---
title: Conflict Attribution Corpus (CAC)
url: https://www.emergentmind.com/topics/conflict-attribution-corpus-cac
type: topic
---

# Conflict Attribution Corpus (CAC)

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The **Conflict Attribution Corpus (CAC)** denotes two distinct research corpora that use the same acronym in different domains. In computational social science, CAC is a Reddit-based dataset centered on the AITA (“Am I the Asshole”) subreddit and was introduced to study interpersonal conflict, conflict typology, and perception classification of **YTA** versus **NTA** verdicts [2208.08758]. In multimodal misinformation detection, CAC is the central supervision resource of the CORE framework and is defined as a corpus of image-text pairs annotated with explicit contradictory content and its sources, so that multimodal large language models can learn **conflict-oriented reasoning** rather than manipulation-specific heuristics [2606.03066]. The shared term “conflict attribution” therefore refers not to a single canonical dataset, but to two independently motivated annotation schemes that make latent conflict structure explicit for downstream modeling.

## 1. Interpersonal-conflict CAC in AITA research

The interpersonal-conflict CAC was introduced as a new Reddit-based resource for studying how people perceive and judge interpersonal conflict, and it is built around the AITA subreddit, where an original poster describes a conflict and community members judge whether the author’s behavior was wrong or not [2208.08758]. Its stated contribution is twofold: it adds a **conflict-typology annotation layer** grounded in interpersonal conflict literature, and it supports experiments on **perception classification**, defined as predicting whether a verdict writer will judge the author as **YTA** (“you’re the asshole”) or **NTA** (“not the asshole”) [2208.08758].

The corpus was created because conflict judgments are deeply tied to **social norms**, but those norms are not uniform, and existing computational work on AITA mainly modeled verdict prediction without explicitly connecting posts to conflict theory [2208.08758]. CAC was designed to bridge that gap by providing a **large AITA corpus** for modeling verdicts, a **manually annotated subset** with conflict-aspect labels derived from conflict psychology and organizational conflict literature, **automatically induced clusters** of conflict situations that were human-validated as meaningful groupings, and analysis of how conflict type and social relation affect model performance and human judgments [2208.08758].

The raw dataset consists of **21K posts** and **364K verdict comments**, including **254K NTA** and **110K YTA**, and all data are **in English** [2208.08758]. For modeling, the resource uses the **post title (“situation”)** and/or the **full post text**, and the released code and dataset include the 21K posts, 364K comments, **two sets of cluster labels**, and **500 posts annotated with six conflict aspects**, corresponding to **1,653 verdicts** [2208.08758].

This design makes CAC more than a verdict-prediction benchmark. It is a corpus intended to connect computational modeling of moral judgment to structured conflict theory, with the explicit goal of explaining not just whether a comment is judged YTA or NTA, but why certain conflict scenarios are easier or harder to judge [2208.08758].

## 2. Annotation scheme and theoretical grounding

The annotation scheme of the AITA-based CAC is explicitly derived from prior conflict theory. The paper cites **Barki & Hartwick (2004)**, who characterize interpersonal conflict in terms of **disagreement**, **negative emotion**, and **interference**; **Bendersky et al. (2014)**, who add **duration** and **manifestation vs. perception**; and **Korsgaard et al. (2008)**, who motivate **number of people involved / group-level conflict** [2208.08758]. These were refined into a **six-aspect conflict annotation scheme**:

1. **Strength of disagreement**  
2. **Intensity of negative emotion**  
3. **Degree of interference**  
4. **Duration of conflict**  
5. **Manifestation vs. perception**  
6. **Number of people involved** [2208.08758]

Some of the annotation questions originally had 3-way scales, but for analysis the authors **merged labels into binary categories** when needed [2208.08758]. For **disagreement strength** and **negative emotion intensity**, **Strong + Intense → Strong**. For **degree of interference**, **Not at all + Somewhat → Mild**, while **Strongly → Strong**. **Duration** is annotated as **One-time incident** or **Longer**. **Manifestation vs. perception** distinguishes **Manifest** conflicts, expressed in observable actions or speech, from **Perceived** conflicts that exist mainly in someone’s mind. **Number of people involved** is labeled **One person** or **Multiple people** [2208.08758].

