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Creators/Authors contains: "Feldman, Anna"

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  1. ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available August 1, 2025
  2. ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available August 1, 2025
  3. ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available June 21, 2025
  4. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available June 22, 2025
  5. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available June 21, 2025
  6. ; ; ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available June 21, 2025
  7. ; ; ; ; ; ; ; ; (, Association for Computational Linguistics)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; (, Water Research)
    Full Text Available 
  9. ; ; (, Proceedings of the 10th Hellenic Conference on Artificial Intelligence)
    This study provides preliminary insights into the linguistic features that contribute to Internet censorship in mainland China. We collected a corpus of 344 censored and uncensored microblog posts thatwere published onSinaWeibo and built a Naive Bayes classifier based on the linguistic, topic-independent, features. The classifier achieves a 79.34%accuracy in predicting whether a blog post would be censored on Sina Weibo. 
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    Accepted Manuscript Full Text Available
  10. ; ; ; (, Proceedings of the 13th Pre-ICIS Workshop on Information Security and Privacy)
    If it remains debatable whether the Internet has surpassed print media in making information accessible to the public, then it must nevertheless be conceded that the Internet makes the manipulation and censorship of information easier than had been on the printed page. In coming years and in an increasing number of countries, everyday producers and consumers of online information will likely have to cultivate a sense of censorship. It behooves the online community to learn how to detect and evade interference by governments, regimes, corporations, con-artists, and vandals. The contribution of this research is to describe a method and platform to study Internet censorship detection and evasion. This paper presents the concepts, initial theories, and future work. 
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    Accepted Manuscript Full Text Available