Taking the pulse of US college campuses with location-based anonymous mobile apps

Yanqiu Wu; Tehila Minkus; Keithw Ross

doi:10.1145/3078843

Taking the pulse of US college campuses with location-based anonymous mobile apps

Yanqiu Wu, Tehila Minkus, Keithw Ross

Research output: Contribution to journal › Article › peer-review

Abstract

We deploy GPS hacking in conjunction with location-based mobile apps to passively survey users in targeted geographical regions. Specifically, we investigate surveying students at different college campuses with Yik Yak, an anonymous mobile app that is popular on US college campuses. In addition to being campus centric, Yik Yak's anonymity allows students to express themselves candidly without self-censorship. We collect nearly 1.6 million Yik Yak messages ("yaks") from a diverse set of 45 college campuses in the United States.We use natural language processing to determine the sentiment (positive, negative, or neutral) of all of the yaks. We employ supervised machine learning to predict the gender of the authors of the yaks and then analyze how sentiment differs among the two genders on college campuses.We also use supervised machine learning to classify all the yaks into nine topics and then investigate which topics are most popular throughout the US and how topic popularity varies on the different campuses. The results in this article provide significant insight into how campus culture and student's thinking varies among US colleges and universities.

Original language	English (US)
Article number	3078843
Journal	ACM Transactions on Intelligent Systems and Technology
Volume	9
Issue number	1
DOIs	https://doi.org/10.1145/3078843
State	Published - Sep 2017

Keywords

Data mining
Social networks

ASJC Scopus subject areas

Theoretical Computer Science
Artificial Intelligence

Access to Document

10.1145/3078843

Cite this

@article{ae9a3ad1ea1d4493b2a520ec67aeb107,

title = "Taking the pulse of US college campuses with location-based anonymous mobile apps",

abstract = "We deploy GPS hacking in conjunction with location-based mobile apps to passively survey users in targeted geographical regions. Specifically, we investigate surveying students at different college campuses with Yik Yak, an anonymous mobile app that is popular on US college campuses. In addition to being campus centric, Yik Yak's anonymity allows students to express themselves candidly without self-censorship. We collect nearly 1.6 million Yik Yak messages ({"}yaks{"}) from a diverse set of 45 college campuses in the United States.We use natural language processing to determine the sentiment (positive, negative, or neutral) of all of the yaks. We employ supervised machine learning to predict the gender of the authors of the yaks and then analyze how sentiment differs among the two genders on college campuses.We also use supervised machine learning to classify all the yaks into nine topics and then investigate which topics are most popular throughout the US and how topic popularity varies on the different campuses. The results in this article provide significant insight into how campus culture and student's thinking varies among US colleges and universities.",

keywords = "Data mining, Social networks",

author = "Yanqiu Wu and Tehila Minkus and Keithw Ross",

note = "Funding Information: This work was supported in part by the NSF grant CNS-1318659. Authors{\textquoteright} addresses: Y. Wu, 200 Greene St, Jersey City, NJ 07311; email: [email protected]; T. Minkus, 10.054, 10th floor, 2 Metrotech, Brooklyn, NY 11201; email: [email protected]; K. W. Ross, 1555 Century Avenue, Shanghai, China; email: [email protected]. 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]. 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 2157-6904/2017/09-ART12 $15.00 http://dx.doi.org/10.1145/3078843 Publisher Copyright: {\textcopyright} 2017 ACM.",

year = "2017",

month = sep,

doi = "10.1145/3078843",

language = "English (US)",

volume = "9",

journal = "ACM Transactions on Intelligent Systems and Technology",

issn = "2157-6904",

publisher = "Association for Computing Machinery (ACM)",

number = "1",

}

TY - JOUR

T1 - Taking the pulse of US college campuses with location-based anonymous mobile apps

AU - Wu, Yanqiu

AU - Minkus, Tehila

AU - Ross, Keithw

N1 - Funding Information: This work was supported in part by the NSF grant CNS-1318659. Authors’ addresses: Y. Wu, 200 Greene St, Jersey City, NJ 07311; email: [email protected]; T. Minkus, 10.054, 10th floor, 2 Metrotech, Brooklyn, NY 11201; email: [email protected]; K. W. Ross, 1555 Century Avenue, Shanghai, China; email: [email protected]. 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]. 2017 Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 2157-6904/2017/09-ART12 $15.00 http://dx.doi.org/10.1145/3078843 Publisher Copyright: © 2017 ACM.

PY - 2017/9

Y1 - 2017/9

N2 - We deploy GPS hacking in conjunction with location-based mobile apps to passively survey users in targeted geographical regions. Specifically, we investigate surveying students at different college campuses with Yik Yak, an anonymous mobile app that is popular on US college campuses. In addition to being campus centric, Yik Yak's anonymity allows students to express themselves candidly without self-censorship. We collect nearly 1.6 million Yik Yak messages ("yaks") from a diverse set of 45 college campuses in the United States.We use natural language processing to determine the sentiment (positive, negative, or neutral) of all of the yaks. We employ supervised machine learning to predict the gender of the authors of the yaks and then analyze how sentiment differs among the two genders on college campuses.We also use supervised machine learning to classify all the yaks into nine topics and then investigate which topics are most popular throughout the US and how topic popularity varies on the different campuses. The results in this article provide significant insight into how campus culture and student's thinking varies among US colleges and universities.

AB - We deploy GPS hacking in conjunction with location-based mobile apps to passively survey users in targeted geographical regions. Specifically, we investigate surveying students at different college campuses with Yik Yak, an anonymous mobile app that is popular on US college campuses. In addition to being campus centric, Yik Yak's anonymity allows students to express themselves candidly without self-censorship. We collect nearly 1.6 million Yik Yak messages ("yaks") from a diverse set of 45 college campuses in the United States.We use natural language processing to determine the sentiment (positive, negative, or neutral) of all of the yaks. We employ supervised machine learning to predict the gender of the authors of the yaks and then analyze how sentiment differs among the two genders on college campuses.We also use supervised machine learning to classify all the yaks into nine topics and then investigate which topics are most popular throughout the US and how topic popularity varies on the different campuses. The results in this article provide significant insight into how campus culture and student's thinking varies among US colleges and universities.

KW - Data mining

KW - Social networks

UR - http://www.scopus.com/inward/record.url?scp=85029784828&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85029784828&partnerID=8YFLogxK

U2 - 10.1145/3078843

DO - 10.1145/3078843

M3 - Article

AN - SCOPUS:85029784828

SN - 2157-6904

VL - 9

JO - ACM Transactions on Intelligent Systems and Technology

JF - ACM Transactions on Intelligent Systems and Technology

IS - 1

M1 - 3078843

ER -

Taking the pulse of US college campuses with location-based anonymous mobile apps

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this