User Anonymity on Twitter

Sai Teja Peddinti, Keith Ross, Justin Cappos

    Research output: Contribution to journalArticle

    Abstract

    A novel machine-based classifier system leverages Twitter user anonymity patterns and their correlation to content sensitivity to automatically identify accounts that tweet sensitive content. Anonymity's role in society and the nuances and complexity of content sensitivity are confirmed.

    Original languageEnglish (US)
    Article number7945182
    Pages (from-to)84-87
    Number of pages4
    JournalIEEE Security and Privacy
    Volume15
    Issue number3
    DOIs
    StatePublished - 2017

    Fingerprint

    twitter
    anonymity
    Classifiers

    Keywords

    • anonymity
    • anonymous
    • content sensitivity
    • privacy
    • pseudonymous
    • real-name policy
    • social media
    • social networks
    • Twitter

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Electrical and Electronic Engineering
    • Law

    Cite this

    Peddinti, S. T., Ross, K., & Cappos, J. (2017). User Anonymity on Twitter. IEEE Security and Privacy, 15(3), 84-87. [7945182]. https://doi.org/10.1109/MSP.2017.74

    User Anonymity on Twitter. / Peddinti, Sai Teja; Ross, Keith; Cappos, Justin.

    In: IEEE Security and Privacy, Vol. 15, No. 3, 7945182, 2017, p. 84-87.

    Research output: Contribution to journalArticle

    Peddinti, ST, Ross, K & Cappos, J 2017, 'User Anonymity on Twitter', IEEE Security and Privacy, vol. 15, no. 3, 7945182, pp. 84-87. https://doi.org/10.1109/MSP.2017.74
    Peddinti ST, Ross K, Cappos J. User Anonymity on Twitter. IEEE Security and Privacy. 2017;15(3):84-87. 7945182. https://doi.org/10.1109/MSP.2017.74
    Peddinti, Sai Teja ; Ross, Keith ; Cappos, Justin. / User Anonymity on Twitter. In: IEEE Security and Privacy. 2017 ; Vol. 15, No. 3. pp. 84-87.
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