Counter-Narratives in Digital Discourse

• Counter-narratives are defined as fact-bound corrective texts that refute hate speech by exposing biases and correcting misinformation.
• They employ expert-guided annotation and algorithmic methods, leveraging datasets like CONAN and MT-CONAN for diverse target coverage.
• They utilize various strategies such as facts, denouncing, and humor while ensuring a civil tone, precise rebuttals, and readability.

Counter-narratives (CNs) are informed, fact-bound textual responses specifically crafted to refute, undermine, or de-escalate hate speech and other forms of toxic online content. In contemporary computational social science and NLP, CNs are positioned as an alternative to punitive or suppressive moderation, instead aiming to challenge stereotypes, correct misinformation, and foster constructive dialogue without violating expressive freedoms. The empirical and methodological foundations of CNs are grounded in multidisciplinary research on digital counterspeech, argumentation, expert-guided interventions, and algorithmically mediated discourse.

1. Formal Definitions and Conceptual Scope

Counter-narratives are typically defined as non-aggressive, corrective utterances whose principal function is to (a) highlight and expose discriminatory premises; (b) provide evidence or data to refute stereotypes; (c) discourage further propagation of hate speech via appeals to shared humanity or social norms (Lee et al., 2024, Chung et al., 2019). A canonical CN addresses a specific hate-speech prompt by leveraging tailored knowledge, logical argumentation, or personal testimony—while maintaining civil tone and avoiding ad hominem escalation. Mechanistically, a CN identifies target group(s), disputes the harmful claim with actionable facts or reasoning, and, where possible, invokes bridging values such as equality or mutual respect.

Beyond the direct hate speech context, the notion of a counter-narrative generalizes: in arguments concerning data governance, social justice, or misinformation, a CN may reframe entrenched or “dominant” narratives by introducing hidden perspectives and negating core premises of the status quo (Abebe et al., 2021).

2. Data Resources and Expert-Guided Annotation

The construction of high-quality CN corpora relies predominantly on expert-driven pipelines. The CONAN dataset (Chung et al., 2019) provides a foundational multilingual benchmark—comprising manually written HS/CN (hate speech/counter-narrative) pairs by NGO operators, enriched with demographic, topical, and strategic annotations. The dataset underpins subsequent advances in multilingual generation, supervised and zero-shot training, and CN type classification (Chung et al., 2021, Bengoetxea et al., 2024).

The Multi-Target CONAN (MT-CONAN) dataset (Fanton et al., 2021) represents an iterative human-in-the-loop (HITL) approach: an initial seed (V₁) of 880 expertly composed CNs is extended through GPT-2–driven loops, each generating and then refining CNs under expert review. Four dynamic adaptation branches facilitate diversity and target balance, including conditioning on implied offensive statements, argumentation, and target-label embedding. Key annotation protocols stipulate a multi-annotator review, intensive NGO-guided training, and explicit well-being protections for annotators. The final release (V₆) contains 5,000 English CNs across seven primary hate targets, with precise proportions—e.g., MUSLIMS (26.7%), MIGRANTS (19.1%), WOMEN (13.2%), LGBT+ (12.3%).

Key annotation quality metrics include acceptance rate ($AR_i$), post-editing effort (HTER), novelty (Jaccard $n$-gram), repetition rate (RR), imbalance degree (ID), and vocabulary expansion origins (Fanton et al., 2021).

3. Types, Strategies, and Argumentation Structures

CNs can be classified into fine-grained strategic types, facilitating both operator selection and computational modeling. The five-way taxonomy formalized in (Chung et al., 2021)—Facts, Denouncing, Hypocrisy, Question, Humor—is widely adopted in multilingual benchmarks. For instance:

• Facts: Empirically grounded information or statistics direct at refuting the hate claim.
• Denouncing: Moral condemnation, signaling broader social unacceptability.
• Hypocrisy: Highlighting logical or moral inconsistencies.
• Question: Reflective prompts to induce reconsideration.
• Humor: Sarcastic or ironic devices to defuse hostility.

The use of argumentative annotation, as in ASFoCoNG (Furman et al., 2022), further segments the CN construction process into Justification–Conclusion pairs (J, C), proposition types (Fact, Value, Policy), and the explicit mapping of collectives, properties, and pivots. This schema bridges classical rhetoric with neural conditional generation, steering models toward targeted refutation strategies.

4. Automatic Generation and Model Architectures

Recent advances in CN generation leverage a spectrum of pre-trained transformers, from autoregressive decoders (GPT-2, DialoGPT) to bidirectional or encoder–decoder architectures (BART, T5, mT5) (Tekiroglu et al., 2022, Bengoetxea et al., 2024). Empirical studies demonstrate the superior diversity and specificity of autoregressive LMs with stochastic decoding (Top-$k$, Top-$p$) over beam search, particularly for out-of-target (unseen group) scenarios. Effective generalization is contingent on training data that includes at least one semantically related hate-target to the test set (Tekiroglu et al., 2022).

