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A Survey on Automated Sarcasm Detection on Twitter

Paper · 2022 · Bleau Moores and Vijay Mago

Summary

Sarcasm usually wears a positive surface over a negative intent. When a sentiment or opinion-mining system takes such a Tweet literally, it gets the polarity wrong. Moores and Mago survey automated sarcasm detection on Twitter and restrict themselves to papers from high-quality venues. They argue that earlier surveys either had no stated selection method or appeared before transformers.

The survey covers four areas:

  • how Tweets are collected through the Twitter developer APIs and libraries such as TwitteR and Tweepy;
  • what sarcasm is and which cues signal it, including hashtags, emoji and emoticons;
  • the available sarcasm datasets;
  • the modelling families, from conventional classifiers to deep networks that use context and posting history.

The main finding is a clear move toward deep learning with induced rather than hand-crafted discrete features, helped by transformers. Context, often taken from a user's earlier posts, improves accuracy but costs efficiency. The authors name poor annotation as possibly the biggest open problem: hashtag self-labels, ambiguous emotes and labels produced by third-party tools all leak noise into the data.

Key points

  • Twitter policy allows sharing only Tweet IDs, not the Tweets themselves. Shared datasets therefore decay as Tweets are deleted, users are banned or accounts are made private, and they must be re-downloaded ("hydrated"), for example with Tweepy.
  • As of late 2021 the API access tiers were:
    • Essential: about 500,000 Tweets/month;
    • Elevated: 2,000,000 Tweets/month;
    • Academic Research: 10,000,000 Tweets/month, non-commercial use only.
  • Rule limits differ by product:
    • PowerTrack: up to 250,000 filter rules;
    • Filtered stream: 5, 25 or 1,000 rules depending on the tier;
    • Historical PowerTrack: 1,000 rules, returning data in 10-minute slices.
  • Common self-labels are #not, #sarcasm, #sarcastic and #irony, plus emoticons such as :p, ;) and ;P. None of these is standardized, and all are used ambiguously.
  • Corpora such as Sarcasm Corpus V2, SARC and IAC tag a whole post as sarcastic without marking which sentence carries the sarcasm. LSTM models struggle once a post runs past about five sentences.

Interesting ideas

  • I1. If a dataset is labelled by a third-party detector, the new model only learns to imitate that tool's mistakes, so label provenance has to be tracked and audited.
  • I2. Some sarcasm can only be caught with world knowledge about numbers, such as an obviously older mother said to have turned 29. A literal claim that clashes with plausible values is a strong irony cue.
  • I3. The same sentence can be sincere or sarcastic depending on when it is said relative to the events. A prediction about a team losing reads differently before kick-off than when that team is far ahead.
  • I4. Using a user's history to judge whether a post is out of character trades efficiency for accuracy. The authors suggest weighing that cost openly rather than always adding more context.
  • I5. Labels attached to a whole post hide which sentence actually carries the sarcasm, which hurts both training and analysis. Finer-grained annotation units help.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Silver data from witness parsers or LLM teachers should record where each label came from. Evaluation should run on gold data that is independent of those witnesses, so we never just measure agreement with a teacher.
  • I2 (world): Compiled knowledge of measures and value ranges (human ages, prices, durations) could flag implausible stated quantities. The flag would be passed to prag as an explainable candidate for irony.
  • I3 (frames): Irony detection in prag could compare a speech act's content against the event order and current states in the text world. It would mark an utterance as ironic when it contradicts a state already established, giving a traceable reason instead of a score.
  • I4 (frames): For a character in a fable, prag could compare an utterance with that character's earlier stance toward the same entity. A reversal would raise an irony or implicature hypothesis, and the costly history lookup would run only when cheaper cues are inconclusive.
  • I5 (frames): Irony and speech-act marks should attach to a specific clause or frame in the UD tree rather than to the whole text. The answer layer could then point to the exact sentence that inverts the meaning.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

📚 Shelves 4 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Psychology · 798 works Social Psychology · 129 works

🧭 Topics 1 Sentiment & emotion · 608 works

Cite

Cite as Bleau Moores and Vijay Mago. 2022. A Survey on Automated Sarcasm Detection on Twitter. arXiv:2202.02516.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2202.02516 · arXiv 2202.02516

@misc{moores2022survey,
  title = {{A Survey on Automated Sarcasm Detection on Twitter}},
  author = {Bleau Moores and Vijay Mago},
  year = {2022},
  doi = {10.48550/arxiv.2202.02516},
  eprint = {2202.02516},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2202.02516}
}

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