Why Sarcasm Detection Is Hard

Sarcasm detection represents one of the most challenging problems in NLP. The difficulties include:

Context dependence: Sarcasm relies on situational knowledge and shared understanding that extends beyond the text itself.

Subtlety: Even humans struggle with sarcastic interpretation, especially in written text without vocal cues.

Cultural variability: Sarcastic expressions vary significantly across cultures and regions.

Annotation disagreement: Human annotators often disagree on what constitutes sarcasm.

These challenges raise a fundamental question: can sarcasm detection be well-defined as a computational problem? This case study explores what happens when we try—and reveals a common pitfall in dataset construction.

The Dataset: A Hidden Flaw

I used the Sarcasm News Headlines dataset, which combines headlines from The Onion (satirical) and The Huffington Post (traditional news). The dataset contains ~50,000 examples.

from datasets import load_dataset

dataset = load_dataset("raquiba/Sarcasm_News_Headline")
print(dataset["train"][0])
print(dataset["train"][1])

{'headline': 'thirtysomething scientists unveil doomsday clock of hair loss',
 'is_sarcastic': 1}
{'headline': 'dem rep. totally nails why congress is falling short on gender, racial equality',
 'is_sarcastic': 0}

The critical flaw: This dataset uses binary classification based on source domain—The Onion headlines are labeled sarcastic, HuffPost headlines are not. This creates a dangerous shortcut where models can learn to detect the publication source rather than sarcasm itself.

After preprocessing to standardize column names:

dataset = dataset.map(
    lambda example: {"text": example["headline"], "label": example["is_sarcastic"]},
    remove_columns=["headline", "article_link", "is_sarcastic"]
)

Fine-Tuning RoBERTa

I fine-tuned a pre-trained RoBERTa model using standard practices:

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments

model_name = "FacebookAI/roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

# Tokenize the data
def tokenize_function(examples):
    return tokenizer(examples["text"], truncation=True, max_length=512)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Training configuration
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=5,
    per_device_train_batch_size=32,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["test"],
    tokenizer=tokenizer,
)

trainer.train()

Results: Too Good to Be True

The model achieved high accuracy:

EpochTest Accuracy
196.3%
297.8%
399.4%
499.8%
599.8%

This should immediately raise red flags. Sarcasm detection is notoriously difficult—even for humans. Such high accuracy suggests the model learned something other than sarcasm detection.

My hypothesis: The model learned to distinguish between The Onion and HuffPost writing styles, not to detect sarcasm.

Interacting with the Model

Let’s test our hypothesis by interacting with the model.

First, let’s load the model and tokenizer:

from transformers import pipeline

model = AutoModelForSequenceClassification.from_pretrained('results/2024-02-25_20-24-51/checkpoint-4475')

clf = pipeline('text-classification', model=model, tokenizer=tokenizer)

Now, let’s test the model with some examples.

First, let’s try an Onion article from this week, something I know to be sarcastic and not in the training data. Let’s use “Alabama Supreme Court Justice Invokes ‘VeggieTales’ In Ruling”:

clf("Alabama Supreme Court Justice Invokes ‘VeggieTales’ In Ruling")

[{'label': 'LABEL_0', 'score': 0.99916672706604}]

The model is extremely confident that this is not sarcastic.

Let’s try a different Onion article, possibly even more difficult: Breaking News Trump Booed, Frozen Burritos, And More: This Week In Breaking News February 24, 2024:

clf("Breaking News Trump Booed, Frozen Burritos, And More: This Week In Breaking News February 24, 2024")

[{'label': 'LABEL_0', 'score': 0.9993497729301453}]

Again, very confident that this is not sarcastic. Hmm. It could be the temporal accuracy of our model just cannot capture the sarcasm of the Onion in 2024.

Let’s try one more Onion article, this one that is still recent but a bit more of a low-hanging fruit: Mom Only Likes The Other Outback Steakhouse:

clf("Mom Only Likes The Other Outback Steakhouse")

[{'label': 'LABEL_1', 'score': 0.9997231364250183}]

Finally, a correct prediction! The model is confident that this is sarcastic. So we see our model can detect sarcasm, but only very specific types of sarcasm. It’s not generalizing to new, unseen data. Not even in the same domain.

Let’s also try some headlines from the Huffington Post, which the model should predict as not sarcastic. Let’s try the five most recent headlines from the Huffington Post:

clf([
    "Donald Trump Won South Carolina — But There's 1 Big Caveat",
    "Man Sets Himself On Fire In Front Of Israeli Embassy In Washington",
    "Israeli Media Report Progress On Reaching A Temporary Truce In Gaza And A Hostage-Prisoner Exchange",
    "A White Liberal Is Trying To Oust A Progressive Black Congressman. His Comments Could Make That Job Harder.",
    "Climate Change-Fueled Winter Extremes Put 90% Of This Country At 'High Risk'"
])

[{'label': 'LABEL_0', 'score': 0.9993808269500732},
 {'label': 'LABEL_0', 'score': 0.9993786811828613},
 {'label': 'LABEL_0', 'score': 0.9985186457633972},
 {'label': 'LABEL_0', 'score': 0.9993883371353149},
 {'label': 'LABEL_0', 'score': 0.9993487000465393}]

The model is extremely confident that these are not sarcastic.

So we see that the model is able to detect sarcasm in some cases, but it’s not generalizing to new, unseen data. It’s not even generalizing to new, unseen data in the same domain. This is a common problem in machine learning. It’s easy to train a model that does well on a particular dataset, but it’s much harder to train a model that generalizes to new, unseen data. Murkier still, we set out to detect sarcasm and we’ve really only created a domain classifier. For fuzzier concepts like sarcasm, it’s important to be clear about what we’re actually detecting, and to collect the necessary scale of data to capture the full range of the concept.

Key Takeaways

This case study reveals a fundamental problem in ML: high accuracy doesn’t guarantee good performance. Here’s what actually happened:

  1. Dataset bias: Using publication source as a proxy for sarcasm created a shortcut for the model
  2. Domain classification: The model learned to distinguish writing styles, not detect sarcasm
  3. Poor generalization: New examples from the same sources often failed

This is a common pitfall when building datasets for subjective concepts. The lesson: always validate that your model learned what you intended, not just that it achieved high accuracy.

For better sarcasm detection, we’d need:

  • Diverse sources beyond two publications
  • Human annotation across multiple contexts
  • Careful evaluation on out-of-domain examples

Sometimes the most valuable ML projects are the ones that fail instructively—they teach us about our assumptions and the limitations of our approaches.