Graph-Hist: Graph Classification from Latent Feature Histograms with Application to Bot Detection

Magelinski, Thomas; Beskow, David M.; Carley, Kathleen M.

doi:10.1609/aaai.v34i04.5956

Cited by 12 publications

(5 citation statements)

References 15 publications

(23 reference statements)

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“…Pham et al [9] proposed a community-based random walk strategy that generates lowdimensional node representations while preserving local neighborhood relationships and intra-community structure. Magelinski et al [10] utilized the graph neural network to extract latent local features of social network graphs by aggregating nodes along onedimensional slices of the feature space, and then performed classification based on generated multi-channel histograms. Feng et al [11] introduced a framework called BotRGCN, which constructs a relation-based heterogeneous graph and enhances the model's ability to detect bots disguised as normal users by using multimodal user semantics and profiles.…”

Section: Graph-based Detection Approachesmentioning

confidence: 99%

MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

Zeng

Sun

2023

Electronics

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Social media bots pose potential threats to the online environment, and the continuously evolving anti-detection technologies require bot detection methods to be more reliable and general. Current detection methods encounter challenges, including limited generalization ability, susceptibility to evasion in traditional feature engineering, and insufficient exploration of user relationships. To tackle these challenges, this paper proposes MRLBot, a social media bot detection framework based on unsupervised representation learning. We design a behavior representation learning model that utilizes Transformer and a CNN encoder–decoder to simultaneously extract global and local features from behavioral information. Furthermore, a network representation learning model is proposed that introduces intra- and outer-community-oriented random walks to learn structural features and community connections from the relationship graph. Finally, the behavioral representation and relationship representation learning models are combined to generate fused representations for bot detection. The experimental results of four publicly available social network datasets demonstrate that the proposed method has certain advantages over state-of-the-art detection methods in this field.

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Section: Graph-based Detection Approachesmentioning

confidence: 99%

MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

Zeng

Sun

2023

Electronics

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“…This paper extracts features from user's meta data, tweets, user behavior, and feeds these features into Adaboost classifier. • FriendBot [Beskow and Carley, 2020]. This paper introduces network metrics into twitter bot detection tasks.…”

Section: B Experiments Details B1 Baseline Detailsmentioning

confidence: 99%

TwiBot-22: Towards Graph-Based Twitter Bot Detection

Feng¹,

Zhaoxuan²,

Wan³

et al. 2022

Preprint

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Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect. However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from limited dataset scale, incomplete graph structure, as well as low annotation quality. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches. In this paper, we propose TwiBot-22, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets. In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress. To facilitate further research, we consolidate all implemented codes and datasets into the TwiBot-22 evaluation framework, where researchers could consistently evaluate new models and datasets. The TwiBot-22 Twitter bot detection benchmark and evaluation framework are publicly available at https://twibot22.github.io/.

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“…Also notable is a model by Xiao et al [57] that uses various features to classify entire clusters of new accounts on LinkedIn to detect batches of fake accounts. Magelinski et al [36] demonstrates bot detection with graph classification by extracting a graph's latent local features and binning nodes together along 1-D cross sections of the feature space.…”

Section: Similarity Based Approaches To Bot Detectionmentioning

confidence: 99%

Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search

Beskow¹,

Carley²

2020

Preprint

Self Cite

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Social bots have emerged over the last decade, initially creating a nuisance while more recently used to intimidate journalists, sway electoral events, and aggravate existing social fissures. This social threat has spawned a bot detection algorithms race in which detection algorithms evolve in an attempt to keep up with increasingly sophisticated bot accounts. This cat and mouse cycle has illuminated the limitations of supervised machine learning algorithms, where researchers attempt to use yesterday's data to predict tomorrow's bots. This gap means that researchers, journalists, and analysts daily identify malicious bot accounts that are undetected by state of the art supervised bot detection algorithms. These analysts often desire to find similar bot accounts without labeling/training a new model, where similarity can be defined by content, network position, or both. A similarity based algorithm could complement existing supervised and unsupervised methods and fill this gap. To this end, we present the Bot-Match methodology in which we evaluate social media embeddings that enable a semi-supervised recursive nearest neighbors search to map an emerging social cybersecurity threat given one or more seed accounts.

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Graph-Hist: Graph Classification from Latent Feature Histograms with Application to Bot Detection

Cited by 12 publications

References 15 publications

MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

TwiBot-22: Towards Graph-Based Twitter Bot Detection

Bot-Match: Social Bot Detection with Recursive Nearest Neighbors Search

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