Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Budak, Ceren"'
Recent debates raised concerns that language models may favor certain viewpoints. But what if the solution is not to aim for a 'view from nowhere' but rather to leverage different viewpoints? We introduce Plurals, a system and Python library for plur
Externí odkaz:
http://arxiv.org/abs/2409.17213
How do Wikipedians maintain an accurate encyclopedia during an ongoing geopolitical conflict where state actors might seek to spread disinformation or conduct an information operation? In the context of the Russia-Ukraine War, this question becomes m
Externí odkaz:
http://arxiv.org/abs/2409.02304
Large language models (LLMs) are trained on broad corpora and then used in communities with specialized norms. Is providing LLMs with community rules enough for models to follow these norms? We evaluate LLMs' capacity to detect (Task 1) and correct (
Externí odkaz:
http://arxiv.org/abs/2407.04183
Publikováno v:
Journal of Quantitative Description: Digital Media (2024)
Social media enables activists to directly communicate with the public and provides a space for movement leaders, participants, bystanders, and opponents to collectively construct and contest narratives. Focusing on Twitter messages from social movem
Externí odkaz:
http://arxiv.org/abs/2406.13820
Exposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted an experiment (800+ participants, 40+ countries) where participants viewed creative ideas that were from ChatGPT or pri
Externí odkaz:
http://arxiv.org/abs/2401.13481
Many studies explore how people 'come into' misinformation exposure. But much less is known about how people 'come out of' misinformation exposure. Do people organically sever ties to misinformation spreaders? And what predicts doing so? Over six mon
Externí odkaz:
http://arxiv.org/abs/2401.13480
Social media enables the rapid spread of many kinds of information, from memes to social movements. However, little is known about how information crosses linguistic boundaries. We apply causal inference techniques on the European Twitter network to
Externí odkaz:
http://arxiv.org/abs/2304.03797
While cross-partisan conversations are central to a vibrant democracy, these are hard conversations to have, especially in the United States amidst unprecedented levels of partisan animosity. Such interactions often devolve into name-calling and pers
Externí odkaz:
http://arxiv.org/abs/2108.06830
Topics in conversations depend in part on the type of interpersonal relationship between speakers, such as friendship, kinship, or romance. Identifying these relationships can provide a rich description of how individuals communicate and reveal how r
Externí odkaz:
http://arxiv.org/abs/2105.06038
Research on online political communication has primarily focused on content in explicitly political spaces. In this work, we set out to determine the amount of political talk missed using this approach. Focusing on Reddit, we estimate that nearly hal
Externí odkaz:
http://arxiv.org/abs/2104.09560