Search Results for author:
Found 9 papers, 8 papers with code
Ideological Orientation and Extremism Detection in Online Social Networking Sites: A Systematic Review
The rise of social networking sites has reshaped digital interactions, becoming fertile grounds for extremist ideologies, notably in the United States.
ThreatGram 101 - Extreme Telegram Replies Data with Threat Levels
To address this gap, we present a comprehensive dataset of user replies from Telegram channels associated with political extremism or the cyberbullying of public officials in the United States.
Comprehensive dataset of user-submitted articles with ideological and extreme bias from Reddit
In total, our combined dataset of 1. 3 million articles collected from 55 different subreddits will assist in examining radicalized communities and providing discourse analysis in associated subreddits, enhancing understanding of the language used in articles shared within radicalized Reddit communities and offering insights into extreme bias in media content.
RICo: Reddit ideological communities
Previous work addressed label curation by using ideological subreddits (r/Liberal and r/Conservative for Liberal and Conservative classes) to label the articles shared on those subreddits according to their prescribed ideologies, albeit with a limited dataset.
Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study
This research addresses the crucial challenge of effectively measuring threats in social media comments targeting voting, public officials, and institutions in the United States.
Classifying the Ideological Orientation of User-Submitted Texts in Social Media
With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i. e., conservative or liberal).
Ranked #1 on
Classification
on Reddit Ideology Database
Deep Residual Network based Automatic Image Grading for Diabetic Macular Edema
Diabetic Macular Edema (DME) is an advanced symptom of diabetic retinopathy that affects central vision of diabetes patients.
Ranked #1 on
Medical Image Classification
on IDRiD
(Accuracy (% ) metric)
Breast cancer histology classification using Deep Residual Networks
In this work, in order to improve the computer aided diagnosis systems’ performance on histopathological image analysis, we have proposed an approach with image pre-processing followed by a deep learning method to classify the breast cancer histology images into four classes; (i) normal tissue, (ii) benign lesion, (iii) in-situ carcinoma, and (iv) invasive carcinoma.
Ranked #1 on
Breast Cancer Histology Image Classification
on ICIAR 2018 Grand Challenge on Breast Cancer Histology Images
Breast Cancer Histology Image Classification
Classification
+1
Imagined speech classification using EEG
The objective of this work is to assess the possibility of using (Electroencephalogram) EEG for communication between different subjects.
Ranked #1 on
EEG Signal Classification
on .
