Papers by Shima Khanehzar
Detecting Online Community Practices with Large Language Models: A Case Study of Pro-Ukrainian Publics on Twitter (2024.emnlp-main)
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| Challenge: | Existing methods for identifying practices within social media are not yet available. |
| Approach: | They propose a methodological workflow for computational identification of such practices within social media texts by using open-source models and OpenAI’s large language models. |
| Outcome: | The proposed method improves accuracy and supports context-sensitive moderation and advancing the understanding of online community dynamics. |
Probing Power by Prompting: Harnessing Pre-trained Language Models for Power Connotation Framing (2023.eacl-main)
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| Challenge: | Using pre-trained language models, we investigated whether word choices can encode subtle connotative information about power differentials between involved entities. |
| Approach: | They propose a framework to disentangle connotation frames implied by the predicate from its arguments and the sentence structure and to quantify predicates. |
| Outcome: | The proposed framework improves power connotation prediction accuracy by fine-tuning pre-trained language models. |
Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing (2023.acl-long)
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| Challenge: | a growing body of work attempts to automatically detect media frames in the news or social media, but most adopts a topic-like view on frames, evading modelling the broader document-level narrative. |
| Approach: | They propose an annotation paradigm that breaks a complex annotation task into a series of simple binary questions. |
| Outcome: | The proposed method is both effective and transparent in its predictions. |
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames (2021.naacl-main)
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| Challenge: | Existing models for news analysis lack transparency in their predictions. |
| Approach: | They propose a semi-supervised model that embeds local information into news articles . it can be used to improve automatic news analysis, authors argue . |
| Outcome: | The proposed model outperforms previous models and can be used with unlabeled training data. |