User Anonymity on Twitter

Sai Teja Peddinti, Keith W. Ross, Justin Cappos

    Research output: Contribution to journalArticlepeer-review


    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
    Issue number3
    StatePublished - 2017


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

    ASJC Scopus subject areas

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


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