Bot Detective

Bot-Detective

Bot Detective

Description

Bot Detective is a Web service that was developed in the context of the thesis of Maria Kouvela, in fulfillment of the requirements for the degree of Master of Data & Web Science, under the supervision of PhD Candidate Ilias Dimitriadis and Professor Athena Vakali. Bot Detective takes into account both the efficient detection of bot users and the interpretability of the results as well. Our main contributions are summarized as follows:

  1. We propose a novel explainable bot-detection approach, which, to the best of authors’ knowledge, is the first one to offer interpretable, responsible, and AI driven bot identification in Twitter. Bot Detective uses a Random Forest classifier and follows an explainable AI state of the art method to provide justifications of high granularity, since explanations are offered for all relevant features that have contributed in the Bot Detective’s scoring.
  2. We provide a publicly available bot detection Web service which integrates an explainable machine learning framework along with users feedback functionality under an effective crowdsourcing mechanism. This service covers the growing demand for bot detection services and it offers extended crowdsourcing functionalities and XAI capabilities which advance existing state of the art tools, such as the Botometer.
  3. We share a new labelled dataset, annotated by exploiting Twitter’s rules and existing tools, which we use to build the proposed service. The dataset consists of thousands of tweets collected using Twitter’s official API and labelled with the use of Botometer and by taking into account that many of the authors of the posting accounts were, at a later time, deactivated by Twitter.

Although several AI driven bot detection methods have been implemented, the justification of bot classification and characterization remains quite opaque and AI decisions lack in ethical responsibility. Most of these approaches operate with AI black-boxed algorithms and their efficiency is often questionable.

People

  • Ilias Dimitriadis
  • Maria Kouvela
  • Athena Vakali