# Automatically Identifying Complaints in Social Media

arXiv: https://arxiv.org/abs/1906.03890 · HTML: https://mlsanity.com/paper/1906.03890/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 25 of 25)

- [Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark](https://mlsanity.com/paper/2305.14938/) (2023)
- [RAFT: A Real-World Few-Shot Text Classification Benchmark](https://mlsanity.com/paper/2109.14076/) (2021)
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- [Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science](https://mlsanity.com/paper/2305.14310/) (2023)
- [Automatic Identification and Classification of Bragging in Social Media](https://mlsanity.com/paper/2203.05840/) (2022)
- [SoMeLVLM: A Large Vision Language Model for Social Media Processing](https://mlsanity.com/paper/2402.13022/) (2024)
- [Point-of-Interest Type Prediction using Text and Images](https://mlsanity.com/paper/2109.00602/) (2021)
- [Automated Few-shot Classification with Instruction-Finetuned Language Models](https://mlsanity.com/paper/2305.12576/) (2023)
- [Fill In The Gaps: Model Calibration and Generalization with Synthetic Data](https://mlsanity.com/paper/2410.10864/) (2024)
- [Modeling the Severity of Complaints in Social Media](https://mlsanity.com/paper/2103.12428/) (2021)
- [SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks](https://mlsanity.com/paper/2402.01980/) (2024)
- [Identifying and Characterizing Active Citizens who Refute Misinformation in Social Media](https://mlsanity.com/paper/2204.10080/) (2022)
- [Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science](https://mlsanity.com/paper/2409.14673/) (2024)
- [A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks](https://mlsanity.com/paper/2104.07944/) (2021)
- [Vietnamese Complaint Detection on E-Commerce Websites](https://mlsanity.com/paper/2104.11969/) (2021)
- [Data Diversity as Implicit Regularization: How Does Diversity Shape the Weight Space of Deep Neural Networks?](https://mlsanity.com/paper/2410.14602/) (2024)
- [The Appeal and Reality of Recycling LoRAs with Adaptive Merging](https://mlsanity.com/paper/2602.12323/) (2026)
- [Span Detection for Aspect-Based Sentiment Analysis in Vietnamese](https://mlsanity.com/paper/2110.07833/) (2021)
- [Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research](https://mlsanity.com/paper/2410.03545/) (2024)
- [Deciphering the complaint aspects: Towards an aspect-based complaint identification model with video complaint dataset in finance](https://mlsanity.com/paper/2503.00054/) (2025)
- [Analyzing the Intensity of Complaints on Social Media](https://mlsanity.com/paper/2204.09366/) (2022)
- ["My life is miserable, have to sign 500 autographs everyday": Exposing Humblebragging, the Brags in Disguise](https://mlsanity.com/paper/2412.20057/) (2024)
- [Contextual Embedding-based Clustering to Identify Topics for Healthcare Service Improvement](https://mlsanity.com/paper/2504.14068/) (2025)
- [When Words Can't Capture It All: Towards Video-Based User Complaint Text Generation with Multimodal Video Complaint Dataset](https://mlsanity.com/paper/2509.19952/) (2025)
- [Predict Training Data Quality via Its Geometry in Metric Space](https://mlsanity.com/paper/2510.15970/) (2025)

## Abstract (arXiv)

Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints. In this paper, we introduce the first systematic analysis of complaints in computational linguistics. We collect a new annotated data set of written complaints expressed in English on Twitter.footnoteData and code is available here: urlhttps://github.com/danielpreotiuc/complaints-social-media We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.