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Marcos Zampieri

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AAAI Conference 2025 Conference Paper

ARTICLE: Annotator Reliability Through In-Context Learning

  • Sujan Dutta
  • Deepak Pandita
  • Tharindu Cyril Weerasooriya
  • Marcos Zampieri
  • Christopher M. Homan
  • Ashiqur R. KhudaBukhsh

Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsically subjective, creating a challenging scenario for traditional quality assessment approaches because it is hard to distinguish disagreement due to poor work from that due to differences of opinions between sincere annotators. With the goal of increasing diverse perspectives in annotation while ensuring consistency, we propose ARTICLE, an in-context learning (ICL) framework to estimate annotation quality through self-consistency. We evaluate this framework on two offensive speech datasets using multiple LLMs and compare its performance with traditional methods. Our findings indicate that ARTICLE can be used as a robust method for identifying reliable annotators, hence improving data quality.

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AAAI Conference 2025 Short Paper

ARTICLE: Annotator Reliability Through In-Context Learning (Student Abstract)

  • Sujan Dutta
  • Deepak Pandita
  • Tharindu Cyril Weerasooriya
  • Marcos Zampieri
  • Christopher M. Homan
  • Ashiqur R. KhudaBukhsh

Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsically subjective, creating a challenging scenario for traditional quality assessment approaches because it is hard to distinguish disagreement due to poor work from that due to differences of opinions between sincere annotators. With the goal of increasing diverse perspectives in annotation while ensuring consistency, we propose ARTICLE, an in-context learning (ICL) framework to estimate annotation quality through self-consistency. We evaluate this framework on two offensive speech datasets using multiple LLMs and compare its performance with traditional methods. Our findings indicate that ARTICLE can be used as a robust method for identifying reliable annotators, hence improving data quality.

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JBHI Journal 2025 Journal Article

On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook

  • Ana-Maria Bucur
  • Andreea-Codrina Moldovan
  • Krutika Parvatikar
  • Marcos Zampieri
  • Ashiqur R. KhudaBukhsh
  • Liviu P. Dinu

Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e. g. , lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics.

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IJCAI Conference 2024 Conference Paper

A Survey of Multimodal Sarcasm Detection

  • Shafkat Farabi
  • Tharindu Ranasinghe
  • Diptesh Kanojia
  • Yu Kong
  • Marcos Zampieri

Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.

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JAIR Journal 2019 Journal Article

Automatic Language Identification in Texts: A Survey

  • Tommi Jauhiainen
  • Marco Lui
  • Marcos Zampieri
  • Timothy Baldwin
  • Krister Lindén

Language identification (“LI”) is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelfLI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

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v2026.09.13