BotRGCN

Feng, Shangbin; Wan, Herun; Wang, Ningnan; Luo, Minnan

doi:10.1145/3487351.3488336

Cited by 66 publications

(41 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Seyed et al [18] firstly apply graph convolutional neural networks to learn one node's representation based on account features of itself and its neighbors. Shangbin et al [19] applied GCN algorithm on the user following relationship graph, then represented raw node features including user profile, categorical and numerical data of account activity. Shangbin et al [20] constructs two kinds of heterogeneity structures, including relation and influence, leveraging the topology to identify the difference between genuine users and social bots.…”

Section: Bot Detection Approachesmentioning

confidence: 99%

Botshape: A Novel Social Bots Detection Approach via Behavioral Patterns

Wu¹,

Ye²,

Mou³

2023

Advanced Information Technologies and Applications

View full text Add to dashboard Cite

An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e.g., misinformation, rumor, and spam) on genuine users. Based on a real-world data set, we construct behavioral sequences from raw event logs. After extracting critical characteristics from behavioral time series, we observe differences between bots and genuine users and similar patterns among bot accounts. We present a novel social bot detection system BOTSHAPE, to automatically catch behavioral sequences and characteristics as features for classifiers to detect bots. We evaluate the detection performance of our system in ground-truth instances, showing an average accuracy of 98.52% and an average f1-score of 96.65% on various types of classifiers. After comparing it with other research, we conclude that BOTSHAPE is a novel approach to profiling an account, which could improve performance for most methods by providing significant behavioral features

show abstract

Section: Bot Detection Approachesmentioning

confidence: 99%

Botshape: A Novel Social Bots Detection Approach via Behavioral Patterns

Wu¹,

Ye²,

Mou³

2023

Advanced Information Technologies and Applications

View full text Add to dashboard Cite

show abstract

“…The earliest GNN-based social bot detection method [22] primarily combined graph convolutional neural networks (GCNs) with multi-layer perception and belief propagation. By constructing heterogeneous graphs and extracting original node features using pre-trained language models to obtain initial node embeddings, followed by aggregation of R-GCN models, BotRGCN [7] demonstrated superior performance compared to conventional detection models on the TwiBot-20 [23], which is a recently released benchmark dataset for social bot detection. Another method proposed in [6] applied heterogeneous graphs and introduced a relational graph converter influenced by natural language processing to model user fusion and learn node representations for improved social bots detection, outperforming BotRGCN.…”

Section: Gnn-based Social Bot Detectionmentioning

confidence: 99%

“…The latest progress in graph neural networks (GNNs) [5] has facilitated a more comprehensive understanding of the implicit relation between bot users and legitimate users, thereby increasing the complexity of detection. GNN-based methods [6,7] represent the detection process as a node classification problem.…”

Section: Introductionmentioning

confidence: 99%

Dual-Channel Relation Mixup Strategy for Social Bot Detection Based on Graph Neural Network

Liu

Shi

et al. 2023

Preprint

View full text Add to dashboard Cite

In social bot detection, accurate labeling needs to be done manually, resulting in large-scale labels are difficult to obtain. However, the limited labels mean that there is less data available for the training set, which may result in over-fitting. Therefore, we proposed a novel data augmentation strategy named Dual-Channel Relation Mixup (DCRM) to address potential over-fitting issues caused by a small training set. In our strategy, we firstly construct two channels that input different account relations, then combine the resulting aggregated representations from both channels before passing them on to the next layer of the “message passing” mechanism. More critically, our DCRM is a general skill that can be equipped with many other backbone models (e.g., GCN, GraphSAGE, GAT, and RGCN) to enhance performance. Extensive experiments on several benchmarks demonstrate that our proposed DCRM strategy is an effective way to enhance the performance of bot detection based on graph neural networks. Furthermore, it also exhibits better generalization ability and stability, especially when working with smaller datasets.

show abstract

“…Bot account detection on Twitter is a challenging task due to the bots' increasing sophistication. Studies towards this direction make use of supervised ((Efthimion, Payne, and Proferes 2018;Feng et al 2021a;Kantepe and Ganiz 2017;Ng, Robertson, and Carley 2022;Rodríguez-Ruiz et al 2020;Abreu, Ralha, and Gondim 2020) or unsupervised ML models ( (Chavoshi, Hamooni, and Mueen 2016;Minnich et al 2017;Anwar and Yaqub 2020;Chen et al 2017;Chen 2018;Wei and Nguyen 2019;Feng et al 2021b;Antonakaki, Fragopoulou, and Ioannidis 2021)), deep neural networks (Kudugunta and Ferrara 2018;Cai, Li, and Zengi 2017;Ilias and Roussaki 2021;Luo et al 2020;Hayawi et al 2022;Feng et al 2021c;Ping and Qin 2018), language agnostic models ((Knauth 2019), word embeddings (Wei and Nguyen 2019;Feng et al 2021c;Cai, Li, and Zengi 2017;Heidari, Jones, and Uzuner 2020), explainable ML (Kouvela, Dimitriadis, and Vakali 2020) and ensemble ML (Shukla, Jagtap, and Patil 2021).…”

Section: Related Workmentioning

confidence: 99%

Discovery and Classification of Twitter Bots

et al. 2022

View full text Add to dashboard Cite

Twitter as one of the most popular social networks, offers a means for communication and online discourse, which unfortunately has been the target of bots and fake accounts, leading to the manipulation and spreading of false information. Towards this end, we gather a challenging, multilingual dataset of social discourse on Twitter, originating from 9M users regarding the recent Russo-Ukrainian war, in order to detect the bot accounts and the conversation involving them. We collect the ground truth for our dataset through the Twitter API suspended accounts collection, containing approximately 343K of bot accounts and 8M of normal users. Additionally, we use a dataset provided by Botometer-V3 with 1,777 Varol, 483 German accounts, and 1,321 US accounts. Besides the publicly available datasets, we also manage to collect 2 independent datasets around popular discussion topics of the 2022 energy crisis and the 2022 conspiracy discussions. Both of the datasets were labeled according to the Twitter suspension mechanism. We build a novel ML model for bot detection using the state-of-the-art XGBoost model. We combine the model with a high volume of labeled tweets according to the Twitter suspension mechanism ground truth. This requires a limited set of profile features allowing labeling of the dataset in different time periods from the collection, as it is independent of the Twitter API. In comparison with Botometer our methodology achieves an average 11% higher ROC-AUC score over two real-case scenario datasets.

show abstract

BotRGCN

Cited by 66 publications

References 10 publications

Botshape: A Novel Social Bots Detection Approach via Behavioral Patterns

Botshape: A Novel Social Bots Detection Approach via Behavioral Patterns

Dual-Channel Relation Mixup Strategy for Social Bot Detection Based on Graph Neural Network

Discovery and Classification of Twitter Bots

Contact Info

Product

Resources

About