The annotation procedure began with **25 posts** used to refine the scheme because existing conflict scales were found not well suited to AITA-style conflicts: not all conflicts are workplace conflicts, distinctions such as “friction vs. tension vs. emotional conflict” were too fine-grained for the setting, and long-term conflict is often hard to infer from short narratives [2208.08758]. The larger annotation phase covered **500 posts** and **1,653 comments/verdicts**, using **14 annotators total**, recruited via **Prolific** and university researchers; all had English fluency and all passed **two attention checks** in every survey [2208.08758].

Inter-annotator agreement was measured with **Matthews correlation coefficient (MCC)**. Reported MCCs were **0.39 → 0.49** after label merging for disagreement strength, **0.33 → 0.41** for emotion intensity, **0.13 → 0.20** for interference degree, **0.39** for conflict duration, **0.10** for manifestation or perception, and **0.40** for number of people [2208.08758]. The strongest agreement was for disagreement, duration, and number of people, while the weakest was for manifestation vs. perception and interference [2208.08758]. This indicates that some conflict dimensions are operationally easier to identify in short social narratives than others.

The merged label distribution was reported as **33.0% Mild, 67.0% Strong** for disagreement; **35.7% Mild, 64.3% Strong** for emotion; **35.3% Mild, 64.7% Strong** for interference; **48.3% Once, 51.7% Longer** for duration; **33.7% Perceived, 66.3% Manifest** for manifestation; and **72.0% One, 28.0% More** for number of people [2208.08758]. A plausible implication is that the corpus is skewed toward relatively strong, explicit, and dyadic conflicts, which is consistent with the narrative style of AITA posts, but that interpretation remains secondary to the reported distributions themselves.

## 3. Clustering and relationship-structured conflict types

Before manual annotation, the AITA-based CAC explored whether AITA conflicts naturally group into types [2208.08758]. The authors used two text representations: **situations**, defined as the post title, and **full text**, defined as the entire post body [2208.08758]. They computed **Sentence-BERT (SBERT)** embeddings, pairwise **cosine similarity** normalized to \([0,1]\), formed a fully connected weighted graph, and applied **Louvain clustering** to maximize modularity [2208.08758].

Weak edges were pruned by dropping the bottom **N%** of edge weights, where \(N\) was chosen based on stability measured by **adjusted Rand index (ARI)** across nearby thresholds [2208.08758]. The chosen pruning levels were **40%** for situations and **30%** for full text. This yielded **3 clusters for situations** and **3 clusters for full text**, after removing one tiny 4th cluster from the 30% full-text cutoff because it had only 25 posts [2208.08758].

Manual inspection and human validation showed that the clusters mainly reflect the **social relation of the author to the other participant(s)**: **Family**, **Close relationships**, and **Distant relationships** [2208.08758]. The authors emphasize that the subject of the conflict is strongly entangled with relationship closeness, and that relation boundaries themselves can be ambiguous [2208.08758]. In human validation, two authors manually clustered 100 posts and found disagreement sources in how to draw boundaries between close relationship types and in ambiguity about who is actually involved in the conflict [2208.08758]. Reported ARI values were **0.33 between the two humans**, **0.38 and 0.15 between full-text clusters and humans**, and **0.31 and 0.13 between situation clusters and humans** [2208.08758]. The clustering is therefore only moderately aligned with manual labels, but the categories were still described as meaningful and analytically useful [2208.08758].

This relationship-structured interpretation is central to the corpus’s analytical use. The paper’s main takeaway is that conflict content varies systematically with social relationship, and that this affects both how humans judge a situation and how well a model can predict those judgments [2208.08758]. The reported gradient is clear: **family conflicts** are easiest to model, **close relationships** are intermediate, and **distant relationships** are hardest [2208.08758]. The authors interpret this as an **indirect relationship between relational closeness and prediction difficulty**, with closer relationships tending to produce richer, more explicit conflict narratives that are easier for the model to classify [2208.08758].

