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Predicting Drug Demand with Wikipedia Views: Evidence from Darknet Markets.

Authors:

Abeer El-Bahrawy,

Mark Graham, and

Joss WrightAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

April 2020

Pages 2669 - 2675

https://doi.org/10.1145/3366423.3380022

Published: 20 April 2020 Publication History

Abstract

Rapid changes in illicit drug demand, such as the Fentanyl epidemic, are a major public health issue. Policymakers currently rely on annual surveys to monitor public consumption, which are arguably too infrequent to detect rapid shifts in drug use. We present a novel method to predict drug use based on high-frequency sales data from darknet markets. We show that models based on historic trades alone cannot accurately predict drug demand. However, augmenting these models with data on Wikipedia page views for each drug greatly improves predictive accuracy, particularly for less popular drugs, suggesting such models may be particularly useful for detecting newly emerging substances. These results hold out-of-sample at high time frequency, across a range of drugs and countries. Therefore Wikipedia data may enable us to build a high-frequency measure of drug demand, which could help policymakers respond more quickly to future drug crises.

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https://doi.org/10.1109/SWC57546.2023.10449058
Ambika N. (2022)Minimum Prediction Error at an Early Stage in Darknet AnalysisDark Web Pattern Recognition and Crime Analysis Using Machine Intelligence10.4018/978-1-6684-3942-5.ch002(18-30)Online publication date: 13-May-2022
https://doi.org/10.4018/978-1-6684-3942-5.ch002
Rivero Albarran DArciniegas Aguirre SFernández Badillo M(2022)Un modelo para predecir la demanda en farmaciasRedmarka. Revista de Marketing Aplicado10.17979/redma.2022.26.1.900726:1(1-14)Online publication date: 30-Jun-2022
https://doi.org/10.17979/redma.2022.26.1.9007
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Index Terms

Predicting Drug Demand with Wikipedia Views: Evidence from Darknet Markets.

Index terms have been assigned to the content through auto-classification.

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Published In

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Wang XChen LWang JZheng D(2023)Machine Learning Approaches for Region-level Prescription Demand Forecasting2023 IEEE Smart World Congress (SWC)10.1109/SWC57546.2023.10449058(1-6)Online publication date: 28-Aug-2023
https://doi.org/10.1109/SWC57546.2023.10449058
Ambika N. (2022)Minimum Prediction Error at an Early Stage in Darknet AnalysisDark Web Pattern Recognition and Crime Analysis Using Machine Intelligence10.4018/978-1-6684-3942-5.ch002(18-30)Online publication date: 13-May-2022
https://doi.org/10.4018/978-1-6684-3942-5.ch002
Rivero Albarran DArciniegas Aguirre SFernández Badillo M(2022)Un modelo para predecir la demanda en farmaciasRedmarka. Revista de Marketing Aplicado10.17979/redma.2022.26.1.900726:1(1-14)Online publication date: 30-Jun-2022
https://doi.org/10.17979/redma.2022.26.1.9007
Bracci ABoehnke JElBahrawy APerra NTeytelboym ABaronchelli A(2022)Macroscopic properties of buyer–seller networks in online marketplacesPNAS Nexus10.1093/pnasnexus/pgac2011:4Online publication date: 6-Oct-2022
https://doi.org/10.1093/pnasnexus/pgac201
Covrig BMikelarena ERosca CGoanta CSpanakis GZarras A(2022)Upside Down: Exploring the Ecosystem of Dark Web Data MarketsICT Systems Security and Privacy Protection10.1007/978-3-031-06975-8_28(489-506)Online publication date: 3-Jun-2022
https://doi.org/10.1007/978-3-031-06975-8_28
Bracci ANadini MAliapoulios MMcCoy DGray ITeytelboym AGallo ABaronchelli A(2021)Dark Web Marketplaces and COVID-19: before the vaccineEPJ Data Science10.1140/epjds/s13688-021-00259-w10:1Online publication date: 21-Jan-2021
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Zheng PYuan SWu XWu Y(2021)Hidden Buyer Identification in Darknet Markets via Dirichlet Hawkes Process2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671406(581-589)Online publication date: 15-Dec-2021
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https://doi.org/10.2139/ssrn.3703865

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