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 1–10 of 16 papers

TitleStatusHype
TwiBot-22: Towards Graph-Based Twitter Bot DetectionCode2
State of the Art Models for Fake News Detection TasksCode1
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection BenchmarkCode1
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic ConsistencyCode1
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot DetectionCode1
Twitter Bot Detection using Diversity Measures—0
BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline—0
BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference—0
Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification—0
Language-Agnostic Twitter-Bot Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RGTAcc92.1—Unverified
2BotRGCNAcc89.6—Unverified
3GATAcc87—Unverified
4GCNAcc85.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DNA String Compression - Compression RatioAccuracy0.98—Unverified