## 4. Perception classification and empirical findings

The core modeling task in the AITA-based CAC is to predict whether an observer judges the author’s action as **right or wrong**, operationalized as **NTA vs. YTA** [2208.08758]. The model operates on the **comment text together with the situation title** by concatenating **situation + comment**, after removing explicit verdict labels from the comment text [2208.08758]. The main model is a **fine-tuned SBERT classifier**, and the authors also report that adding a representation of the full text as extra features did **not improve performance** over using the comment plus situation alone [2208.08758]. The baseline is **JudgeBERT** from Botzer et al. (2022), a **BERT-base** classifier with dropout and a classification layer, re-implemented on this dataset [2208.08758]. Training used **10 epochs**, **Adam**, learning rate **1e-4**, and **focal loss** to cope with imbalance between YTA and NTA [2208.08758].

The data for perception classification were split **70/20/10** into **train/validation/test**, stratified by **situation clusters** and by **full-text clusters** [2208.08758]. The annotated subset consists of **500 posts** from the **test set**, with **1,653 comments/verdicts** annotated for conflict aspects [2208.08758].

The SBERT-based approach outperformed JudgeBERT on both stratifications. Under **full text stratification**, **JudgeBERT** achieved **72.7 F1, 84.9 Acc**, while the proposed model achieved **77.2 F1, 87.0 Acc**. Under **situation stratification**, **JudgeBERT** achieved **70.1 F1, 83.2 Acc**, while the proposed model achieved **77.4 F1, 87.2 Acc** [2208.08758]. The paper reports that the improvement is statistically significant by permutation test, **p < 0.0001**, with about **+5 F1** on full text and about **+7 F1** on situations [2208.08758].

Using full-text clusters, the model also outperformed JudgeBERT in all three relationship clusters: **Family** (**74.9 F1 / 86.8 Acc** vs. **79.0 F1 / 88.3 Acc**), **Close** (**72.2 F1 / 84.4 Acc** vs. **76.7 F1 / 86.9 Acc**), and **Distant** (**71.2 F1 / 82.2 Acc** vs. **75.9 F1 / 85.0 Acc**) [2208.08758]. The situation-stratified numbers show the same pattern, with best performance on Family, lower on Close, and lowest on Distant [2208.08758].

The paper’s most distinctive analysis compares model performance across the six conflict dimensions. The reported trends are that **more negative emotion**, **stronger disagreement**, and **more people involved** make classification harder, while **stronger interference**, **longer duration**, and **more manifest conflicts** make classification easier [2208.08758]. Under full-text stratification, the reported aspect-level results were:

| Aspect | Condition | Reported results |
|---|---|---|
| Disagreement | Mild | 89.5 Acc / 70.8 micro F1 / 78.0 macro F1 |
| Disagreement | Strong | 88.3 Acc / 69.5 micro F1 / 76.4 macro F1 |
| Emotion | Mild | 88.3 / 70.0 / 77.8 |
| Emotion | Strong | 84.0 / 69.6 / 76.6 |
| Interference | Weak | 84.7 / 56.4 / 74.5 |
| Interference | Strong | 86.3 / 85.5 / 85.5 |
| Duration | Once | 82.7 / 68.2 / 71.7 |
| Duration | Longer | 86.5 / 70.7 / 82.0 |
| Manifestation | Perceived | 81.8 / 51.9 / 73.2 |
| Manifestation | Manifest | 86.4 / 73.7 / 78.9 |
| Number of people | One | 86.1 / 73.1 / 78.5 |
| Number of people | More | 80.0 / 42.5 / 72.7 |

Most dyads differed significantly under **one-sided unpaired permutation tests**, except interference, where improvement was not statistically significant in the model comparison [2208.08758]. In the appendix, the authors also analyze how verdict distributions vary across conflict dimensions using **Fisher’s exact test**. They report that **Strong disagreement** has an **11%** higher YTA/NTA ratio difference than mild, **Strong emotion** differs by **9%**, **Strong interference** shows the largest difference at **78%**, **One-time incidents** have a **13%** higher YTA/NTA ratio difference, **More manifest** conflicts show a **13%** higher difference, and **Single-person** conflicts differ by **11%** [2208.08758].

These results support the paper’s broader claim that “social norm violations” are not a single uniform phenomenon, but depend on emotional intensity, disagreement strength, interference, duration, explicitness, and relationship structure [2208.08758]. This suggests that the AITA-based CAC is both a dataset and an empirical argument for modeling moral judgment as conflict-sensitive rather than label-only.

