mlsanityData to 14 Oct 2026

Automatically Identifying Complaints in Social Media

arXiv 1906.03890 ↗First posted 10 Jun 2019cs.CL cs.SIcited by 25

Not on the map. Why?

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.

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

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 3; 2023: 4; 2024: 7; 2025: 4; 2026: 1.

25 papers.

Sort:

By area:

  1. 2023Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmarkcited by 63
  2. 2021RAFT: A Real-World Few-Shot Text Classification Benchmarkcited by 44
  3. 2023PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillationcited by 25
  4. 2023Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Sciencecited by 19
  5. 2022Automatic Identification and Classification of Bragging in Social Mediacited by 12
  6. 2024SoMeLVLM: A Large Vision Language Model for Social Media Processingcited by 11
  7. 2021Point-of-Interest Type Prediction using Text and Imagescited by 10
  8. 2023Automated Few-shot Classification with Instruction-Finetuned Language Modelscited by 9
  9. 2024Fill In The Gaps: Model Calibration and Generalization with Synthetic Datacited by 8
  10. 2021Modeling the Severity of Complaints in Social Mediacited by 7
  11. 2024SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Taskscited by 7
  12. 2022Identifying and Characterizing Active Citizens who Refute Misinformation in Social Mediacited by 5
13 more
  1. 2024Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Sciencecited by 5
  2. 2021A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Taskscited by 4
  3. 2021Vietnamese Complaint Detection on E-Commerce Websitescited by 4
  4. 2024Data Diversity as Implicit Regularization: How Does Diversity Shape the Weight Space of Deep Neural Networks?cited by 4
  5. 2026The Appeal and Reality of Recycling LoRAs with Adaptive Mergingcited by 2
  6. 2021Span Detection for Aspect-Based Sentiment Analysis in Vietnamesecited by 1
  7. 2024Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Researchcited by 1
  8. 2025Deciphering the complaint aspects: Towards an aspect-based complaint identification model with video complaint dataset in financecited by 1
  9. 2022Analyzing the Intensity of Complaints on Social Mediacited by 0
  10. 2024"My life is miserable, have to sign 500 autographs everyday": Exposing Humblebragging, the Brags in Disguisecited by 0
  11. 2025Contextual Embedding-based Clustering to Identify Topics for Healthcare Service Improvementcited by 0
  12. 2025When Words Can't Capture It All: Towards Video-Based User Complaint Text Generation with Multimodal Video Complaint Datasetcited by 0
  13. 2025Predict Training Data Quality via Its Geometry in Metric Spacecited by 0