Drunk Language: Detection & Characteristics

• Drunk Language is defined as the observable linguistic and paralinguistic behavior linked to alcohol intoxication, spanning social media text, speech, and induced LLM outputs.
• Research methodologies include tweet-level and user-level classification, as well as acoustic and prosodic analysis using features like LIWC categories and MFCCs.
• Empirical studies report high detection accuracies using machine learning models while also highlighting significant safety and ethical concerns in labeling and LLM-induced outputs.

Drunk language denotes linguistic and paralinguistic behavior associated with intoxication, most commonly alcohol intoxication, as manifested in social-media text, speech, and, more recently, induced large-language-model outputs. In computational work, the term has been operationalized in at least three distinct ways: as tweet-level detection of whether a message was written under the influence of alcohol; as user-level characterization of “predominant drunk texters” whose timelines repeatedly contain self-reported intoxication; and as speech- or audio-based intoxication detection using prosodic and acoustic correlates such as pauses, pitch-related features, loudness, intensity, and MFCCs (Joshi et al., 2016, Maity et al., 2018, Mehta et al., 2018). A newer line of work extends the concept to LLMs by treating “drunk language” as an induced persona or post-trained style characterized by looseness, typos, digressions, and reduced inhibition, then measuring its effects on task reliability and safety (Doudkin, 21 Dec 2025, Shetty et al., 19 Jan 2026).

1. Conceptual scope and definitional boundaries

The human-authored literature distinguishes between intoxication as a latent state and intoxication-related language as its observable trace. In the tweet-level setting, “drunk-texting prediction” is framed as the task of determining whether a tweet was authored while the writer was under the influence of alcohol (Joshi et al., 2016). In the user-level setting, “drunk language” is defined more conservatively as the language produced by Twitter users who repeatedly self-report drinking or intoxication and whose timelines exhibit stable psycholinguistic patterns consistent with alcohol-related states and contexts (Maity et al., 2018).

This distinction matters methodologically. Tweet-level labeling is sensitive to momentary ambiguity, retrospective narration, and sarcasm, whereas user-level labeling can average over many posts and recover a more stable behavioral signature. The speech literature introduces a further distinction between clinical intoxication and perceived intoxication. In DIF, intoxication labels are inferred from online video titles and captions, so the target is perceived intoxication rather than BAC-grounded physiological state (Mehta et al., 2018). The pause-based phonological note is even more speaker-relative: it defines drunkenness operationally as an increase in pause behavior relative to previous sober recordings of the same speaker (Rahimi, 2015).

The LLM literature uses the term in a different but related sense. Here, drunk language is not observed but induced: a model is instructed or adapted to produce output resembling human drunk texting, with orthographic noise, random tangents, and lowered goal adherence (Doudkin, 21 Dec 2025, Shetty et al., 19 Jan 2026). This extends the notion of drunk language from behavioral analytics to prompt-conditioned and post-trained model behavior.

2. Operationalization, labeling, and corpus construction

The principal datasets and labeling strategies differ sharply in granularity, supervision, and noise profile.

Study	Modality	Labeling scheme
(Joshi et al., 2016)	Twitter text	Hashtag-based distant supervision; user-matched negatives in Dataset 2
(Maity et al., 2018)	Twitter user timelines	Manual validation of alcohol-keyword candidates; user labeled drunk texter if they post $\geq 5$ unanimously labeled drunk tweets
(Mehta et al., 2018)	Audio-visual clips	Weak labels from video titles/captions on YouTube and Periscope
(Shetty et al., 19 Jan 2026)	LLM training corpus	DrunkText curated from TFLN and Reddit’s r/drunk, filtered by a drunk-text classifier
(Doudkin, 21 Dec 2025)	LLM prompting benchmark	Single-sentence psychoactive persona prompts compared with a sober control

The tweet-level study constructs three datasets. Dataset 1 contains 2,435 drunk tweets and 762 sober tweets, with positives labeled by hashtags such as #drunk, #drank, and #imdrunk, and negatives by #notdrunk, #imnotdrunk, and #sober. Dataset 2 retains the same 2,435 drunk tweets but uses 5,644 negatives drawn from other tweets by the same users that do not contain drunk hashtags. Held-out Dataset H contains 193 drunk and 317 sober tweets, with no overlap with training data (Joshi et al., 2016). Conservative preprocessing removes non-Unicode characters, hyperlinks, tweets shorter than 6 words, and the labeling hashtags themselves, explicitly to prevent label leakage.

