Papers with automatic detection
Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential (D19-50)
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| Challenge: | Calls to action are effective means of mobilization in social networks, but their potential for censorship and predicting offline protest events has not yet been evaluated. |
| Approach: | They examine the possibility of their automatic detection on historical data from the 2011-2013 protests in Bolotnaya, Russia. |
| Outcome: | The political calls to action can be annotated and detected with relatively high accuracy and have a moderate positive correlation with actual rally attendance. |
Synthetic Text Detection in the Age of Large Language Models: Watermark vs. Automatic Detection (2026.acl-industry)
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| Challenge: | Large Language Models (LLMs) are ubiquitous and capable of generating long coherent texts that look almost indistinguishable from human-written texts. |
| Approach: | They propose to use watermark and automatic detection to detect synthetic texts generated from Large Language Models (LLMs) they evaluate six different models, six different watermark techniques and two different automatic detectors for different levels of syntactic changes. |
| Outcome: | The proposed methods outperform on unperturbed and perturbed datasets on six different sizes of Qwen2.5 models, six watermark techniques and detectors, and two automatic detectors. |
A Study of Incorrect Paraphrases in Crowdsourced User Utterances (N19-1)
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| Challenge: | Developing bots requires high quality training samples, especially for unqualified crowd workers. |
| Approach: | They propose an annotated dataset for detecting quality issues in crowdsourced paraphrasing . they propose to use existing tools and services to provide baselines for identifying issues . |
| Outcome: | The proposed dataset provides a baseline for detecting unqualified paraphrases. |
DISPUTool 3.0: Fallacy Detection and Repairing in Argumentative Political Debates (2025.acl-demo)
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| Challenge: | DISPUTool 3.0 is a web-based application for identifying and fixing fallacious arguments in political debates. |
| Approach: | They propose a web-based application designed to identify and repair fallacious arguments in political debates. |
| Outcome: | The proposed tool is based on the ElecDeb60to20 dataset covering US presidential debates from 1960 to 2020. |
Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021.naacl-main)
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| Challenge: | Existing datasets make learning implicit abuse difficult, argues a new position paper . a lack of work on implicit abuse has limited the effectiveness of automatic detection . |
| Approach: | They argue that existing datasets make learning implicit abuse difficult . they propose a divide-and-conquer strategy to detect implicit abuse . |
| Outcome: | The proposed model could be improved to detect implicit abuse in a dataset with a standardized model. |
BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
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| Challenge: | a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles . |
| Approach: | They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection. |
| Outcome: | The proposed model is based on linguistic features and will be extended in the future . it will be used to improve the existing model and improve the tools in the field of fake news detection . |
PACO: a Corpus to Analyze the Impact of Common Ground in Spontaneous Face-to-Face Interaction (2020.lrec-1)
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| Challenge: | PAC0 is a conversational corpus of 15 face-to-face interactions lasting around 20 min each. |
| Approach: | They have created a conversational corpus of 15 face-to-face dyadic interactions lasting around 20 min each. |
| Outcome: | The compared corpus consists of 15 face-to-face dyadic interactions lasting around 20 min each. |
Data Augmentation for Hypernymy Detection (2021.eacl-main)
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| Challenge: | Existing methods for supervised inference have limited quality training data. |
| Approach: | They propose two techniques which generate new training examples from existing ones . they combine linguistic principles of hypernym transitivity and intersective modifier-noun composition . |
| Outcome: | The proposed techniques generate new training examples from existing datasets. |
Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models (2023.findings-emnlp)
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| Challenge: | a study comparing static and contextualized language models for subtle semantic shifts in Dutch and English shows that they can detect political connotations and associations. |
| Approach: | They propose a method for detecting subtle semantic shifts between political communities in Dutch and English using static and contextualized language models. |
| Outcome: | The proposed method outperforms static models on a Russian and Spanish task . it relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. |
Pre- and In-Parsing Models for Neural Empty Category Detection (P18-1)
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| Challenge: | Existing studies on empty category detection have shown positive effects on syntactic parsing . empty categories are used to indicate long-distance dependencies, discontinuous constituents, and certain dropped elements. |
| Approach: | They propose to use ECD to detect empty categories without syntactic analysis. |
| Outcome: | The proposed models outperform the prior state-of-the-art by significant margins. |
CDB: A Unified Framework for Hope Speech Detection Through Counterfactual, Desire and Belief (2025.findings-naacl)
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Tulio Ferreira Leite Da Silva, Gonzalo Freijedo Aduna, Farah Benamara, Alda Mari, Zongmin Li, Li Yue, Jian Su
| Challenge: | Using algorithms to model user-generated desires on social media, we propose a new approach to understanding and detection of hope speech. |
| Approach: | They propose a language-driven decomposition of the notional category hope and its automatic detection in a unified setting. |
| Outcome: | The proposed model captures future-oriented hopes through desires and beliefs and the counterfactuality of past unfulfilled wishes and regrets. |
GM-RKB WikiText Error Correction Task and Baselines (2020.lrec-1)
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| Challenge: | GM-RKB WikiText Error Correction Task for automatic detection and correction of typographical errors in Wikitext annotated pages. |
| Approach: | They propose to use a GM-RKB semantic wiki to automatically detect typographical errors in WikiText annotated pages. |
| Outcome: | The included corpus is based on a snapshot of the GM-RKB domain-specific semantic wiki consisting of a large collection of concepts, personages, and publications . Numerous Wikipedia pages were also included as additional training data in the task’s evaluation process. |
Detecting Stance in Media On Global Warming (2020.findings-emnlp)
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| Challenge: | a global warming denier might frame an opinion of an untrustworthy source with a predicate connoting doubt. |
