Writer Profiling Without the Writer’s Text

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


Social network users may wish to preserve their anonymity online by masking their identity and not using language associated with any particular demographics or personality. However, they have no control over the language in incoming communications. We show that linguistic cues in public comments directed at a user are sufficient for an accurate inference of that user’s gender, age, religion, diet, and even personality traits. Moreover, we show that directed communication is even more predictive of a user’s profile than the user’s own language. We then conduct a nuanced analysis of what types of social relationships are most predictive of users’ attributes, and propose new strategies on how individuals can modulate their online social relationships and incoming communications to preserve their anonymity.


Incoming Communications Top Departments Demographic Inference Linguistic Inquiry And Word Count (LIWC) Demographic Attributes 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank the anonymous reviewers, SocInfo organizers, the Stanford Data Science Initiative, and Twitter and Gnip for providing access to part of data used in this study. This work was supported by the National Science Foundation through awards IIS-1159679 and IIS-1526745.


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Authors and Affiliations

  1. 1.Stanford UniversityStanfordUSA

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