Hate or No Hate: Defining Online Hostility

• Hate or No Hate is a framework that distinguishes deliberate hate speech from offensive or benign communication by assessing intent, target, and contextual framing.
• The approach integrates linguistic analysis, multimodal data, and structured classification to refine hate speech detection and reduce false positives.
• Research highlights that effective detection depends on discourse context, annotator variability, and dynamic interactions across online communities.

“Hate or No Hate” denotes the operational problem of deciding whether a communicative artifact should be treated as hateful rather than merely offensive, abusive, controversial, or benign. Research consistently shows that this boundary is necessary for moderation and analysis, but unstable if reduced to a single lexical rule: the decision depends on whether there is a deliberate attack, whether the target is a protected group or a specific individual, how the attack is linguistically framed, what discourse context is required for interpretation, and, in multimodal settings, how text, audio, and imagery jointly realize hostility (Gibert et al., 2018, ElSherief et al., 2018, Jin et al., 2024, Das et al., 2023).

1. Definitional boundaries and label semantics

A central line of work treats hate recognition as a definitional problem before it becomes a modeling problem. The Stormfront sentence-level corpus operationalizes hate speech through three jointly necessary premises: a deliberate attack, directed towards a specific group of people, and motivated by aspects of the group’s identity. On that basis it distinguishes hate from noHate, and adds relation for cases where no single sentence is hateful on its own but adjacent sentences jointly express hate, plus skip for material that cannot be judged meaningfully, such as non-English text or irrelevant content (Gibert et al., 2018). This formulation is consequential because it excludes many superficially offensive or extremist-adjacent utterances from the hate class.

Other work shows that even within the positive class, the binary label compresses materially different phenomena. A target-based analysis of Twitter separates Directed hate, aimed at a specific individual or entity, from Generalized hate, aimed at a group sharing a protected characteristic. In that formulation, directed hate is more personal and explicitly insulting, whereas generalized hate is dominated by religious hate and more often uses lethal terms such as “murder,” “exterminate,” and “kill” (ElSherief et al., 2018). This implies that “hate” is not a uniform surface category even before modality or context are considered.

Shared-task formulations in Arabic make the same point structurally. In the Arabic Fine-Grained Hate Speech Detection setting, offensive language detection, binary hate speech detection, and six-way hate-category classification are arranged hierarchically: an offensive post is not necessarily hate speech, while a hate-speech post is always offensive. The negative class for binary hate speech therefore includes both clean, non-offensive tweets and offensive-but-non-hateful tweets (AlKhamissi et al., 2022). A related complication arises in counterspeech corpora: comments opposing hate may contain slurs, anger, or hostility, yet still be anti-hate rather than hateful. In a YouTube dataset built around hate-targeting videos, counterspeech and non-counterspeech are almost balanced, with 6,898 counterspeech comments and 7,026 non-counterspeech comments, and much of the non-counterspeech class consists of comments agreeing with the hateful video or themselves expressing hate (Mathew et al., 2018).

2. Linguistic realization, target structure, and lexical ambiguity

Linguistic analyses show that hate recognition cannot be reduced to taboo-word detection. In target-based Twitter data, directed hate is characterized as more informal, angrier, and more likely to attack via name-calling, with fewer analytic words and more words associated with authority and influence. Generalized hate, by contrast, is dominated by religious hate and marked by lethal vocabulary and quantity words such as “million” and “many” (ElSherief et al., 2018). The resulting distinction is not merely stylistic; it changes what counts as the strongest evidence for the hate label.

Coded language further destabilizes lexical heuristics. In work on “Operation Google”-style euphemistic hate, benign words such as Google, Yahoo, Skype, Bing, Skittle, and Butterfly are used as substitutes for targeted communities. The same token can therefore be literal and non-hateful, or a hate marker in context. A linear SVM trained on coded-language tweets achieves 79.4397% accuracy, with precision = 0.795 and recall = 0.794, by relying on contextual lexical cues rather than the code words alone (Magu et al., 2017). The broader implication is that hate often resides in local compositional context and community-specific conventions, not in isolated lexemes.

Counterspeech research reinforces the same lesson from the opposite direction. Anti-hate replies often contain strong negative affect, swearing, and direct address, yet function to oppose hatred rather than propagate it. In the YouTube counterspeech dataset, counterspeech comments receive more likes on average than non-counterspeech comments, and LIWC analysis shows counterspeech to be more associated with anxiety, anger, sadness, negative emotion, swearing, insight, and discrepancy, while non-counterspeech is more associated with certainty, religion, achievement, and other personal concerns (Mathew et al., 2018). Functionality testing of modern detectors exposes a parallel failure mode: many systems assign higher hatefulness scores merely because specific identities are mentioned, and they often conflate hatefulness with negative emotion. Performance drops sharply on non-hateful posts with negative emotions, especially counterspeech-like disapproval and sadness, revealing that identity mention and affect polarity frequently act as confounds rather than valid evidence (Jin et al., 2024).

