Arrow Research search

Author name cluster

Filippo Menczer

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers
2 author rows

Possible papers

5

JAAMAS Journal 2026 Journal Article

MySpiders: Evolve Your Own Intelligent Web Crawlers

  • Gautam Pant
  • Filippo Menczer

Abstract The dynamic nature of the World Wide Web makes it a challenge to find information that is both relevant and recent. Intelligent agents can complement the power of search engines to meet this challenge. We present a Web tool called MySpiders, which implements an evolutionary algorithm managing a population of adaptive crawlers who browse the Web autonomously. Each agent acts as an intelligent client on behalf of the user, driven by a user query and by textual and linkage clues in the crawled pages. Agents autonomously decide which links to follow, which clues to internalize, when to spawn offspring to focus the search near a relevant source, and when to starve. The tool is available to the public as a threaded Java applet. We discuss the development and deployment of such a system.

Details DOI

AAAI Conference 2020 Conference Paper

Scalable and Generalizable Social Bot Detection through Data Selection

  • Kai-Cheng Yang
  • Onur Varol
  • Pik-Mai Hui
  • Filippo Menczer

Efficient and reliable social bot classification is crucial for detecting information manipulation on social media. Despite rapid development, state-of-the-art bot detection models still face generalization and scalability challenges, which greatly limit their applications. In this paper we propose a framework that uses minimal account metadata, enabling efficient analysis that scales up to handle the full stream of public tweets of Twitter in real time. To ensure model accuracy, we build a rich collection of labeled datasets for training and validation. We deploy a strict validation system so that model performance on unseen datasets is also optimized, in addition to traditional cross-validation. We find that strategically selecting a subset of training data yields better model accuracy and generalization than exhaustively training on all available data. Thanks to the simplicity of the proposed model, its logic can be interpreted to provide insights into social bot characteristics.

PDF Details

AAMAS Conference 2019 Conference Paper

The DARPA SocialSim Challenge: Massive Multi-Agent Simulations of the Github Ecosystem

  • James Blythe
  • Emilio Ferrara
  • Di Huang
  • Kristina Lerman
  • Goran Muric
  • Anna Sapienza
  • Alexey Tregubov
  • Diogo Pacheco

We model the evolution of GitHub, a large collaborative softwaredevelopment ecosystem, using massive multi-agent simulations as a part of DARPA’s SocialSim program. Our best performing models and our agent-based simulation framework are described here. Six different agent models were tested based on a variety of machine learning and statistical methods. The most successful models are based on sampling from a stationary probability distribution of actions and repositories for each agent.

PDF
v2026.09.13