## 5. Multimodal-conflict CAC in CORE

A separate corpus with the same acronym appears in the CORE framework for multimodal fake news and manipulation detection [2606.03066]. In that setting, the **Conflict Attribution Corpus (CAC)** is the paper’s central supervision resource for teaching multimodal large language models to perform **conflict-oriented reasoning** rather than relying on manipulation-specific cues [2606.03066]. The stated motivation is that existing detection methods rely heavily on manipulation-specific models and large-scale labeled data, which leads to poor generalization to emerging manipulation types [2606.03066]. The authors’ core observation is that manipulated multimodal misinformation is unified by an underlying **conflict**—a semantic contradiction or a physical inconsistency appearing either **across modalities** or between a sample and **world knowledge** [2606.03066].

CAC in this setting was created because existing benchmarks and training sets do not provide the **fine-grained conflict supervision** needed for a model to learn human-like deception detection [2606.03066]. Rather than supplying only real/fake labels or manipulation categories, it annotates manipulated samples with the exact contradictory content and its origin, enabling a model to learn the abstract notion of conflict itself [2606.03066]. The corpus is therefore explicitly designed for robustness to **out-of-distribution** and **unseen manipulation types** [2606.03066].

The corpus is built from **100k image-text pairs** selected from **SAMM** as the source pool [2606.03066]. The construction pipeline has four steps: **Source Sample Selection**, **Background Knowledge Collection**, **Conflict Rationale Generation**, and **Conflict Structuring** [2606.03066]. Background knowledge is retrieved using the **Google Search API**. The image, caption, manipulation prior, and background knowledge are then fed into an MLLM randomly chosen from \(\{GPT-4o,\ \text{Gemini2.5-Pro},\ \text{Qwen3-VL-Plus}\}\), and the generated rationale is cross-validated by the other two MLLMs [2606.03066]. The validated rationale is distilled into a structured annotation with two **Conflict Factors** and their corresponding **Conflict Sources** [2606.03066].

The paper defines **Conflict Factor (C)** as the specific contradictory content, with examples such as “US President”, “Ballon d’Or”, “normal face”, and “unnatural face skin”, and defines **Conflict Source (S)** as where that contradictory element comes from, with allowed source labels **Image**, **Caption**, and **World Knowledge** [2606.03066]. The final record format is
\[
\langle \text{Image } I, \text{Text } T, \{C_1, C_2\}, \{S_1, S_2\} \rangle.
\]
This schema makes the multimodal CAC a dataset of explicit contradiction attribution rather than a binary label set [2606.03066].

After validation, the paper reports that CAC contains **14k instances** [2606.03066]. The source distribution is **29.98%** from the **caption**, **36.86%** from the **image**, and **33.16%** from **world knowledge** [2606.03066]. Human verification was performed by **5 annotators** on a random sample of **1k items**, and **993 samples passed** human review, yielding a **99.3% pass rate** [2606.03066]. The labeling rules emphasize **Conflict existence**, **Source accuracy**, and **Granularity**, requiring a real logical contradiction, correct attribution to Image, Caption, or World Knowledge, and fine-grained concepts rather than vague labels [2606.03066].

## 6. Conflict types, supervision role, and relation between the two CACs

The multimodal CAC captures multiple conflict types: **semantic conflicts**, **physical inconsistencies**, **cross-modal conflicts**, and **world-knowledge conflicts** [2606.03066]. Examples include “Donald Trump won the football award,” where the contradiction is between the caption and world knowledge; mismatches between a caption describing a politician and an image depicting an unrelated celebrity or athlete; and visual inconsistencies such as lighting or shadows, facial skin artifacts, unnatural symmetry, blurred or sharpened regions, and mismatched emotional expressions [2606.03066]. The paper’s examples include annotations such as **CF: actress - Everton attack, CS: WK - Caption**, **CF: basketball player - Perform, CS: WK - Image**, **CF: TV Show - football club, CS: Image - Caption**, and **CF: football coach - government official, CS: WK - Caption** [2606.03066].