| Approach: | They propose a dataset of stance-labeled GW sentences and train a BERT classifier to study opinion-framing in the global warming debate. |
| Outcome: | The proposed dataset of stance-labeled GW sentences and a BERT classifier study opinion-framing in the global warming debate. |
Generation of a Spanish Artificial Collocation Error Corpus (L18-1)
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| Challenge: | collocations are combinations of two elements where one (the base) is freely chosen, despite the limitations of the other (collocate) current tools for collocation error detection and correction focus on collocation validation and identification of miscollocations . |
| Approach: | They propose an algorithm for automatic generation of an artificial collocation error corpus of american English learners of Spanish that includes 17 different types of collocation errors. |
| Outcome: | The proposed algorithm can detect and classify collocation errors in learners' writings . collocation error detection and correction has not received the attention it deserves . |
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model (2021.naacl-main)
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| Challenge: | linguistic steganography is the practice of concealing a secret message in some cover data such that an eavesdropper is not even aware of the existence of the secret message. |
| Approach: | They propose to use edit-based linguistic steganography to generate genuine-looking texts by using a masked language model that eliminates painstaking rule construction and has a high payload capacity. |
| Outcome: | The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. |
The Search for Agreement on Logical Fallacy Annotation of an Infodemic (2022.lrec-1)
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Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss
| Challenge: | a parallel "infodemic" has emerged with the COVID-19 pandemic . logical fallacies can be subtly encoded in the structure of a document across multiple sentences . |
| Approach: | They evaluate an annotation schema for labeling logical fallacy types using linguist annotations . they propose to use a machine learning algorithm to train annotators for fallacy detection . |
| Outcome: | The proposed annotation schema is clear and non-overlapping for manual and system assignment. |
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022.emnlp-main)
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| Challenge: | a new approach to combat online hate speech is being proposed for NLG . existing methods to train NLG are limited to 2-turn interactions, while in real life, interactions can consist of multiple turns. |
| Approach: | They propose to combine human annotators with machine generated dialogues to create a dataset . DIALOCONAN is the first dataset comprising over 3000 fictitious multi-turn dialogues . |
| Outcome: | The proposed approach combines human experts over machine generated dialogues . it is the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator . |
Hate Speech Detection Based on Sentiment Knowledge Sharing (2021.acl-long)
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| Challenge: | Existing methods for hate speech detection are stereotyped and biased . et al., a paper examining the effectiveness of multitask learning in hate speech recognition tasks . |
| Approach: | They propose a hate speech detection framework based on sentiment knowledge sharing . they extract affective features of the target sentence and use sentiment features from external resources . |
| Outcome: | The proposed model can detect hate speech over two public datasets. |
What to Fuse and How to Fuse: Exploring Emotion and Personality Fusion Strategies for Explainable Mental Disorder Detection (2023.findings-acl)
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| Challenge: | Mental health disorders (MHD) are one of the greatest challenges facing our healthcare systems and modern societies in general. |
| Approach: | They integrate and extend the research by conducting extensive experiments with three types of deep learning-based fusion strategies: feature-level fusion, model fusion and task fusion. |
| Outcome: | The proposed techniques show that they can be used to improve mental health detection from textual data. |
A Survey of Cognitive Distortion Detection and Classification in NLP (2025.findings-emnlp)
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| Challenge: | despite momentum in natural language processing, the field remains fragmented . inconsistencies in CD taxonomies, task formulations and evaluation practices limit comparability . |
| Approach: | This review provides a comprehensive review of 38 studies spanning two decades . they map how CDs have been implemented in computational research and evaluate the methods applied. |
| Outcome: | The paper presents the first comprehensive review of 38 studies spanning two decades . it summarises common task setups and highlights persistent challenges to support more coherent research. |
Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness (2024.emnlp-main)
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| Challenge: | Existing zero-shot detection paradigms that use token cohesiveness are not available for large language models. |
| Approach: | They propose a generic dual-channel detection paradigm that uses token cohesiveness as a plug-and-play module to improve existing zero-shot detectors. |
| Outcome: | The proposed model is able to detect human-like text in black-box environments. |
Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can generate highly persuasive text, raising concerns about misuse for propaganda, manipulation, and other harmful purposes. |
| Approach: | They propose a multilingual benchmark to compare LLM-generated persuasive texts with human-written ones. |
| Outcome: | The proposed benchmark compares human-authored and LLM-generated persuasive texts . it finds that overtly persuasive LLMs are easier to detect than human-written ones . |
MemeIntel: Explainable Detection of Propagandistic and Hateful Memes (2025.emnlp-main)
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| Challenge: | Existing methods for label detection and explanation generation have been limited in understanding complex issues . identifying propaganda and hate in memes is essential for combating misinformation and minimizing harm . |
| Approach: | They propose an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes on English to solve these tasks. |
| Outcome: | The proposed model outperforms the current state-of-the-art in label detection and explanation generation. |
Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection (2026.acl-long)
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| Challenge: | Cognitive distortions are systematic errors in thinking that occur when individuals perceive and interpret external information, leading to a negative conclusion that does not correspond to reality. |
| Approach: | They propose a framework that combines Large Language Models with a Multiple-Instance Learning architecture to enhance interpretability and expression-level reasoning. |
| Outcome: | The proposed framework improves interpretability and expression-level reasoning on Korean and English datasets. |