3. Context, discourse dependence, and annotator variability

The “hate or no hate” boundary is often undecidable from isolated local text. The Stormfront corpus formalizes this with the relation label, used when the hateful meaning emerges only across adjacent sentences. Examples such as a sequence celebrating the fact that “2 blacks won’t be having kids” are not recoverable from any single sentence alone; the relevant unit is the discourse relation rather than the sentence (Gibert et al., 2018). This is an important annotation principle because it makes explicit that some hate is irreducibly contextual.

A more exploratory semantic approach reaches a similar conclusion through document-level representation rather than discourse labeling. Work on “quantum semantic correlations” uses HAL-derived word vectors and a correlation score $$r = \langle \phi | \hat{B}_x \hat{W}_x | \phi \rangle$$ to analyze how keywords such as “women,” “white,” and “black” relate in hate and non-hate texts. The point is not benchmark classification performance—there is no train/test split or precision/recall table—but that similar keywords can realize very different semantic structures across documents, and that cosine similarity alone can mask whether terms are semantically unified, opposed, or effectively unrelated (Galofaro et al., 2018). This suggests that some label disagreements may reflect deeper structural ambiguity rather than annotator error.

Recent work pushes this instability further by treating hate judgments as culturally conditioned rather than universally fixed. A culture-aware framework defines prediction as $P(\text{hate}\mid u_i,p)$ or $P(\text{hate}\mid \mathbf{c}_i,p)$, where $\mathbf{c}_i$ denotes the annotator’s cultural attributes. It constructs “hate subspaces” from combinations of background attributes, uses label propagation over the power set of those attributes, and learns a personalized hate perception embedding $HP(u_i)$ that conditions the final classifier (Cai et al., 11 Oct 2025). A directly related diagnostic study shows why such conditioning may be necessary: detectors exhibit systematic identity bias, assign higher hatefulness to some target mentions than others, and confuse negative emotion with hate, even on controlled functionality tests (Jin et al., 2024). Together, these results imply that binary hate labels often encode both semantic ambiguity and culturally structured disagreement.

4. Binary classification architectures in text

Despite these conceptual complications, binary hate detection remains a core supervised task. On the binary ETHOS dataset, one line of work compares conventional static embeddings with “static BERT embeddings,” obtained by averaging contextual BERT vectors into fixed word embeddings for downstream neural models. The best model, BiLSTM + static BE, achieves F1 79.71, Accuracy 80.15, Precision 80.37, Recall 79.76, and Specificity 83.03. Compared with a fine-tuned BERT baseline with Specificity 74.31, the main reported advantage is lower false-positive propensity on the non-hate class (Rajput et al., 2021). This is notable because over-flagging legitimate speech is a recurring concern across the literature.

A different strategy improves binary detection by embedding it in a task hierarchy. The Arabic AraHS system uses MARBERTv2 with three task heads for offensive detection, hate speech detection, and fine-grained hate-category classification, optimizing the sum of the three negative log-likelihood terms. Final predictions are ensembled through element-wise multiplication of probabilities across models. On the binary hate speech detection subtask, AraHS reaches 94.1% Accuracy, 87.0% Precision, 79.5% Recall, and 82.7% Macro-F1, outperforming the shared-task baseline; the validation ablation also shows Multitask HSD at 88.1% Macro-F1 versus 86.4% for Single-task HSD (AlKhamissi et al., 2022). The main lesson is that binary hate detection benefits from structured auxiliary supervision about offense and target type.

Architecture-comparison work then revisits whether contemporary decoder-only LLMs materially improve the binary decision. In a benchmark contrasting fine-tuned encoders such as DistilBERT and Twitter-RoBERTa with decoder-only models such as Gemma-3-1B and Qwen1.5-0.5B, the strongest result is that a fine-tuned Qwen1.5-0.5B slightly edges out the encoder baselines on the validation split, while few-shot prompting substantially improves Gemma over pure zero-shot, with gains of over 0.15–0.20 F1 (Mon et al., 14 Jul 2025). The evidence therefore favors task-specific adaptation over generic prompting for reliable hate/no-hate decisions, even when large decoder-only models are used.

5. Multimodal extensions: images, memes, and video

Multimodal settings show that “hate or no hate” can no longer be defined at the text level alone. In the CASE 2023 Sub-task A setting, text-embedded images from the Russia–Ukraine conflict are labeled “Hate Speech” or “No Hate Speech.” A stacked ensemble using InceptionV3 for the image, Tesseract-OCR plus BERT and XLNet for embedded text, and feature concatenation to a 1536-dimensional representation achieves 75.21 accuracy and 74.96 F1 internally, though its official shared-task result is lower and remains below the baseline and median (Kashif et al., 2023). The accompanying error analysis is instructive: false positives arise from conflict terms such as “explosion,” “kills,” and “invasion of Ukraine,” while false negatives arise from sarcasm, symbolic imagery, and historical context.

Not all multimodal datasets preserve a binary framing. Indian political memes in code-switched language are labeled Hate-Inducing, Satirical, or Non-Offensive, not simply hate versus non-hate. The proposed binary-channelled CNN-cum-LSTM model fuses a CNN image branch with an LSTM text branch and, with GloVe embeddings, reaches Precision = 0.762, Recall = 0.816, and F1 = 0.792 on this three-class task (Rajput et al., 2022). The significance for hate/no-hate research is that satire is treated as a distinct middle category rather than collapsed into non-hate, which makes explicit a boundary that binary formulations often hide.