The user-level Twitter study imposes a stricter annotation pipeline. Candidate drunk tweets are drawn from users who posted alcohol-related keywords from a 61-term seed list compiled from West et al. (2012) and expanded via WordNet. Three independent annotators manually validate candidate tweets, and only unanimously judged drunk-texts are retained. A user is labeled a drunk texter only if they have posted at least 5 unanimously labeled drunk tweets. The final balanced dataset comprises 278 drunk texters and 278 non-drunk texters, with the latter sampled from the Twitter 1% random stream for January 2014 and restricted to users whose tweets contain none of the 61 alcohol-related keywords (Maity et al., 2018).

The audio-visual DIF dataset is weakly labeled and explicitly non-clinical. The sober category contains 78 videos from 78 unique subjects, and the drunk category contains 91 videos from 88 unique subjects. After shot detection, face detection, tracking, and 10-second sample extraction, the dataset yields 4,658 intoxicated and 1,769 sober face video samples. The train/validation/test split is 4,540 / 642 / 948 AV samples, with a subject-independent partition attempted via face-embedding clustering, though the authors note it is not perfect (Mehta et al., 2018).

The LLM safety study introduces DrunkText, a 63,577-message corpus curated from TFLN and Reddit’s r/drunk and filtered by a logistic-regression classifier on Sentence-BERT embeddings. The split is 57,219 training samples and 6,358 held-out samples, with average length approximately 41 tokens and human validation indicating 75% relevance with Fleiss’ $\kappa = 0.71$ (Shetty et al., 19 Jan 2026).

Across these corpora, labeling noise is a recurring issue. Hashtag supervision can capture retrospective mentions rather than in-the-moment intoxication; keyword filtering can pick up quotations or jokes; weak video labels do not measure exact intoxication; and LLM “drunkness” is an induced style rather than a physiological condition (Joshi et al., 2016, Maity et al., 2018, Mehta et al., 2018, Shetty et al., 19 Jan 2026).

3. Psycholinguistic and lexical structure of human drunk language

The richest psycholinguistic profile comes from the user-level Twitter study, which uses LIWC categories, sentiment lexicons, stress lexicons, and topical keyword lists. The most discriminative features by $\chi^2$ are overwhelmingly LIWC categories, with the top 25 including Dictionary words (494.82), Function words (468.80), Relativity (407.51), Adverbs (395.30), Time (391.33), Ingestion (381.04), Space (373.13), Inclusive words (363.99), Cognitive processes (353.07), Auxiliary verbs (352.51), Prepositions (349.97), Common verbs (342.18), Smoking-related words (328.77), Biological (322.17), Conjunctions (318.62), Present tense (316.12), Pronouns (314.34), Past tense (312.53), 1st person singular (311.68), Home-related words (304.38), Quantifiers (294.91), Impersonal pronouns (292.51), Motion-related words (292.30), Food-related words (289.97), and Certainty (281.10) (Maity et al., 2018).

Substantively, drunk texters exhibit elevated negative affect and social content across both weekdays and weekends. Selected LIWC means show higher social processes, anxiety, anger, sadness, sexual language, ingestion, and leisure, alongside lower religious language. For example, social processes rise from 6.88 and 6.78 in non-drunk weekday/weekend tweets to 8.69 and 8.86 in drunk texters; ingestion rises from 0.36 and 0.35 to 0.79 and 0.83; religious terms decline from 0.41 and 0.42 to 0.37 and 0.36 (Maity et al., 2018). Drunk texters also use significantly more swear words, higher fractions of health- and food-related keywords, and stress language tied more strongly to interpersonal conflicts, smoking, and family problems. Money-related words are more frequent during weekdays than weekends among drunk texters.

The tweet-level study complements this profile with a different feature geometry. It finds that lexical content is highly informative: unigram and bigram features, plus LDA-derived lexical features such as “drinking,” “tonight,” “drunken,” and “lmao,” dominate performance. Stylistic and affective features include POS ratios, named entities, discourse connectors, spelling errors, repeated characters, capitalization, tweet length, emoticons, and MPQA-derived sentiment ratios (Joshi et al., 2016). Ranked stylistic indicators include spelling_error, capitalization, POS_NOUN, length, sentiment_ratio, char_repeat, and several LDA-derived tokens.

A common misconception is that drunk language is reducible to orthographic degradation. The evidence is more specific. The tweet-level paper reports that noun, adjective, and adverb ratios are nearly similar across classes, with maximum difference 0.03%, so POS alone is weak (Joshi et al., 2016). The user-level paper shows that the strongest predictors are not merely typos or profanity but broad psycholinguistic categories: function words, pronouns, temporal and spatial relativity, ingestion, cognitive processes, and self-focus (Maity et al., 2018). Drunk language, in this sense, is simultaneously lexical, affective, social, and self-referential.

