Utilizing natural honeypots for efficiently labeling astroturfer profiles

Jonathan Schler, Elisheva Bonchek-Dokow, Tomer Vainstein, Moshe Gotam, Mike Teplitsky

Research output: Contribution to journalConference articlepeer-review

Abstract

Astroturfing is the practice of using a fake online social me- dia (OSM) profile in order to influence public opinion, while giving the impression that the profile belongs to an authentic human user. In at- tempting to train a classifier for discriminating between authentic users and astroturfers, a labeled dataset must first be arranged. The labeling is generally done manually, by human judges, on a collection of profiles garnered from the social media network. However, the fact that any ran- domly collected set of profiles will statistically contain a small proportion of astroturfers, renders this process inefficient: a lot of time and effort is invested on manually labeling lots of data, while producing only a small set of astroturfer profiles. We present here a method for quickly and ef- ficiently collecting a data set for manual labeling, with a high percent of astroturfers.

Original languageEnglish
Pages (from-to)41-45
Number of pages5
JournalCEUR Workshop Proceedings
Volume2751
StatePublished - 2020
Externally publishedYes
Event22nd International Conference on Knowledge Engineering and Knowledge Management - Posters and Demonstrations Session, EKAW-PD 2020 - Virtual, Bozen-Bolzano, Italy
Duration: 16 Sep 202018 Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 CEUR-WS. All rights reserved.

Keywords

  • Astroturfing
  • Efficient Labeling
  • Facebook

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