BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns

• URL: http://arxiv.org/abs/2303.10214v2
• Date: Tue, 18 Apr 2023 16:47:12 GMT
• Title: BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns
• Authors: Jun Wu, Xuesong Ye and Chengjie Mou
• Abstract summary: Based on a real-world data set, we construct behavioral sequences from raw event logs. 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.
• Score: 4.386183132284449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Unmasking Social Bots: How Confident Are We? [41.94295877935867]
  We propose to address both bot detection and the quantification of uncertainty at the account level. This dual focus is crucial as it allows us to leverage additional information related to the quantified uncertainty of each prediction. Specifically, our approach facilitates targeted interventions for bots when predictions are made with high confidence and suggests caution (e.g., gathering more data) when predictions are uncertain.
  arXiv  Detail & Related papers  (2024-07-18T22:33:52Z)
• Adversarial Botometer: Adversarial Analysis for Social Bot Detection [1.9280536006736573]
  Social bots produce content that mimics human creativity. Malicious social bots emerge to deceive people with their unrealistic content. We evaluate the behavior of a text-based bot detector in a competitive environment.
  arXiv  Detail & Related papers  (2024-05-03T11:28:21Z)
• BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning [6.317191658158437]
  We propose a novel framework for social Bot detection with Self-Supervised Contrastive Learning (BotSSCL) BotSSCL uses contrastive learning to distinguish between social bots and humans in the embedding space to improve linear separability. We demonstrate BotSSCL's robustness against adversarial attempts to manipulate bot accounts to evade detection.
  arXiv  Detail & Related papers  (2024-02-06T06:13:13Z)
• My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection [69.99192868521564]
  Social platforms such as Twitter are under siege from a multitude of fraudulent users. Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks. We propose a node injection-based adversarial attack method designed to deceive bot detection models.
  arXiv  Detail & Related papers  (2023-10-11T03:09:48Z)
• From Online Behaviours to Images: A Novel Approach to Social Bot Detection [0.3867363075280544]
  A particular type of social accounts is known to promote unreputable content, hyperpartisan, and propagandistic information. We propose a novel approach to bot detection: we first propose a new algorithm that transforms the sequence of actions that an account performs into an image. We compare our performances with state-of-the-art results for bot detection on genuine accounts / bot accounts datasets well known in the literature.
  arXiv  Detail & Related papers  (2023-04-15T11:36:50Z)
• BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection [63.447493500066045]
  This work proposes a data driven learning model for the synthesis of keystroke biometric data. The proposed method is compared with two statistical approaches based on Universal and User-dependent models. Our experimental framework considers a dataset with 136 million keystroke events from 168 thousand subjects.
  arXiv  Detail & Related papers  (2022-07-27T09:26:15Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments [70.1864008701113]
  Bots are used in Github repositories to automate repetitive activities that are part of the distributed software development process. This paper proposes a ground-truth dataset, based on a manual analysis with high interrater agreement, of pull request and issue comments in 5,000 distinct Github accounts. We propose an automated classification model to detect bots, taking as main features the number of empty and non-empty comments of each account, the number of comment patterns, and the inequality between comments within comment patterns.
  arXiv  Detail & Related papers  (2020-10-07T09:30:52Z)
• Detection of Novel Social Bots by Ensembles of Specialized Classifiers [60.63582690037839]
  Malicious actors create inauthentic social media accounts controlled in part by algorithms, known as social bots, to disseminate misinformation and agitate online discussion. We show that different types of bots are characterized by different behavioral features. We propose a new supervised learning method that trains classifiers specialized for each class of bots and combines their decisions through the maximum rule.
  arXiv  Detail & Related papers  (2020-06-11T22:59:59Z)
• BeCAPTCHA-Mouse: Synthetic Mouse Trajectories and Improved Bot Detection [78.11535724645702]
  We present BeCAPTCHA-Mouse, a bot detector based on a neuromotor model of mouse dynamics. BeCAPTCHA-Mouse is able to detect bot trajectories of high realism with 93% of accuracy in average using only one mouse trajectory.
  arXiv  Detail & Related papers  (2020-05-02T17:40:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.