Papers by Kathleen C. Fraser
Extracting Age-Related Stereotypes from Social Media Texts (2022.lrec-1)
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| Challenge: | a method for extracting age-related stereotypes from Twitter data is under-studied in NLP . stereotyping on the basis of protected characteristics has been understudied . |
| Approach: | They propose a method for extracting age-related stereotypes from Twitter data . they generate a corpus of 300,000 over-generalizations about four contemporary generations . |
| Outcome: | The method uncovers common stereotypes as reported in media and psychological literature . it also finds that stereotypes for different generations vary across topics . |
Recognizing UMLS Semantic Types with Deep Learning (D19-62)
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Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn, Muqun Li, Astha LaPlante, Khaldoun Zine El Abidine
| Challenge: | Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. |
| Approach: | They propose to use general and domain-specific information to combine general and specific information to create a new entity recognition method. |
| Outcome: | The proposed method produces a state-of-the-art result on a newly released dataset, MedMentions. |
Tackling Social Bias against the Poor: a Dataset and a Taxonomy on Aporophobia (2025.findings-naacl)
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Georgina Curto, Svetlana Kiritchenko, Muhammad Hammad Fahim Siddiqui, Isar Nejadgholi, Kathleen C. Fraser
| Challenge: | Poverty is a multidimensional phenomenon that affects 712 million people worldwide . |
| Approach: | They propose to annotate a corpus of English tweets from five world regions for the presence of harmful beliefs and discriminative actions against poor people on social media. |
| Outcome: | The proposed model can be used to identify, track and mitigat aporophobia on social media at scale. |
Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes (2024.lrec-main)
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| Challenge: | Gender stereotypes are pervasive beliefs about individuals based on their gender that shape societal attitudes, behaviours, and even opportunities. |
| Approach: | They propose eleven strategies to automatically counteract gender stereotypes by generating gender-based counter-stereotypes from a questionnaire to male and female participants. |
| Outcome: | The proposed strategies were perceived as offensive and/or implausible by the raters . humour, perspective-taking, counter-examples, and empathy for the speaker were perceived to be less effective. |
Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals (2025.naacl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. |
| Approach: | They propose large vision-Language Models to augment LLMs with visual inputs. |
| Outcome: | The proposed models condition generated text on both an input image and a visual prompt, enabling a variety of use cases such as visual question answering and multimodal chat. |
When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text (2025.findings-acl)
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| Challenge: | detecting AI-generated text on social media is difficult due to short text length and informal language of the internet . a recent study shows that detection of AI-generated posts is difficult under assumptions that an attacker has no knowledge of the generating model . |
| Approach: | They use open-source, closed-source and fine-tuned social media to detect AI-generated text . they use assumptions about knowledge of and access to the generating models to test detection . |
| Outcome: | a human study shows that detection of AI-generated social media posts is difficult . the study compared 505,159 posts from open-source, closed-source and fine-tuned models . |
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model (2021.acl-long)
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| Challenge: | Stereotypical language expresses widely-held beliefs about different social categories. |
| Approach: | They propose a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. |
| Outcome: | The proposed model compares favourably with survey-based studies in the psychological literature on stereotypes and shows that it is realistic and effective. |
Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)
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Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis
| Challenge: | Existing work on speech and language models has been limited by the size of available datasets. |
| Approach: | They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls. |
| Outcome: | The proposed model improves classification performance in English and French separately. |