Zobrazeno 1 - 10
of 14 360
pro vyhledávání: '"Budak, A."'
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
Autor:
Miller, John A., Aldosari, Mohammed, Saeed, Farah, Barna, Nasid Habib, Rana, Subas, Arpinar, I. Budak, Liu, Ninghao
Deep Learning has been successfully applied to many application domains, yet its advantages have been slow to emerge for time series forecasting. For example, in the well-known Makridakis (M) Competitions, hybrids of traditional statistical or machin
Externí odkaz:
http://arxiv.org/abs/2401.13912
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
Autor:
Burcu Yıldırım Budak, Gözde Yazkan Akgül, Ayşe Burcu Erdoğdu Yıldırım, Buğu Subaşı, Yankı Yazgan
Publikováno v:
Psychiatry and Clinical Psychopharmacology, Vol 34, Iss 3, Pp 229-237 (2024)
Background: It is to examine how child psychiatry admissions, diagnosis and treatment trends in the second wave (September–December 2020/SD20) of the coronavirus disease 2019 (COVID-19) pandemic change compared to the pre-pandemic (SD19) and the fi
Externí odkaz:
https://doaj.org/article/11ca2c5fca5e4fd5b6c184a0909b13fd
Autor:
Davut Budak, Neslihan Kandil
Publikováno v:
Research in Sport Education and Sciences, Vol 26, Iss 3, Pp 126-138 (2024)
This study aimed to examine the levels of leisure involvement and perceived leisure constraints of individuals who ski and snowboard based on various characteristics. The study group of research consisted of 796 individuals (Mage=32.48±12.82) who sk
Externí odkaz:
https://doaj.org/article/cf9c948155f142ddb7bfb8da81d8b6d9
Autor:
Lisa Singh, Le Bao, Leticia Bode, Ceren Budak, Josh Pasek, Trivellore Raghunathan, Michael Traugott, Yanchen Wang, Nathan Wycoff
Publikováno v:
npj Vaccines, Vol 9, Iss 1, Pp 1-12 (2024)
Abstract Anti-vaccine sentiment during the COVID-19 pandemic grew at an alarming rate, leaving much to understand about the relationship between people’s vaccination status and the information they were exposed to. This study investigated the relat
Externí odkaz:
https://doaj.org/article/213c75e09d204407a4fef570416dab58