Analyzing crowdsourced assessment of user traits through Twitter posts

Citation:
Flekova, Lucie, Daniel Preoţiuc-Pietro, Jordan Carpenter, Salvatore Giorgi, and Lyle Ungar. Analyzing crowdsourced assessment of user traits through Twitter posts In Work-in-Progress. HCOMP, 2015.

Abstract:

Social media allows any user to express themselves to the public through posting content. Using a crowdsourcing experiment, we aim to quantify and analyze which human attributes lead to better perceptions of the true identity of others. Using tweet content from a set of users with known age and gender information, we ask workers to rate their perception of these traits and we analyze those results in relation to the crowdsourcing workers’ age and gender. Results show that female workers are both more confident and more accurate at reporting gender, and workers in their thirties were most accurate but least confident for rating age. Our study is a first step in identifying the worker traits which contribute to a better understanding of others through their posted text content. Our findings help to identify the types of workers best suited for certain tasks.

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