4. Acoustic, phonological, and multimodal correlates

In speech, drunk language is typically studied through acoustic and phonological manifestations rather than lexical content. The pause-based note foregrounds pauses as a primary cue, drawing on prior work reporting longer durations, more pauses, and increased percentage of silent intervals under alcohol. Its proposed detector compares a test recording against “previous recordings of the same person,” uses an FFT-based speech activity detector in the 80–300 Hz band, and labels the speaker as drunk if pause behavior is higher than baseline (Rahimi, 2015). The note explicitly cautions that mean $F0$ is unreliable because it is confounded by age, sex, and medical conditions, even though variability may increase.

Methodologically, the pause-centric proposal is minimalist. It specifies the 80–300 Hz band, scipy.fftpack.rfft, and sliding-window analysis, but does not provide window size, hop size, minimum pause duration, or a concrete thresholding rule. It therefore functions more as a design note than a validated benchmark. No accuracy, precision, recall, F1, or ROC-AUC are reported (Rahimi, 2015).

DIF provides a stronger empirical baseline for drunk-speech detection in the wild. Audio features are extracted with OpenSmile and include fundamental frequency, loudness, intensity, and MFCCs. Audio_DNN uses utterance-level vectors in a two-layer perceptron with ReLU activations, batch normalization, and dropout; two hidden-layer configurations are tested, 256-128 and 512-256. Audio_LSTM operates on segment-level OpenSmile sequences using either fixed-size 75 ms windows with 30 ms overlap or a variable-size scheme with 87 segments per sample (Mehta et al., 2018).

Audio outperforms video in this setting. On the validation set, Audio_DNN reaches 85.23% accuracy with 256-128 and 88.51% with 512-256, while Audio_LSTM yields 74.40% with fixed segment size and 82.80% with variable segment size. On the test set, audio-only achieves 87.55% accuracy, 0.85 precision, and 0.98 recall, whereas visual baselines are 76.37% for VGG-LSTM and 77.42% for the 3D CNN Block_2+ variant. Weighted audio-visual fusion improves to 88.39% accuracy, 0.85 precision, and 0.99 recall (Mehta et al., 2018).

These results indicate that acoustic correlates of intoxication are often more stable than visual ones under unconstrained capture conditions. At the same time, the labels remain weak and “perceived,” the recordings are in-the-wild, and the target domain may differ from clinically or legally meaningful intoxication detection (Mehta et al., 2018).

5. Detection architectures and reported performance

The literature contains three distinct prediction settings: tweet-level classification, user-level classification, and audio- or multimodal intoxication detection.

Study	Task	Best reported result
(Joshi et al., 2016)	Tweet-level drunk vs sober	85.5% accuracy on Dataset 1 with N-grams only; 78.1% on Dataset 2 with all features
(Maity et al., 2018)	User-level drunk texter vs non-drunk texter	SVM weekday accuracy 96.78%, precision 0.968, recall 0.968, F1 0.968, ROC-AUC 0.991
(Mehta et al., 2018)	Audio-visual intoxication detection	Weighted ensemble test accuracy 88.39%, precision 0.85, recall 0.99

The tweet-level study uses SVMs as the primary model and compares N-grams only, stylistic only, and all features. On Dataset 1, N-grams only achieve 85.5% accuracy, drunk-class precision 88.8%, and drunk-class recall 92.5%. Stylistic-only features fall to 75.6% accuracy and produce an extremely unbalanced error profile, with sober negative recall of 3.2%. On Dataset 2, which uses user-matched negatives, all features yield the best overall result at 78.1% accuracy, with positive precision 65.3% and positive recall 57.5% (Joshi et al., 2016). The drop from Dataset 1 to Dataset 2 is itself informative: user-matched negatives reduce user-style confounds and make the task substantially harder.

Held-out generalization is more limited. On Dataset H, human annotators average 68.8% accuracy, with Cohen’s $\kappa$ ranging from 0.30 to 0.42. The classifier trained on Dataset 2 reaches 64% accuracy on H, compared with 47.3% for the classifier trained on Dataset 1 (Joshi et al., 2016). This supports the claim that machine performance approaches human judgment on a difficult and noisy inference problem, but does not eliminate the underlying ambiguity.

The user-level study reports markedly higher performance, consistent with its richer aggregation and stricter labeling. Using 10-fold cross-validation on the balanced 278/278 user dataset, SVM is best: weekday accuracy 96.78%, precision 0.968, recall 0.968, F1 0.968, ROC-AUC 0.991; weekend accuracy 96.14%, precision 0.963, recall 0.961, F1 0.962, ROC-AUC 0.994. Logistic regression is close behind at 96.62% weekday and 95.17% weekend accuracy. RF, Bagging, J48, Naive Bayes, and AdaBoost also perform strongly, though below SVM and LR (Maity et al., 2018).

