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Identifying spam in the iOS app store

Published: 16 April 2012 Publication History

Abstract

Popular apps on the Apple iOS App Store can generate millions of dollars in profit and collect valuable personal user information. Fraudulent reviews could deceive users into downloading potentially harmful spam apps or unfairly ignoring apps that are victims of review spam. Thus, automatically identifying spam in the App Store is an important problem. This paper aims to introduce and characterize novel datasets acquired through crawling the iOS App Store, compare a baseline Decision Tree model with a novel Latent Class graphical model for classification of app spam, and analyze preliminary results for clustering reviews.

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Cited By

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  • (2024)Arabic App Reviews: Analysis and ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/370898724:2(1-28)Online publication date: 23-Dec-2024
  • (2023)Thematic analysis of google play reviews of lifestyle appsHuman Technology10.14254/1795-6889.2023.19-1.619:1(82-102)Online publication date: 22-May-2023
  • (2023)Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviewsSoft Computing10.1007/s00500-023-08507-z27:16(11357-11378)Online publication date: 3-Jun-2023
  • Show More Cited By

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cover image ACM Other conferences
WebQuality '12: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
April 2012
71 pages
ISBN:9781450312370
DOI:10.1145/2184305
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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Author Tags

  1. fraud detection
  2. mobile apps
  3. opinion spam
  4. review spam

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Cited By

View all
  • (2024)Arabic App Reviews: Analysis and ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/370898724:2(1-28)Online publication date: 23-Dec-2024
  • (2023)Thematic analysis of google play reviews of lifestyle appsHuman Technology10.14254/1795-6889.2023.19-1.619:1(82-102)Online publication date: 22-May-2023
  • (2023)Unmasking deception: a CNN and adaptive PSO approach to detecting fake online reviewsSoft Computing10.1007/s00500-023-08507-z27:16(11357-11378)Online publication date: 3-Jun-2023
  • (2023)A Proposed Keyword-Based Feature Extraction Approach for Labeling and Classifying Egyptian Mobile Apps Arabic Slang User Requirements ReviewsBig Data Technologies and Applications10.1007/978-3-031-33614-0_2(24-37)Online publication date: 26-May-2023
  • (2021)The U in Crypto Stands for Usable: An Empirical Study of User Experience with Mobile Cryptocurrency WalletsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445407(1-14)Online publication date: 6-May-2021
  • (2021)Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learningExpert Systems with Applications10.1016/j.eswa.2021.115111181(115111)Online publication date: Nov-2021
  • (2021)Combating Against Potentially Harmful Mobile AppsProceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021)10.1007/978-3-030-76346-6_15(154-173)Online publication date: 29-May-2021
  • (2020)A Sentiment-Statistical Approach for Identifying Problematic Mobile App Updates Based on User ReviewsInformation10.3390/info1103015211:3(152)Online publication date: 12-Mar-2020
  • (2020)Hall-of-AppsProceedings of the 17th International Conference on Mining Software Repositories10.1145/3379597.3387497(568-572)Online publication date: 29-Jun-2020
  • (2020)A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play StoreIEEE Transactions on Mobile Computing10.1109/TMC.2020.3007260(1-1)Online publication date: 2020
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