Distinguishing legitimate critical speech from coordinated conspiracy narratives is a pressing challenge for the integrity of the online information ecosystem, yet prevailing approaches rely on labeled entity roles and rigid predefined schemas that fail to capture the diffuse and context-dependent nature of participation in such narratives. We propose a modular framework that extracts an entity-interaction graph directly from narratives and reasons over it for classification, without depending on annotated node-level labels. Our pipeline enumerates candidate entity spans from a domain-adapted BERT encoder and learns a bipartite graph structure, which is then classified with a Heterogeneous Graph Transformer. To make predictions interpretable, we cast the graph as a hypergraph and apply a counterfactual distillation procedure that estimates the Individual Treatment Effect of each node, pruning extraneous entities to recover a minimal predictive subgraph. On the standard PAN Oppositional Narratives benchmark, our model achieves a macro F1-score of 0.93 and an MCC of 0.84, reaching state-of-the-art performance with roughly one-third of the parameters of the next best system, while pruning the graphs by 75.2% on average without altering predictions. Our results demonstrate that graph-based representations coupled with counterfactual inference offer both strong predictive accuracy and transparency into the entities driving conspiratorial discourse.
Figures & tables
Figure 1: Overview of the proposed methodology. Stage 1 defines the input, which in this case consists of oppositional narratives. Stage 2 generates tokens and contextual embeddings using BERT. Stage 3 extracts the entities V from the tokens and embeddings by applying Algorithm 1 (Appendix A ). Stage 4 produces enriched low-dimensional representations of the entities. Stage 5 constructs the incidence matrix A . Stage 6 performs graph classification. The final stage presents the counterfactual analysis. The key point is that every classification decision is routed through an explicit entity-interaction graph, which makes the counterfactual distillation of the last stage possible. Entity labels are abbreviated for readability; actual nodes are token spans (cf. Figure 2 ).
Component
Parameters
Percentage (%)
BERT (frozen)
109,483,778
99.841
Enricher
2,240
0.002
HyperLink classifier
759
0.001
Hypergraph classifier
126,745
0.116
Entity projector
24,592
0.022
Span classifier
11,525
0.011
Table 1: Decomposition of the total number of model parameters by component. Over 99.8% of the parameters live in the frozen text encoder: the entire graph construction and reasoning machinery adds fewer than 200K trainable parameters.
Team
MCC ↑
F1 -Macro ↑
F1 -Cons. ↑
F1 -Crit. ↑
Our model
0.840
0.930
0.895
0.945
IUCL
0.838
0.919
0.894
0.944
AI_Fusion
0.830
0.914
0.886
0.942
SINAI
0.829
0.914
0.888
0.941
Fine-tuned BERT
0.796
0.897
0.863
0.931
BERT+LOCO
0.655
0.844
0.787
0.901
Table 2: Classification results of our model compared to the PAN benchmark [ 21 ] . Our model outperforms IUCL [ 30 ] , AI_Fusion [ 1 ] and SINAI [ 39 ] on every metric while using roughly one-third of the parameters of the runner-up. Fine-tuned BERT is adapted to the specific context of the target class, whereas BERT+LOCO is a fine-tuned version trained on a larger dataset, enabling broader domain adaptation across several types of conspiracy-related texts.
Figure 2: Per-entity ITE contributions for an oppositional narrative text correctly classified as conspiracy, with a predicted probability of 0.836. A small number of entities account for most of the positive contribution which allows the graph to be pruned aggressively without flipping the prediction.
Ablation Approach
F1 -Score
Relative Decrease
Original model
0.9300
–
No enrichment module
0.8600
7.53%
No entity classifier
0.5256
43.48%
No Gumbel-Sigmoid module
0.3293
64.59%
Table 3: Ablation study results and performance decrease. Learning the graph structure with Gumbel-Sigmoid is the single most important component: forcing a fully connected graph costs almost two-thirds of the model’s performance.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Parameter
Value
Description
Span and entity representation
max_span_width
5
Maximum number of tokens allowed in a candidate span.
num_span_type
4
Number of span/entity categories considered by the model.
entity_dim
16
Dimensionality to represent each entity.
dropout_rate
0.15
Dropout probability applied for regularization.
Hyperlink module
Appendix
Table 4: Hyperparameters of the experiment and their roles in the architecture. Note that the hyperparameter num_hyperlink refers to the set size k , and all hyperlink references correspond to the set E .
School of Future Technology, South China University of Technology, Guangzhou, China · China Mobile GBA Innovation Institute, Guangzhou, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China