SOTAVerified

Twitter Bot Detection

Academic studies estimate that up to 15% of Twitter users are automated bot accounts [1]. The prevalence of Twitter bots coupled with the ability of some bots to give seemingly human responses has enabled these non-human accounts to garner widespread influence. Hence, detecting non-human Twitter users or automated bot accounts using machine learning techniques has become an area of interest to researchers in the last few years.

[1] https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15587

Papers

Showing 1116 of 16 papers

TitleStatusHype
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings0
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot DetectionCode0
Evading classifiers in discrete domains with provable optimality guaranteesCode0
Detecting Bot Behaviour in Social Media using Digital DNA CompressionCode0
Bot and Gender Detection of Twitter Accounts Using Distortion and LSACode0
Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case StudyCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RGTAcc92.1Unverified
2BotRGCNAcc89.6Unverified
3GATAcc87Unverified
4GCNAcc85.8Unverified
#ModelMetricClaimedVerifiedStatus
1DNA String Compression - Compression RatioAccuracy0.98Unverified