CAC is the supervision backbone of CORE’s **Conflict Perception Training (CPT)** [2606.03066]. Because CAC annotations are textual but some conflict sources originate from images, CORE first learns a **Cross-modal Aligner** during **Modality Bridging Pre-Training (MBPT)**. The aligner is trained with FineHARD using
\[
V_p = \text{Aligner}(t_p, V, V), \quad \{v_{n_i}\} = \text{Aligner}(\{t_{n_i}\}, V, V) \tag{1}
\]
and
\[
L_a = \mathbb{E}_{(t,v)\in Q} \frac{1}{1 + e^{y_t(s_1\cdot (t,v) + b_1)}} \tag{2}
\]
with total MBPT loss
\[
L_{\text{mbpt}} = L_{cl} + L_{o2vqa}. \tag{3}
\]
In **Conflict Perception Training**, given a CAC sample
\[
\langle I, T, \{C_1, C_2\}, \{S_1, S_2\} \rangle,
\]
the two conflict factors are converted into features \(t_{c1}, t_{c2}\), and if a factor comes from the image, the aligned visual feature is used:
\[
z_i =
\begin{cases}
\text{Aligner}(t_{c_i}, V, V), & \text{if } S_i \text{ is image} \\
t_{c_i}, & \text{otherwise}
\end{cases} \tag{4}
\]
CORE then applies a conflict-aware contrastive objective
\[
L_{cacl} : 1 + e^{-(s_2 \cdot (z_1, z_2) + b_2)} \tag{5}
\]
and adds a conflict reasoning objective with the target verbalization:
> “Real. / Fake. Because the \(C_1\) from \(S_1\) conflicts with \(C_2\) from \(S_2\).”
The total CPT loss is
\[
L_{\text{cpt}} = L_{cacl} + L_{er}. \tag{6}
\]
All of these components are described as mechanisms for making MLLMs not just know facts, but learn to separate conflicting concepts in feature space and verbalize the contradiction [2606.03066].

Although the interpersonal-conflict CAC and the multimodal-conflict CAC are unrelated datasets in provenance, they share a methodological theme: both attempt to convert a latent, human-interpretable notion of conflict into explicit annotation structure. In the AITA-based CAC, the target is conflict typology and perceived right/wrong judgment in interpersonal situations [2208.08758]. In the CORE CAC, the target is contradiction attribution in manipulated multimodal information [2606.03066]. This suggests a common editorial shorthand—*“structured conflict supervision”* (*Editor's term*)—for their shared design principle, though the two corpora differ sharply in modality, ontology, and downstream task.

## 7. Scope, distinctions, and common misconceptions

A common misconception is that **Conflict Attribution Corpus (CAC)** refers to a single established benchmark. The available arXiv literature instead documents at least two separate resources with that name: one for interpersonal conflict and social-norm judgment in AITA [2208.08758], and one for multimodal contradiction attribution in fake-news detection [2606.03066]. Their overlap is nominal rather than genealogical.

Another possible confusion is with other uses of the acronym **CAC** in unrelated literatures. In reverse mathematics, **CAC** denotes the ordinary **chain-antichain theorem**, and “CAC for trees” is the statement that every infinite subtree of \(\mathbb N^{<\mathbb N}\) has an infinite path or an infinite antichain [2206.00571]. In coding theory, **CAC** denotes **conflict-avoiding codes**, a deterministic transmission scheme for asynchronous multiple access without feedback [2009.11754; 2407.11554]. These meanings are unrelated to either corpus. The cyber-attribution paper “Argumentation Models for Cyber Attribution” uses a DEFCON CTF attribution dataset, but it does **not** introduce a dataset called Conflict Attribution Corpus by that name [1607.02171].

Within the social-computing CAC, an additional misconception would be to treat the six conflict aspects as if they were uniformly easy to annotate. The reported MCC values show otherwise, especially for **manifestation vs. perception** and **interference**, which were among the hardest aspects to annotate [2208.08758]. Within the multimodal CAC, a parallel misconception would be to regard it as merely a real/fake benchmark. Its defining contribution is the structured annotation of **Conflict Factors** and **Conflict Sources**, including **World Knowledge** as an explicit source category [2606.03066].

Taken together, the corpora show two different ways of operationalizing conflict for machine learning. One treats conflict as a socially embedded situation whose dimensions affect human judgment and prediction difficulty [2208.08758]. The other treats conflict as explicit contradiction structure that can supervise reasoning under unseen manipulations [2606.03066]. A plausible implication is that “conflict attribution” has emerged as a cross-domain design pattern for moving beyond coarse labels toward explanatory supervision, but the specific ontologies remain domain-dependent.

Source: https://www.emergentmind.com/topics/conflict-attribution-corpus-cac