What’s it like to get trolled all day long?

Being an outspoken woman on Twitter is hard. To find out how hard, we gathered a day’s worth of tweets sent to four prominent Indian women — Barkha Dutt, Rana Ayyub, Tavleen Singh, and Madhu Kishwar. We then filtered those tweets for profane or abusive words, categorized them, and sped them up by a thousand times. Now you, too, can experience the vitriol unleashed upon these women over the course of a day. Warning: The following contains coarse language and may not be suitable for children.

Barkha Dutt @BDUTT Barkha Rani Jamke Barasti Hai. Emmy Nominated Reporter. Author.Contributing Columnist @WashingtonPost Contributing Ed @TheWeekLive . Argumentative!Yaaron Ka Yaar : : Pause Count of categories (some tweets have more than one), per half hour

Twitter users who are unable or unwilling to engage with the content of Dutt’s reporting often resort, as a child might, to name calling. Dutt frequently endures sexist and racist slurs and violent threats. Trolls also accuse her of being a shill for political causes, typically left-wing ones. As you can see, some of the terms we’ve highlighted are not always used abusively. Sometimes they are even used supportively. Other terms are not abusive in and of themselves — Pakistani, for example, is just a nationality — but are used so frequently in abusive ways that we’ve included them here.

Rana Ayyub @RanaAyyub Journalist, writer, film buff. Rooted in Indian politics and social justice. Free, fair, fearless. Author of Gujarat Files-anatomy of a cover up. : : Pause Count of categories (some tweets have more than one), per half hour

For Ayyub, a Muslim, hateful tweets are often coloured by Islamophobia. Trolls like to pretend Ayyub is an agent of Pakistan’s Inter-Services Intelligence (ISI) and to demand she “return” to Pakistan. Any criticism of India or its government, however reasonable or constructive, may be deemed traitorous. The trolls are about as vicious to Ayyub as they are to Dutt — during the week beginning Monday, April 10 and ending Sunday, April 16, we tagged 2,582 abusive tweets mentioning Ayyub and 3,020 mentioning Dutt.

Tavleen Singh @tavleen_singh columnist, author : : Pause Count of categories (some tweets have more than one), per half hour

Trolls spare no one. Even Singh, who has been generally supportive of the Modi government and is perceived as being more “right-wing” than Dutt or Ayyub, has been accused of being a Pakistani propagandist and an anti-national. Yet even a cursory glance through her tweets reveals that the harassment directed at Singh pales in comparison to that received by her more “left-wing” counterparts. Singh received 195 tweets we tagged as being potentially abusive during the entire week that began Monday, April 10 and ended Sunday, April 16 — that’s fewer than Dutt or Ayyub received on any given day throughout the week. Moreover, the hateful tweets sent in reply to Singh are often directed at people other than Singh herself.

Madhu Kishwar @madhukishwar Factarian, Averse to all Isms. Maulana Azad National Professor, ICSSR. Founder human rights organisation MANUSHI : : Pause Count of categories (some tweets have more than one), per half hour

Like Singh, Kishwar has been generally supportive of the Modi government; and, like Singh, the hateful tweets she receives are generally directed at people other than her. Rather, trolls try to dissuade her from even engaging people with whom they disagree. Unlike the other women here, Kishwar occasionally resorts to ad hominem attacks herself, calling her opponents names like libtard and Aaptard.

A note on our methodology These tweets were collected on the evening of April 17, 2017, using Twitter’s free REST API. We collected as many tweet mentions as possible for all four women, and then selected the day with the highest minimum tagged tweets among the four. That day was April 14, when Dutt was mentioned in 520 tagged tweets, Ayyub was mentioned in 433, Kiswhar was mentioned in 117, and Singh was mentioned in 56 tagged tweets. We chose the terms used to tag the tweets by reading through hundreds of tweets and noting which words seemed to be most often used in abusive contexts. This is not an exhaustive list — some abusive tweets were missed, while other non-abusive ones were included.