Video sharpens the multimodal argument further. HateMM defines a video-level binary classifier $Z(F; A; T) \rightarrow y$, where $y \in \{0,1\}$, over frames, audio, and transcript. The dataset contains 1083 videos, with 431 hate and 652 non-hate labels, plus hateful frame spans as rationales. The best multimodal model, BERT $\odot$ ViT $\odot$ MFCC, achieves 0.798 accuracy and 0.790 macro-F1, improving by about 5.7% in macro-F1 over the best unimodal model (Das et al., 2023). The reported qualitative patterns explain why: some hateful videos are transcript-noisy but visually explicit, others are visually irrelevant but hateful in shouted audio, and many require temporal alignment across modalities.

A distinct multimodal branch reframes the problem as localization and mitigation rather than binary decision. DeHate constructs a synthetic image-text dataset from hateful prompts, uses Stable Diffusion plus DAAM to derive hate attention maps, and evaluates systems by Intersection over Union against hateful regions to be blurred. The final dataset contains 2,411 instances, and the authors’ own system is a baseline rather than the top performer (Dalal et al., 26 Sep 2025). This work is therefore relevant to “hate or no hate” chiefly as evidence that, in images, the operational problem may be region grounding rather than whole-item classification.

6. From static classification to dynamics, interaction, and mitigation

At larger scales, hate/non-hate distinctions become properties of diffusion, communities, and trajectories rather than just items. A topic-aware Twitter study separates hate generation from hate diffusion, using user history, topic affinity, endogenous trending signals, and exogenous news. For predicting whether a user will initiate hate on a hashtag, the best model reaches Macro-F1 = 0.65; for dynamic retweet prediction, RETINA-dynamic reaches Macro-F1 = 0.89, with hateful cascades described as more bursty, concentrated, and echo-chamber-like than non-hateful ones (Masud et al., 2020). A temporal study of Gab similarly treats hate as a user-level dynamic state inferred by DeGroot propagation over follower and repost networks: the amount of hate speech increases over time, new users become hateful at an increased and faster rate, hateful users move into the network core more quickly, and the broader platform language becomes closer to that of high-hate users (Mathew et al., 2019).

Community-level work on Reddit adds that hateful environments can be behaviorally distinct even when individual utterances are not being directly classified. In a matched comparison of 25 hateful and 25 non-hateful subreddits, users who receive replies to their first comment are less likely to remain engaged in hateful subreddits than in matched non-hateful subreddits, with mean ERR values of 0.98 and 1.03 respectively; first replies in hateful communities are more toxic, more negative, and more likely to attack the commenter (Hickey et al., 2023). At an even larger scale, the “hate universe” literature models a multi-platform network-of-networks and argues that the 2020 U.S. election and January 6 were associated with structural and narrative hardening of online hate, including a 299% increase in Telegram-related connections between November 1–3 and November 4–7 (Verma et al., 2024). These studies shift the unit of analysis from item labels to ecosystem behavior.

Interaction after hate also becomes a predictive object in its own right. A Reddit forecasting study defines conversation incivility after a reply $r$ as $P(\text{hate}\mid u_i,p)$0, where $P(\text{hate}\mid u_i,p)$1, $P(\text{hate}\mid u_i,p)$2, and $P(\text{hate}\mid u_i,p)$3. The metric matches human pairwise judgments in 183/194 pairs (94.3%), with $P(\text{hate}\mid u_i,p)$4, and the best RoBERTa-based 3-way forecaster reaches 0.52 weighted F1, while extreme-case binary prediction reaches 0.75 weighted F1 at $P(\text{hate}\mid u_i,p)$5 (Yu et al., 2023). This extends hate/no-hate from a content label to a trajectory question: a reply can be non-hateful yet escalation-prone.

Finally, mitigation work suggests that the binary label can be operationally complemented by graded intervention. Hate speech normalization defines a target $P(\text{hate}\mid u_i,p)$6 and, for strong hate where $P(\text{hate}\mid u_i,p)$7, seeks a semantically similar $P(\text{hate}\mid u_i,p)$8 with $P(\text{hate}\mid u_i,p)$9, using $P(\text{hate}\mid \mathbf{c}_i,p)$0. The NACL pipeline decomposes this into hate intensity prediction, hateful span identification, and span paraphrasing; it reports 0.1365 RMSE for intensity prediction, 0.622 F1-score for span identification, and 82.27 BLEU with 80.05 perplexity for normalized text generation (Masud et al., 2022). The explicit claim is that normalization is not de-hating: the goal is to weaken the intensity of hatred, not necessarily to render the output fully non-hateful. That formulation captures the larger lesson of the field: “hate or no hate” remains a necessary boundary, but research increasingly treats it as one layer inside a richer system of target analysis, discourse interpretation, multimodal grounding, temporal dynamics, and intervention.