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FDVT: Data Valuation Tool for Facebook Users

Published: 02 May 2017 Publication History

Abstract

The OECD, the European Union and other public and private initiatives are claiming for the necessity of tools that create awareness among Internet users about the monetary value associated to the commercial exploitation of their online personal information. This paper presents the first tool addressing this challenge, the Facebook Data Valuation Tool (FDVT). The FDVT provides Facebook users with a personalized and real-time estimation of the revenue they generate for Facebook. Relying on the FDVT, we are able to shed light into several relevant HCI research questions that require a data valuation tool in place. The obtained results reveal that (i) there exists a deep lack of awareness among Internet users regarding the monetary value of personal information, (ii) data valuation tools such as the FDVT are useful means to reduce such knowledge gap, (iii) 1/3 of the users testing the FDVT show a substantial engagement with the tool.

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cover image ACM Conferences
CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
May 2017
7138 pages
ISBN:9781450346559
DOI:10.1145/3025453
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 the author(s) 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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Published: 02 May 2017

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Author Tags

  1. FDVT
  2. data valuation
  3. facebook
  4. personal data
  5. privacy

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CHI '17 Paper Acceptance Rate 600 of 2,400 submissions, 25%;
Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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  • (2024)Online advertisement in a pink-colored marketEPJ Data Science10.1140/epjds/s13688-024-00473-213:1Online publication date: 8-May-2024
  • (2023)Representation, Self-Determination, and Refusal: Queer People’s Experiences with Targeted AdvertisingProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594110(1711-1722)Online publication date: 12-Jun-2023
  • (2023)The Dimensions of Data Labor: A Road Map for Researchers, Activists, and Policymakers to Empower Data ProducersProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594070(1151-1161)Online publication date: 12-Jun-2023
  • (2023)More Data Types More Problems: A Temporal Analysis of Complexity, Stability, and Sensitivity in Privacy PoliciesProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594065(1088-1100)Online publication date: 12-Jun-2023
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  • (2021)Problematic Machine BehaviorProceedings of the ACM on Human-Computer Interaction10.1145/34491485:CSCW1(1-34)Online publication date: 22-Apr-2021
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