Lulwah AlKulaib
  • Bio
  • Papers
  • Experience
  • Recent & Upcoming Talks
    • Example Talk
  • Publications
    • Unveiling the Ethical Dimension of Urban Digital Twins: A Framework for Responsible and Sustainable Urban Planning
    • Adaptive Hierarchical GHSOM with Federated Learning for Context-Aware Anomaly Detection in IoT Networks
    • Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
    • Empowering Airline Route Decisions with LLM-Generated Pseudo-labels and Zero-Shot Review Prediction
    • Enhancing School Success Prediction with FRC and Merged GNN
    • Fhirviz: Multi-agent platform for fhir visualization to advance healthcare analytics
    • HyperSMOTE-MC: Enhancing Multiclass Bot Detection on X Through Hypergraph-Based Resampling
    • Synthetic arabic medical dialogues using advanced multi-agent llm techniques
    • Balancing the scales: Hypersmote for enhanced hypergraph classification
    • From Guest to Family: An Innovative Framework for Enhancing Memorable Experiences in the Hotel Industry
    • Hypergraph Text Classification for Mental Health Misleading Advice
    • UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset
    • Dod-explainer: Explainable drug overdose deaths predictor from crime and socioeconomic data
    • HyperTwitter: A Hypergraph-based Approach to Identify Influential Twitter Users and Tweets
    • Predicting depression and anxiety on reddit: a multi-task learning approach
    • PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit
    • Twitter bot identification: An anomaly detection approach
    • Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis
    • Collect Ethically: Reduce Bias in Twitter
    • Collect ethically: Reduce bias in twitter datasets
    • Detecting and characterizing bot-like behavior on Twitter
    • Twitter Bots Multiclass Classification Using Bot-Like Behavior Features
    • Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate
  • Experience
  • Publications
    • VT News covered our best paper award from ASONAM 2022.
    • An example preprint / working paper
    • NYT coverage of our "Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate" paper.

Collect ethically: Reduce bias in twitter datasets

Jan 1, 2019·
Lulwah Alkulaib
,
Abdulaziz Alhamadani
,
Taoran Ji
,
Chang-Tien Lu
· 0 min read
Last updated on Jan 1, 2019
← Collect Ethically: Reduce Bias in Twitter Jan 1, 2020
Detecting and characterizing bot-like behavior on Twitter Jan 1, 2018 →

© 2025 Lulwah AlKulaib. This work is licensed under CC BY NC ND 4.0

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