Twitter Bot Detection
10 papers with code • 2 benchmarks • 3 datasets
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
Benchmarks
These leaderboards are used to track progress in Twitter Bot Detection
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MGTAB
Most implemented papers
Evading classifiers in discrete domains with provable optimality guarantees
We introduce a graphical framework that (1) generalizes existing attacks in discrete domains, (2) can accommodate complex cost functions beyond $p$-norms, including financial cost incurred when attacking a classifier, and (3) efficiently produces valid adversarial examples with guarantees of minimal adversarial cost.
Bot and Gender Detection of Twitter Accounts Using Distortion and LSA
In this work, we present our approach for the Author Profiling task of PAN 2019.
Detecting Bot Behaviour in Social Media using Digital DNA Compression
In our approach, we employ a lossless compression algorithm on these Digital DNA sequences and use the compression statistics as a measure of predictability in the behaviour of a group of Twitter accounts.
State of the Art Models for Fake News Detection Tasks
aub-mind/fake-news-detection
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This paper presents state of the art methods for addressing three important challenges in automated fake news detection: fake news detection, domain identification, and bot identification in tweets.
Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study
Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
LzyFischer/BIC
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In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process.
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
graphdetec/mgtab
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However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research.
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
These tools employ machine learning and often achieve near perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications.
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection
czjdsg/lmbot
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For datasets without graph structure, we simply replace the GNN with an MLP, which has also shown strong performance.
