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Twitter Bot Detection
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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
논문
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Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study
State of the Art Models for Fake News Detection Tasks
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings
Evading classifiers in discrete domains with provable optimality guarantees
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