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

Yanqiu Wu, Tehila Minkus, Keith Ross

    Research output: Contribution to journalArticle

    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 languageEnglish (US)
    Article number3078843
    JournalACM Transactions on Intelligent Systems and Technology
    Volume9
    Issue number1
    DOIs
    StatePublished - Sep 1 2017

    Fingerprint

    Supervised Learning
    Application programs
    Machine Learning
    Vary
    Students
    Learning systems
    Anonymity
    Natural Language
    Geographical regions
    Express
    Classify
    Surveying
    Predict
    Global positioning system
    Processing
    Gender
    Culture
    Universities

    Keywords

    • Data mining
    • Social networks

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Artificial Intelligence

    Cite this

    Taking the pulse of US college campuses with location-based anonymous mobile apps. / Wu, Yanqiu; Minkus, Tehila; Ross, Keith.

    In: ACM Transactions on Intelligent Systems and Technology, Vol. 9, No. 1, 3078843, 01.09.2017.

    Research output: Contribution to journalArticle

    @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 Keith Ross",
    year = "2017",
    month = "9",
    day = "1",
    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, Keith

    PY - 2017/9/1

    Y1 - 2017/9/1

    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

    VL - 9

    JO - ACM Transactions on Intelligent Systems and Technology

    JF - ACM Transactions on Intelligent Systems and Technology

    SN - 2157-6904

    IS - 1

    M1 - 3078843

    ER -