# Help! Need Advice on Identifying Advice

arXiv: https://arxiv.org/abs/2010.02494 · HTML: https://mlsanity.com/paper/2010.02494/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Analyzing Dataset Annotation Quality Management in the Wild](https://mlsanity.com/paper/2307.08153/) (2023)
- [AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence](https://mlsanity.com/paper/2404.11826/) (2024)
- [Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets](https://mlsanity.com/paper/2207.00907/) (2022)
- [Written Justifications are Key to Aggregate Crowdsourced Forecasts](https://mlsanity.com/paper/2109.07017/) (2021)
- [Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent Networks vs. Recurrent Networks](https://mlsanity.com/paper/2306.09705/) (2023)
- [Multimodal QUD: Inquisitive Questions from Scientific Figures](https://mlsanity.com/paper/2604.23733/) (2026)

## Abstract (arXiv)

Humans use language to accomplish a wide variety of tasks - asking for and giving advice being one of them. In online advice forums, advice is mixed in with non-advice, like emotional support, and is sometimes stated explicitly, sometimes implicitly. Understanding the language of advice would equip systems with a better grasp of language pragmatics; practically, the ability to identify advice would drastically increase the efficiency of advice-seeking online, as well as advice-giving in natural language generation systems. We present a dataset in English from two Reddit advice forums - r/AskParents and r/needadvice - annotated for whether sentences in posts contain advice or not. Our analysis reveals rich linguistic phenomena in advice discourse. We present preliminary models showing that while pre-trained language models are able to capture advice better than rule-based systems, advice identification is challenging, and we identify directions for future research. Comments: To be presented at EMNLP 2020.