Sophisticated pipelines incorporate knowledge-grounded mechanisms: retrieval-augmented paradigms extract stance-aligned counter-knowledge from debate or Wikipedia–news repositories, then inject fact-checked snippets into the generation stage (Chung et al., 2021, Jiang et al., 2023). Methods such as energy-based constrained decoding enforce differentiable constraints for knowledge preservation, countering, and fluency (Jiang et al., 2023).

Novel approaches employ attention regularization (EAR, KLAR) to counteract in-domain overfitting, promoting more uniform or targeted context usage and yielding higher CN specificity, especially for previously unseen hate targets (Bonaldi et al., 2023). Other architectures use contrastive optimal transport kernels for target-aware representation and diversity maximization (Zhang et al., 2024).

5. Evaluation Protocols and Benchmarking

Evaluation of CN generation has shifted from n-gram–based overlaps (BLEU, ROUGE-L) to LLM-driven, human-aligned models. The multi-aspect framework in (Jones et al., 2024) decomposes CN quality into specificity, opposition, relatedness, toxicity, and fluency, with neural evaluators achieving strong correlation with human benchmarks (e.g., Vicuna-33B $r=0.82$ for multi-aspect agreement). Tournament-style pairwise LLM ranking further refines system ordering: Zephyr and Mistral-Instruct chat-aligned models often outperform instruct-tuned and base variants under this regime (Zubiaga et al., 2024). The best correlations with human judgment reach Spearman's $\rho=0.88$.

Supplementary metrics include repetition rate, novelty, vocabulary expansion, and knowledge overlap (fraction of CN tokens traced to retrieved facts or snippets). Exhaustively paired datasets such as FC-CONAN (Junqueras et al., 4 Jan 2026) facilitate retrieval evaluation, supporting MAP, nDCG, precision/recall, and comprehensive error analysis over all HS–CN pairings.

Personality framing, verbosity, readability (Flesch Reading Ease, FK Grade Level), affective tone (GoEmotions/DistilBERT sentiment), and ethical robustness (refusal rates, hatefulness scores) comprise further dimensions in LLM-generated CN evaluation (Ngueajio et al., 4 Jun 2025).

6. Emerging Directions: Multilinguality, Alternative Speech, and Practical Deployment

Recent CN corpora extend into Basque, Spanish, and structurally diverse languages via expert–post-edited neural machine translation (Bengoetxea et al., 2024). Results indicate that multilingual augmentation boosts transfer for related language pairs (English–Spanish), with reduced gains for language isolates (Basque).

Alternative Speech (AS) has emerged as a corrective paradigm, delivering direct phrasal substitutions for hate speech, emphasizing one-to-one mapping with maximal context preservation, and eschewing coaching or argument (Lee et al., 2024). This complements the traditional, argument-based CN, creating a feedback loop between education (CN) and behavioral guidance (AS).

Best practices for hybrid human–AI CN pipelines include HITL post-editing, emotion-guided persona prompting, brevity enforcement, and dynamic topic rebalancing. Ensuring audience readability (targeting FK $\leq$ Grade 8) and monitoring refusal by safety-tuned LLMs remain open challenges in practical deployment contexts.

7. Socio-Technical Implications and Future Challenges

The deployment of CNs presents unique operational and ethical considerations. Expert annotation regimes must address annotator well-being, cognitive load, and training in NGO–operationalized speech norms (Fanton et al., 2021). CNs provide an alternative to deletion or filtering, preserving expressive rights and supporting healthy discourse, but efficacy is bounded by challenges in generalization, semantic coverage, and audience accessibility.

There is a sustained need for continuous dataset expansion, argument-structure integration, cross-cultural robustness, multi-modal counterspeech, and reference-free, interpretable evaluation metrics that directly track the social and rhetorical functions of CNs in dynamic digital environments.

---

References:

• (Chung et al., 2019) CONAN: COunter NArratives Through Nichesourcing
• (Fanton et al., 2021) Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech
• (Chung et al., 2021) Multilingual Counter Narrative Type Classification
• (Furman et al., 2022) Parsimonious Argument Annotations for Hate Speech Counter-narratives
• (Tekiroglu et al., 2022) Using Pre-Trained LLMs for Producing Counter Narratives Against Hate Speech: a Comparative Study
• (Chung et al., 2021) Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech
• (Jiang et al., 2023) Retrieval-Augmented Zero-Shot Counter Narrative Generation for Hate Speech
• (Bonaldi et al., 2023) Weigh Your Own Words: Improving Hate Speech Counter Narrative Generation via Attention Regularization
• (Jones et al., 2024) A Multi-Aspect Framework for Counter Narrative Evaluation using LLMs
• (Junqueras et al., 4 Jan 2026) FC-CONAN: An Exhaustively Paired Dataset for Robust Evaluation of Retrieval Systems
• (Ngueajio et al., 4 Jun 2025) Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate
• (Bengoetxea et al., 2024) Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation
• (Zhang et al., 2024) COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport
• (Lee et al., 2024) Alternative Speech: Complementary Method to Counter-Narrative for Better Discourse
• (Zubiaga et al., 2024) A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation
• (Abebe et al., 2021) Narratives and Counternarratives on Data Sharing in Africa