Lulwah AlKulaib
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    • Analyzing Networks with Hypergraphs: Detection, Classification, and Prediction
    • Hypergraph Text Classification for Mental Health Misleading Advice
    • Analyzing Prediction of Depression and Anxiety on Reddit: a Multi-task Learning Approach through GMMTL
    • Balancing the Scales: HyperSMOTE for Enhanced Hypergraph Classification
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    • 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 Datasets
    • Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate
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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 URL
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
Forecasting high-risk areas of covid-19 infection through socioeconomic and static spatial analysis Jan 1, 2021 →

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