Interpreting these numbers requires attention to task definition. The tweet-level problem asks whether a single tweet was written under alcohol’s influence; the user-level problem asks whether a user is a predominant drunk texter. The latter is easier because it aggregates over many tweets and relies on repeated self-reports plus unanimous annotation (Maity et al., 2018, Joshi et al., 2016).

6. LLM-induced drunk language, normalization, and normative concerns

Recent work treats drunk language as an induced model behavior rather than a naturally occurring human signal. In a controlled ARC-Challenge study on GPT-5-mini, a sober control is compared with four single-sentence psychoactive persona prompts. Control accuracy is 0.45, while alcohol drops to 0.10, cocaine to 0.21, LSD to 0.19, and cannabis to 0.30. The dominant failure mode is formatting noncompliance with the required output contract “Answer: <LETTER>”: missing predictions rise from 21 in the control to 60 under alcohol, 55 under cocaine, 53 under LSD, and 38 under cannabis (Doudkin, 21 Dec 2025). Alcohol produces the largest collapse, with Fisher’s exact $p = 3.2 \times 10^{-8}$ versus control.

A broader safety study operationalizes drunk language through persona-based prompting, causal fine-tuning, and PPO-based post-training. The evaluated models are GPT-3.5, GPT-4, LLaMA2-7B, LLaMA3-8B, and Mistral-7B. Style induction succeeds quantitatively: held-out perplexity falls from 43.50 to 15.48 for LLaMA2-7B under fine-tuning and from 52.33 to 21.54 for LLaMA3-8B; “drunk reward” rises from 0.48 to 0.71 and from 0.45 to 0.78, respectively (Shetty et al., 19 Jan 2026).

Under this induced style, safety degrades. On JailbreakBench, Attack Success Rate rises to 31% / 51% / 41% for LLaMA2-7B under persona / FT / RL, to 21% / 41% for GPT-4 under persona / FT, to 76% / 63% / 35% for LLaMA3-8B, and to 90% / 74% / 53% for Mistral-7B (Shetty et al., 19 Jan 2026). Privacy behavior also worsens on ConfAIde. For GPT-3.5, Tier 3 leakage increases from 0.80 to 0.98 under persona, and control error rises from 0.00 to 0.73. For GPT-4, Tier 3 control error rises from 0.06 to 0.54 under persona and to 0.75 under FT; privacy error rises from 0.07 to 0.80 and 0.97 (Shetty et al., 19 Jan 2026).

These LLM findings recast drunk language as a stress test for instruction following and safety alignment. The observable mechanisms differ from human intoxication, but the induced outputs are deliberately designed to resemble oversharing, impaired judgment, typos, repetition, unusual capitalization, and random tangents (Shetty et al., 19 Jan 2026).

A separate line of work on “Jabberwockified” language is not a drunk-language study, but it has direct implications for normalization of severely degraded intoxication-like text. It shows that LLMs can recover meaning surprisingly well when function words, morphosyntax, and punctuation remain intact, with higher stop-word proportion predicting better recovery ($b = .71$, $t = 3.0$, $p = .004$), preposition use correlating positively with translation quality ($r = .27$, $\kappa = 0.71$0), and incremental context improving sentence-by-sentence translation ($\kappa = 0.71$1, $\kappa = 0.71$2) (Lupyan et al., 27 Feb 2026). This suggests that function words, auxiliaries, prepositions, and discourse structure are likely critical anchors for drunk-text normalization as well. The same study also shows that removing punctuation or key function-word classes significantly lowers recovery quality, which is consistent with the difficulty of interpreting highly degraded, fragmentary intoxication-related text (Lupyan et al., 27 Feb 2026).

Normative concerns run through the entire literature. Human-centered studies repeatedly note privacy, consent, stigmatization, bias, and downstream misuse risks in labeling individuals as drunk texters or inferring intoxication from public speech (Joshi et al., 2016, Maity et al., 2018). The LLM safety literature adds a different risk: persona or style adaptation can become a low-cost counter to safety tuning, increasing jailbreak susceptibility and privacy leakage without changing model weights or requiring elaborate attack infrastructure (Doudkin, 21 Dec 2025, Shetty et al., 19 Jan 2026). Across both settings, the central methodological tension is the same: drunk language is detectable enough to be operationally useful, but ambiguous enough that false positives, demographic confounds, and harmful deployment remain serious concerns.