@article{halevy2026evaluating,title={Evaluating Large Language Models for Antisemitic Incident Classification},author={Halevy, Karina and Mendelsohn, Julia and Park, Chan Young and Tsvetkov, Yulia and Sap, Maarten},journal={arXiv preprint arXiv:2607.04890},year={2026},url={https://arxiv.org/abs/2607.04890},}
Persuasion Index: A Theory-Guided Framework for Persuasion Analysis
Liancheng Gong, Zhiyang Wang, Yiwei Xu, and Julia Mendelsohn
@inproceedings{gong-2026-limits,title={The Limits of De-politicizing—and Also of Annotation: A Case Study in Russian Media Outlets' Social Media Posts, 2016-2024},author={Gong, Liancheng and Hopkins, Daniel J. and Wolken, Samuel},booktitle={Proceedings of the International AAAI Conference on Web and Social Media},volume={20},number={1},pages={889--909},year={2026},url={https://ojs.aaai.org/index.php/ICWSM/article/view/42672},doi={https://doi.org/10.1609/icwsm.v20i1.42672}}
Motivational Framing in Networked Publics on Indian Social Media: A Case Study of #HindusUnderAttack
Anmol Panda, Syeda Zainab Akbar, Julia Mendelsohn, Ceren Budak, and Matthew Bui
In Proceedings of the International AAAI Conference on Web and Social Media , 2026
@inproceedings{panda-2026-motivational,title={Motivational Framing in Networked Publics on Indian Social Media: A Case Study of #HindusUnderAttack},author={Panda, Anmol and Akbar, Syeda Zainab and Mendelsohn, Julia and Budak, Ceren and Bui, Matthew},booktitle={Proceedings of the International AAAI Conference on Web and Social Media},volume={20},number={1},pages={1721--1737},year={2026},url={https://ojs.aaai.org/index.php/ICWSM/article/view/42720},doi={https://doi.org/10.1609/icwsm.v20i1.42720}}
2025
When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models
Julia Mendelsohn, and Ceren Budak
In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2025
@inproceedings{mendelsohn-budak-2025-people,title={When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models},author={Mendelsohn, Julia and Budak, Ceren},editor={Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},month=jul,year={2025},address={Vienna, Austria},publisher={Association for Computational Linguistics},url={https://aclanthology.org/2025.acl-long.398/},doi={10.18653/v1/2025.acl-long.398},pages={8079--8103},isbn={979-8-89176-251-0},}
How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment
Joshua Ashkinaze, Julia Mendelsohn, Li Qiwei, Ceren Budak, and Eric Gilbert
In Proceedings of the ACM Collective Intelligence Conference , 2025
Exposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted a dynamic experiment (800+ participants, 40+ countries) where participants viewed creative ideas that were from ChatGPT or prior experimental participants, and then brainstormed their own idea. We varied the number of AI-generated examples (none, low, or high exposure) and if the examples were labeled as “AI” (disclosure). We find that high AI exposure (but not low AI exposure) did not affect the creativity of individual ideas but did increase the average amount and rate of change of collective idea diversity. AI made ideas different, not better. There were no main effects of disclosure. We also found that self-reported creative people were less influenced by knowing an idea was from AI and that participants may knowingly adopt AI ideas when the task is difficult. Our findings suggest that introducing AI ideas may increase collective diversity but not individual creativity.
@inproceedings{ashkinaze2025ai,author={Ashkinaze, Joshua and Mendelsohn, Julia and Qiwei, Li and Budak, Ceren and Gilbert, Eric},title={How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment},year={2025},isbn={9798400714894},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3715928.3737481},doi={10.1145/3715928.3737481},booktitle={Proceedings of the ACM Collective Intelligence Conference},pages={198–213},series={CI '25},}
What’s in a Prompt?: A Large-Scale Experiment to Assess the Impact of Prompt Design on the Compliance and Accuracy of LLM-Generated Text Annotations
Shubham Atreja, Joshua Ashkinaze, Lingyao Li, Julia Mendelsohn, and Libby Hemphill
Proceedings of the International AAAI Conference on Web and Social Media, 2025
@article{atreja2025prompt,title={What's in a Prompt?: A Large-Scale Experiment to Assess the Impact of Prompt Design on the Compliance and Accuracy of LLM-Generated Text Annotations},volume={19},url={https://ojs.aaai.org/index.php/ICWSM/article/view/35807},doi={10.1609/icwsm.v19i1.35807},number={1},journal={Proceedings of the International AAAI Conference on Web and Social Media},author={Atreja, Shubham and Ashkinaze, Joshua and Li, Lingyao and Mendelsohn, Julia and Hemphill, Libby},year={2025},month=jun,pages={122-145},}
AI-LieDar : Examine the Trade-off Between Utility and Truthfulness in LLM Agents
Zhe Su, Xuhui Zhou, Sanketh Rangreji, Anubha Kabra, Julia Mendelsohn, Faeze Brahman, and Maarten Sap
In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , 2025
@inproceedings{su2025ai,title={{AI}-{L}ie{D}ar : Examine the Trade-off Between Utility and Truthfulness in {LLM} Agents},author={Su, Zhe and Zhou, Xuhui and Rangreji, Sanketh and Kabra, Anubha and Mendelsohn, Julia and Brahman, Faeze and Sap, Maarten},editor={Chiruzzo, Luis and Ritter, Alan and Wang, Lu},booktitle={Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},month=apr,year={2025},address={Albuquerque, New Mexico},publisher={Association for Computational Linguistics},pages={11867--11894},isbn={979-8-89176-189-6},}
T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning
@inproceedings{ethiraj2025tvec,title={T-VEC: A Telecom-Specific Vectorization Model with Enhanced Semantic Understanding via Deep Triplet Loss Fine-Tuning},author={Ethiraj, Vignesh and David, Ashwath and Menon, Sidhanth and Vijay, Divya and Kannan, Vidhyakshaya},booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track},editor={Potdar, Saloni and Rojas-Barahona, Lina and Montella, Sebastien},year={2025},month=nov,address={Suzhou, China},publisher={Association for Computational Linguistics},pages={2449--2460},doi={10.18653/v1/2025.emnlp-industry.168},isbn={979-8-89176-333-3},}