Protective measures implemented by online harassment victims


Pavel Prikhodko, Ph.D. Machine Learning

Online harassment is a big problem in the United States nowadays, and problems of this nature can be difficult to control. According to the statistics on the most common types of online harassment experienced by internet users in the U.S., 23 percent of female internet users had experienced harassment with offensive names as of January 2017. About 21 percent of American female internet users suffered from purposeful embarrassment, while 8 percent experienced physical threats and sexual harassment. Additionally, 6 percent suffered from stalking and 7 percent from sustained harassment.

As a result, overall around 37 percent of female U.S. internet users said that they experienced online harassment. In comparison, the overall percentage of male internet users in the country who experienced online harassment was even higher — 44 percent of men said they had been harassed online. If we look at the most common types of harassment that men experienced, 30 percent were called offensive names, 24 percent experienced purposeful embarrassment, 12 percent experienced physical threats, 8 percent experienced stalking and sustained harassment and 4 percent experienced sexual harassment online.


Many victims of online harassment take steps to protect themselves. As of July 2016, 43 percent of the people surveyed changed their contact information. About 36 percent stated that they changed their email address or telephone number. Approximately 21 percent of online harassment victims created a new social media profile under a different name.

Many people (26 percent of the respondents) asked a friend or family member for help, while 11 percent got a restraining order or protection order. About 27 percent of people reported or flagged content that was posted about someone on a website without person’s permission. About 26 percent disconnected from online networks or devices, and 21 percent stopped using social media.

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About Pavel Prikhodko


Pavel Prikhodko, Ph.D. Machine Learning

Pavel has worked for many years as a researcher and developer on a wide range of applications (varying from mechanics and manufacturing to social data, finance and advertising), building predictive systems and trying to find stories that data can tell.

In his free time, he enjoys being with his family.

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