Zobrazeno 1 - 10
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pro vyhledávání: '"Yang, Kai‐Cheng"'
Autor:
Scarano, Stephen, Vasudevan, Vijayalakshmi, Samory, Mattia, Yang, Kai-Cheng, Yang, JungHwan, Grabowicz, Przemyslaw A.
Social media platforms allow users to create polls to gather public opinion on diverse topics. However, we know little about what such polls are used for and how reliable they are, especially in significant contexts like elections. Focusing on the 20
Externí odkaz:
http://arxiv.org/abs/2405.11146
Recent advancements in generative artificial intelligence (AI) have raised concerns about their potential to create convincing fake social media accounts, but empirical evidence is lacking. In this paper, we present a systematic analysis of Twitter (
Externí odkaz:
http://arxiv.org/abs/2401.02627
Autor:
Yang, Kai-Cheng, Varol, Onur, Nwala, Alexander C., Sayyadiharikandeh, Mohsen, Ferrara, Emilio, Flammini, Alessandro, Menczer, Filippo
While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter, we focus on malicious social bot
Externí odkaz:
http://arxiv.org/abs/2312.17423
Autor:
Duan, Zening, Shao, Anqi, Hu, Yicheng, Lee, Heysung, Liao, Xining, Suh, Yoo Ji, Kim, Jisoo, Yang, Kai-Cheng, Chen, Kaiping, Yang, Sijia
While researchers often study message features like moral content in text, such as party manifestos and social media, their quantification remains a challenge. Conventional human coding struggles with scalability and intercoder reliability. While dic
Externí odkaz:
http://arxiv.org/abs/2312.05990
Fact checking can be an effective strategy against misinformation, but its implementation at scale is impeded by the overwhelming volume of information online. Recent artificial intelligence (AI) language models have shown impressive ability in fact-
Externí odkaz:
http://arxiv.org/abs/2308.10800
Autor:
Yang, Kai-Cheng, Menczer, Filippo
Publikováno v:
Journal of Quantitative Description: Digital Media (2024)
Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far re
Externí odkaz:
http://arxiv.org/abs/2307.16336
Autor:
Yang, Kai-Cheng, Menczer, Filippo
Although large language models (LLMs) have shown exceptional performance in various natural language processing tasks, they are prone to hallucinations. State-of-the-art chatbots, such as the new Bing, attempt to mitigate this issue by gathering info
Externí odkaz:
http://arxiv.org/abs/2304.00228
Autor:
Cresci, Stefano, Yang, Kai-Cheng, Spognardi, Angelo, Di Pietro, Roberto, Menczer, Filippo, Petrocchi, Marinella
Research on social bots aims at advancing knowledge and providing solutions to one of the most debated forms of online manipulation. Yet, social bot research is plagued by widespread biases, hyped results, and misconceptions that set the stage for am
Externí odkaz:
http://arxiv.org/abs/2303.17251
Autor:
Aiyappa, Rachith, DeVerna, Matthew R., Pote, Manita, Truong, Bao Tran, Zhao, Wanying, Axelrod, David, Pessianzadeh, Aria, Kachwala, Zoher, Kim, Munjung, Seckin, Ozgur Can, Kim, Minsuk, Gandhi, Sunny, Manikonda, Amrutha, Pierri, Francesco, Menczer, Filippo, Yang, Kai-Cheng
Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of politica
Externí odkaz:
http://arxiv.org/abs/2301.06287
Innovation diffusion in the networked population is an essential process that drives the progress of human society. Despite the recent advances in network science, a fundamental understanding of network properties that regulate such processes is stil
Externí odkaz:
http://arxiv.org/abs/2301.00151