Lulwah AlKulaib
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    • Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
    • Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
    • Synthetic Arabic Medical Dialogues Using Advanced Multi-Agent LLM Techniques
    • Hypergraph Text Classification for Mental Health Misleading Advice
    • Analyzing Prediction of Depression and Anxiety on Reddit: a Multi-task Learning Approach through GMMTL
    • Analyzing Prediction of Depression and Anxiety on Reddit: a Multi-task Learning Approach through GMMTL
    • Balancing the Scales: HyperSMOTE for Enhanced Hypergraph Classification
    • 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
    • The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination
    • The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination
    • UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset
    • UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset
    • VT News covered our best paper award from ASONAM 2022.
    • DOD-Explainer: Explainable Drug Overdose Deaths Predictor from Crime and Socioeconomic Data
    • DOD-Explainer: Explainable Drug Overdose Deaths Predictor from Crime and Socioeconomic Data
    • HyperTwitter: A Hypergraph-based Approach to Identify Influential Twitter Users and Tweets
    • HyperTwitter: A Hypergraph-based Approach to Identify Influential Twitter Users and Tweets
    • Predicting depression and anxiety on reddit: a multi-task learning approach
    • 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
    • Twitter bot identification: An anomaly detection approach
    • Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis
    • Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis
    • Collect Ethically: Reduce Bias in Twitter Datasets
    • Collect Ethically: Reduce Bias in Twitter Datasets
    • Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate
    • NYT coverage of our "Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate" paper.
    • Detecting and characterizing bot-like behavior on Twitter
    • Detecting and Characterizing Bot-Like Behavior on Twitter
    • Twitter Bots Multiclass Classification Using Bot-Like Behavior Features.
    • Twitter Bots Multiclass Classification Using Bot-Like Behavior Features.
    • Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate
    • Recognizing Images of Eating Disorders in Social Media.
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Twitter bot identification: An anomaly detection approach

Jan 1, 2022·
Lulwah AlKulaib
,
Lei Zhang
,
Yanshen Sun
,
Chang-Tien Lu
· 0 min read
Cite
Type
Conference paper
Publication
2022 IEEE International Conference on Big Data (Big Data)
Last updated on Jan 1, 2022
← PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit Jan 1, 2022
Twitter bot identification: An anomaly detection approach Jan 1, 2022 →

© 2024 Lulwah AlKulaib. This work is licensed under CC BY NC ND 4.0