  Objective: The goal of this study is to understand how people experience
sexism and sexual harassment in the workplace by discovering themes in 2,362
experiences posted on the Everyday Sexism Project's website everydaysexism.com.
Method: This study used both quantitative and qualitative methods. The
quantitative method was a computational framework to collect and analyze a
large number of workplace sexual harassment experiences. The qualitative method
was the analysis of the topics generated by a text mining method. Results:
Twenty-three topics were coded and then grouped into three overarching themes
from the sex discrimination and sexual harassment literature. The Sex
Discrimination theme included experiences in which women were treated
unfavorably due to their sex, such as being passed over for promotion, denied
opportunities, paid less than men, and ignored or talked over in meetings. The
Sex Discrimination and Gender harassment theme included stories about sex
discrimination and gender harassment, such as sexist hostility behaviors
ranging from insults and jokes invoking misogynistic stereotypes to bullying
behavior. The last theme, Unwanted Sexual Attention, contained stories
describing sexual comments and behaviors used to degrade women. Unwanted
touching was the highest weighted topic, indicating how common it was for
website users to endure being touched, hugged or kissed, groped, and grabbed.
Conclusions: This study illustrates how researchers can use automatic processes
to go beyond the limits of traditional research methods and investigate
naturally occurring large scale datasets on the internet to achieve a better
understanding of everyday workplace sexism experiences.
