Výsledky vyhledávání - "Oghaz, Toktam"

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Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media

Recent advances in natural language processing (NLP) in online social media are evidently owed to large-scale datasets. However, labeling, storing, and processing a large number of textual data points, e.g., tweets, has remained challenging. On top o

Externí odkaz: http://arxiv.org/abs/2202.00540

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A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19

Autor: Mutlu, Ece Çiğdem, Oghaz, Toktam A., Jasser, Jasser, Tütüncüler, Ege, Rajabi, Amirarsalan, Tayebi, Aida, Ozmen, Ozlem, Garibay, Ivan

At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, specially the Twitte

Externí odkaz: http://arxiv.org/abs/2009.01188

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Quantifying Latent Moral Foundations in Twitter Narratives: The Case of the Syrian White Helmets Misinformation

Autor: Mutlu, Ece Çiğdem, Oghaz, Toktam, Tütüncüler, Ege, Jasser, Jasser, Garibay, Ivan

For years, many studies employed sentiment analysis to understand the reasoning behind people's choices and feelings, their communication styles, and the communities which they belong to. We argue that gaining more in-depth insight into moral dimensi

Externí odkaz: http://arxiv.org/abs/2004.13142

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Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model

Autor: Oghaz, Toktam A., Mutlu, Ece C., Jasser, Jasser, Yousefi, Niloofar, Garibay, Ivan

Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To a

Externí odkaz: http://arxiv.org/abs/2004.06793

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Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and uti

Externí odkaz: http://arxiv.org/abs/2003.11611

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Review on Learning and Extracting Graph Features for Link Prediction

Autor: Mutlu, Ece C., Oghaz, Toktam A., Rajabi, Amirarsalan, Garibay, Ivan

Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and

Externí odkaz: http://arxiv.org/abs/1901.03425

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A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19

Autor: Mutlu, Ece C., Oghaz, Toktam, Jasser, Jasser, Tutunculer, Ege, Rajabi, Amirarsalan, Tayebi, Aida, Ozmen, Ozlem, Garibay, Ivan

Publikováno v: In Data in Brief December 2020 33

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Dissertation/ Thesis

Low-Resource Machine Learning Techniques for the Analysis of Online Social Media Textual Data

Autor: Amanzadeh Oghaz, Toktam

Publikováno v: Electronic Theses and Dissertations, 2020-

Low-resource and label-efficient machine learning methods can be described as the family of statistical and machine learning techniques that can achieve high performance without needing a substantial amount of labeled data. These methods include both

Externí odkaz: https://stars.library.ucf.edu/etd2020/1734
https://stars.library.ucf.edu/context/etd2020/article/2733/viewcontent/CFE0009820.pdf

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