodal and multimodal models across three tasks: figurative language, suicide severity, and suicide-related content detection. Overall, FigSIM demonstrates that suicide memes pose unique challenges for both modeling and content moderation. Analysis revealed biases, such as underprediction of higher suicide severity levels, especially for figurative memes. The dataset (including splits used for analyses) is publicly available. Content Warning: This paper contains suicide-related content that may be triggering.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces FigSIM, the first dataset of 1049 suicide memes annotated for fine-grained suicide severity levels, figurative phenomena (e.g., metaphors), and suicide-related content (e.g., method depiction). It benchmarks 16 unimodal and multimodal models on three detection tasks and reports biases including underprediction of higher severity levels, especially for figurative memes. The dataset and splits are released publicly with a content warning.

Significance. If the human annotations are shown to be reliable, FigSIM would fill a documented gap by enabling fine-grained modeling and moderation research on suicide memes. The benchmarking results and bias analysis would then provide concrete evidence of modeling challenges with figurative language. Public release of the data and splits is a clear strength that supports reproducibility.

major comments (2)
  1. [Section 3] Section 3 (Dataset Creation and Annotation): The operationalization of the fine-grained suicide severity scale, annotation guidelines, and inter-annotator agreement statistics are not reported. This directly undermines the load-bearing assumption for the bias claims in Section 5, where model underprediction of higher severity levels (especially on figurative memes) is attributed to model behavior rather than label noise or subjectivity.
  2. [Section 4] Section 4 (Data Collection Pipeline): No details are given on how the 1049-meme sample was sampled from social media or any steps taken to ensure representativeness across platforms, time periods, or meme styles. Without this, the generalization of the observed prediction biases cannot be assessed.
minor comments (2)
  1. [Results tables] Table 2 or equivalent results table: Clarify whether the reported metrics are macro- or micro-averaged and whether statistical significance tests were performed across the 16 models.
  2. [Abstract] Abstract: The phrase 'analysis revealed biases' should briefly indicate the number of models and the specific tasks involved for immediate clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback. We address each major comment point-by-point below and will revise the manuscript to provide the requested details.

read point-by-point responses

  1. Referee: [Section 3] Section 3 (Dataset Creation and Annotation): The operationalization of the fine-grained suicide severity scale, annotation guidelines, and inter-annotator agreement statistics are not reported. This directly undermines the load-bearing assumption for the bias claims in Section 5, where model underprediction of higher severity levels (especially on figurative memes) is attributed to model behavior rather than label noise or subjectivity.

    Authors: We acknowledge that Section 3 lacks explicit operationalization of the severity scale, full annotation guidelines, and IAA statistics. These details are necessary to substantiate the reliability of labels and the interpretation of model biases in Section 5. In the revised manuscript we will expand Section 3 to include: (1) precise definitions and examples for each severity level, (2) key excerpts from the annotation guidelines, and (3) IAA metrics (e.g., Fleiss’ kappa). This addition will directly support the distinction between label noise and model behavior. revision: yes

  2. Referee: [Section 4] Section 4 (Data Collection Pipeline): No details are given on how the 1049-meme sample was sampled from social media or any steps taken to ensure representativeness across platforms, time periods, or meme styles. Without this, the generalization of the observed prediction biases cannot be assessed.

    Authors: We agree that the absence of sampling details in Section 4 prevents assessment of representativeness and generalizability. The manuscript currently omits the specific collection procedures, platform sources, temporal range, and any stratification steps. We will revise Section 4 to document the full data collection pipeline, including selection criteria and diversity considerations, enabling readers to evaluate the scope of the reported biases. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset release and empirical benchmarking with no derivations or self-referential predictions

full rationale

The paper introduces a new annotated dataset (FigSIM) of 1049 memes and reports benchmark results from 16 models on three detection tasks. No mathematical derivations, fitted parameters, or predictions are present; the work consists of data collection, human annotation, and standard model evaluation. No self-citation chains, ansatzes, or uniqueness theorems are invoked to support any claim. The central results (dataset statistics and observed model biases) are direct empirical outputs rather than reductions to prior inputs by construction. This is a standard data-release paper with no internal circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Relies on standard human annotation practices for sensitive content without providing verification details in the abstract; no free parameters or invented entities.

axioms (1)
  • domain assumption Human annotators can reliably assign fine-grained suicide severity and figurative language labels to memes
    Central to dataset creation; invoked implicitly by the annotation process described in the abstract.

pith-pipeline@v0.9.1-grok · 5764 in / 1112 out tokens · 27743 ms · 2026-06-28T15:01:35.938652+00:00 · methodology

discussion (0)

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