TY - GEN
T1 - Demo
T2 - 2nd EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles, SmartSP 2024
AU - Aldeen, Mohammed
AU - Pradosh Silimkhan, Pranav
AU - Anderson, Ethan
AU - Kavuru, Taran
AU - Chang, Tsu Yao
AU - Ma, Jin
AU - Luo, Feng
AU - Hu, Hongxin
AU - Cheng, Long
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - The proliferation of online social media platforms has led to an increase in various types of content, including online hate. This trend poses substantial risks by amplifying harmful ideologies, inciting violence, and perpetuating discrimination. In response to this growing concern, Machine Learning (ML) has emerged as powerful tools for the automatic analysis of online hate. However, researchers from diverse fields are facing fundamental challenges in accessing essential resources, such as datasets, ML models, and analysis tools. In this paper, we present Integrative Cyberinfrastructure for Online Abuse Research (ICOAR), a system that automates the process of collecting, analyzing, and visualizing online abuse data. ICOAR pipeline begins with automated data collection from various social media platforms, followed by integration of state-of-the-art ML models to streamline the detection, categorization, and analysis of online abuse. ICOAR also features customizable tools for data visualizations, such as network and temporal analysis, catering to a range of research needs and expertise levels.
AB - The proliferation of online social media platforms has led to an increase in various types of content, including online hate. This trend poses substantial risks by amplifying harmful ideologies, inciting violence, and perpetuating discrimination. In response to this growing concern, Machine Learning (ML) has emerged as powerful tools for the automatic analysis of online hate. However, researchers from diverse fields are facing fundamental challenges in accessing essential resources, such as datasets, ML models, and analysis tools. In this paper, we present Integrative Cyberinfrastructure for Online Abuse Research (ICOAR), a system that automates the process of collecting, analyzing, and visualizing online abuse data. ICOAR pipeline begins with automated data collection from various social media platforms, followed by integration of state-of-the-art ML models to streamline the detection, categorization, and analysis of online abuse. ICOAR also features customizable tools for data visualizations, such as network and temporal analysis, catering to a range of research needs and expertise levels.
KW - Cyberinfrastructure
KW - Data Annotation
KW - Data Visualization
KW - Hate Speech Detection
KW - Multimodal Analysis
KW - Online Abuse
KW - Social Media Analysis
UR - https://www.scopus.com/pages/publications/105010040306
U2 - 10.1007/978-3-031-93354-7_18
DO - 10.1007/978-3-031-93354-7_18
M3 - Conference contribution
AN - SCOPUS:105010040306
SN - 9783031933530
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 361
EP - 365
BT - Security and Privacy in Cyber-Physical Systems and Smart Vehicles - 2nd EAI International Conference, SmartSP 2024, Proceedings
A2 - Hei, Xiali
A2 - Garcia, Luis
A2 - Kim, Taegyu
A2 - Kim, Kyungtae
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 November 2024 through 8 November 2024
ER -