Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Sheth, Paras"'
Autor:
Kapkiç, Ahmet, Mandal, Pratanu, Wan, Shu, Sheth, Paras, Gorantla, Abhinav, Choi, Yoonhyuk, Liu, Huan, Candan, K. Selçuk
While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poor substitute for causation. The conventional way to discover causal re
Externí odkaz:
http://arxiv.org/abs/2409.08419
Content moderation faces a challenging task as social media's ability to spread hate speech contrasts with its role in promoting global connectivity. With rapidly evolving slang and hate speech, the adaptability of conventional deep learning to the f
Externí odkaz:
http://arxiv.org/abs/2404.11036
Although social media platforms are a prominent arena for users to engage in interpersonal discussions and express opinions, the facade and anonymity offered by social media may allow users to spew hate speech and offensive content. Given the massive
Externí odkaz:
http://arxiv.org/abs/2403.12403
Autor:
Kumarage, Tharindu, Agrawal, Garima, Sheth, Paras, Moraffah, Raha, Chadha, Aman, Garland, Joshua, Liu, Huan
We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs) capable of generating high-quality text. While these LLMs have revolutionized text generation across various domains, they also pose significant risks to the info
Externí odkaz:
http://arxiv.org/abs/2403.01152
Machine Learning (ML) has become an integral aspect of many real-world applications. As a result, the need for responsible machine learning has emerged, focusing on aligning ML models to ethical and social values, while enhancing their reliability an
Externí odkaz:
http://arxiv.org/abs/2402.02696
In recent years, there has been a rapid proliferation of AI-generated text, primarily driven by the release of powerful pre-trained language models (PLMs). To address the issue of misuse associated with AI-generated text, various high-performing dete
Externí odkaz:
http://arxiv.org/abs/2310.05095
Social media platforms, despite their value in promoting open discourse, are often exploited to spread harmful content. Current deep learning and natural language processing models used for detecting this harmful content overly rely on domain-specifi
Externí odkaz:
http://arxiv.org/abs/2308.02080
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible t
Externí odkaz:
http://arxiv.org/abs/2308.02011
Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making. We present Upload, PREprocess, Visualize, and Evaluate (UPREVE), a user-friendly web-based graphical user interface (GUI)
Externí odkaz:
http://arxiv.org/abs/2307.13757
The rise of social media platforms has facilitated the formation of echo chambers, which are online spaces where users predominantly encounter viewpoints that reinforce their existing beliefs while excluding dissenting perspectives. This phenomenon s
Externí odkaz:
http://arxiv.org/abs/2307.04668