Adversarial Training for Satire Detection: Controlling for Confounding Variables (N19-1)
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| Challenge: | Existing methods for satire detection focus on satirical news based on article sources . satiric news are written with the aim of mimicking regular news in diction . |
| Approach: | They propose a model for satire detection with an adversarial component to control for the confounding variable of publication source. |
| Outcome: | The proposed model improves generalization performance to unseen publications with an adversarial component. |
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SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles (2021.acl-short)
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| Challenge: | a corpus for satire detection in Romanian news is based on satirical reporting . the goal is to ridicule public figures, politics or contemporary events . |
| Approach: | They propose a corpus for satire detection in Romanian news . they gather 55,608 public news articles from multiple real and satirical sources . |
| Outcome: | The proposed corpus is one of the largest corpora for satire detection regardless of language . it is the only one for the Romanian language, and the results show that it is low on the machine level compared to human level . |
Attending Sentences to detect Satirical Fake News (C18-1)
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| Challenge: | Existing approaches to capture news satire do not explore sentence and document difference . |
| Approach: | They propose a hierarchical deep neural network approach for satire detection . it is able to capture satirical news both at the sentence level and document level . |
| Outcome: | The proposed approach can capture satire at sentence and document levels. |
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues (D19-50)
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| Challenge: | a blurry line between fake news and protected-speech satire has been a struggle for social media platforms . purveyors of fake news have begun to masquerade as satirical sites to avoid being demoted . |
| Approach: | They propose to automatically classify fake news versus satire based on language differences . they hypothesize that nuances could be identified using semantic and linguistic cues . |
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Discovering Biased News Articles Leveraging Multiple Human Annotations (2020.lrec-1)
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| Challenge: | Political propaganda and one-sided views can be found in the news and can cause distrust in media. |
| Approach: | They propose to annotate politically biased news articles by an algorithm annotated by domain experts and crowd workers and to compare them to crowd workers. |
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We Can Detect Your Bias: Predicting the Political Ideology of News Articles (2020.emnlp-main)
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| Challenge: | a new study examines the role of media in predicting political ideology or bias in news articles . systematic exposure to bias in the news can foster intolerance and ideological segregation . |
| Approach: | They propose an adversarial media adaptation and a specially adapted triplet loss for predicting political ideology in news articles. |
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Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (2022.coling-1)
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| Challenge: | Recent advances to neural fake news generators have made it difficult to understand how misinformation generated by these models may best be confronted. |
| Approach: | They conduct feature-based analysis to gain an interpretative understanding of the linguistic attributes that neural fake news generators may most effectively exploit. |
| Outcome: | The proposed models are compared with models trained on subsets of features and confronted with increasingly advanced neural fake news. |
How to Generalize the Detection of AI-Generated Text: Confounding Neurons (2025.findings-emnlp)
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| Challenge: | Linguistic and domain confounders introduce spurious correlations, leading to poor out-of-distribution (OOD) performance. |
| Approach: | They propose a novel post-hoc, neuron-level intervention framework to disentangle AI-generated text detection factors from data-specific biases. |
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Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models (2022.emnlp-main)
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| Challenge: | Traditional adversarial examples involve finding a small perturbation that does not change an input’s true label but confuses the classifier into outputting a different prediction. |
| Approach: | They propose to use contrastive learning to increase model robustness against fickle adversarial examples by reducing the vulnerability of adversarials to fickle ones. |
| Outcome: | The proposed method improves model robustness against fickle and obstinate adversarial examples. |
Training Language Models under Resource Constraints for Adversarial Advertisement Detection (2021.naacl-industry)
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| Challenge: | e-commerce and social media sites require content moderation to ensure ethical standards . a tiered moderation workflow with automated components complements human experts . |
| Approach: | They propose techniques for training text classification models under resource constraints . they use weak supervision, curriculum learning and multi-lingual training to fine-tune BERT . |
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Multilingual Irony Detection with Dependency Syntax and Neural Models (2020.coling-main)
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Alessandra Teresa Cignarella, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Paolo Rosso, Farah Benamara
| Challenge: | Several semantic and syntactic devices can be used to express irony, causing the incongruity, determine the clash and play the role of irony triggers within a text. |
| Approach: | They propose to exploit linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |
| Outcome: | The proposed method exploits linguistic resources where syntax is annotated according to the Universal Dependencies scheme. |