João Filgueiras, Luís Barbosa, Gil Rocha, Henrique Lopes Cardoso, Luís Paulo Reis, João Pedro Machado, Ana Maria Oliveira
| Challenge: | Governmental institutions are using artificial intelligence to deal with specific problems and exploit their huge amounts of structured and unstructured information. |
| Approach: | They propose to use natural language processing and machine learning to classify complaints . they use feature-based approaches and traditional classifiers to analyze complaints based on citizen feedback . |
| Outcome: | The proposed methods have accuracy scores above 70% and can be used to improve public services. |
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Piyawat Lertvittayakumjorn, Ivan Petej, Yang Gao, Yamuna Krishnamurthy, Anna Van Der Gaag, Robert Jago, Kostas Stathis
| Challenge: | Fig. 1 illustrates the major components and workflow of our proposed system to improve the efficiency of complaints investigation for nursing and midwifery regulators. |
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Automatically Identifying Complaints in Social Media (P19-1)
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| Challenge: | Complaining is a basic speech act used to express a negative mismatch between reality and expectations in a particular situation. |
| Approach: | They present a systematic analysis of complaints in computational linguistics . they collect annotated data set of written complaints expressed on Twitter . |
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Modeling the Severity of Complaints in Social Media (2021.naacl-main)
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| Challenge: | Complaining is a speech act used by humans to communicate a negative mismatch between reality and expectations . recent work on modeling complaints in natural language processing (NLP) has focused on distinguishing complaints from non-complaints in social media. |
| Approach: | They propose to classify complaints into various severity levels based on the face-threat that the complainer is willing to undertake and their purpose. |
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Complaint Identification in Social Media with Transformer Networks (2020.coling-main)
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| Challenge: | Existing work on identifying complaints in social media has focused on feature-based and task-specific neural network models. |
| Approach: | They evaluate a battery of neural models underpinned by transformer networks and combine them with linguistic information to predict complaints. |
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Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)
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| Challenge: | Complaining is an expression of negative emotions communicated due to a discrepancy between reality and expectations. |
| Approach: | They propose to use an explainable complaint dataset to generate a commonsense-aware generative framework that can predict the complaint cause, severity level, emotion, and polarity of the text. |
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LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification (2021.findings-acl)
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| Challenge: | Automated fact-checking is time-consuming and cannot scale due to a lack of suitable training data. |
| Approach: | They propose to use a dataset to automatically check facts and a text classifier to infer the likelihood of the input being a piece of fake-news. |
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A Survey on Automated Fact-Checking (2022.tacl-1)
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| Challenge: | Fact-checking is an essential task in journalism due to the speed with which information and misinformation can spread in the media ecosystem. |
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Environmental Claim Detection (2023.acl-short)
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Measuring the Impact of Readability Features in Fake News Detection (2020.lrec-1)
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Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva, Carolina Scarton
| Challenge: | Recent efforts to detect fake news use language-based approaches to detect news articles . authors show that readability features can improve classification accuracy . |
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Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection (2025.acl-long)
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| Challenge: | Existing tools for sexism detection fail to capture subtle distinctions within sexist content, limiting their practical applicability. |
| Approach: | They propose two techniques to address class imbalance and nuanced nature of sexist language . definition-based data augmentation leverages category-specific definitions to generate semantically-aligned examples . |
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