Wednesday September 18, 2019
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#AfterSeptember11: 50k people share stories of racism in US

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By NewsGram Staff Writer

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credit: www.newsx.com

New York: Jessica Talwar, an Indian American woman, has created #AfterSeptember11 on micro-blogging site Twitter to highlight the issues of hatred and racial attacks meted out to various groups from India and other places in the US.

The hashtag reminds people of the situation that has developed, for worse, for various minority communities, religious and regional, where they face daily hatred and at times even lose their lives, just because they appear non-White.

The hashtag was created by Jessica Talwar, a 19-year-old political science student from Loyola University in Chicago who tweets as @jesstalwar, the Los Angeles Times reported on Friday.

People came in huge numbers to talk about their stories after the hashtag #AfterSeptember11 began trending since September 10 night — the day it was created. Over 50,000 victims shared their stories using it.

The victims said they were targeted for being Muslim – or often, just for having brown skin.

Many of the victims were children during the attacks on the Trade Towers but their tweets reflect the impact of the racial abuse on their young lives.

One said her father shaved his face and stopped wearing a turban after he was assaulted at work.

“America needs to recognize that the trauma and repercussions of these attacks were not confined to the day of September 11, 2001 itself,” Talwar wrote in an email to Los Angeles Times.

“Desis, Arabs, and Muslims have felt the impact of this day for 14 years,” she said.

Indian American poet Hari Kondabolu echoed Talwar’s views and wrote “his mother put the US flag on their house because she feared that people would throw rocks through the window”.

Just a few days ago, on September 8, an elderly Sikh-American man Inderjit Singh Mukker, was attacked in Chicago and was dubbed a “terrorist” and “bin Laden” by the attacker.

Soon after its creation, the detractors used the hashtag to flood hate messages. They used racial slurs and threatened to kill Muslims.

“It was as if there was some rigid dichotomy between American society and the South Asian, Muslim, and Arab communities,” Talwar was quoted as saying.

“This movement was not intended to belittle the tragic events of September 11 itself,” she said.

(With inputs from IANS)

Next Story

Researchers Develop New Algorithm to Identify Cyber-bullies on Twitter

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn

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FILE - A man reads tweets on his phone in front of a displayed Twitter logo. VOA

Researchers have developed machine learning algorithms which can identify bullies and aggressors on Twitter with 90 per cent accuracy.

For the study published in the journal Transactions on the Web, the research team analysed the behavioural patterns exhibited by abusive Twitter users and their differences from other users.

“We built crawlers — programs that collect data from Twitter via variety of mechanisms,” said study researcher Jeremy Blackburn from Binghamton University in the US.

“We gathered tweets of Twitter users, their profiles, as well as (social) network-related things, like who they follow and who follows them,” Blackburn said.

The researchers then performed natural language processing and sentiment analysis on the tweets themselves, as well as a variety of social network analyses on the connections between users.

twitter, white swan, suicide, awareness
Twitter is a social media app that encourages short tweets and brief conversations. Pixabay

They developed algorithms to automatically classify two specific types of offensive online behaviour, i.e. cyber-bullying and cyber-aggression.

The algorithms were able to identify abusive users — who engage in harassing behaviour like those who send death threats or make racist remarks — on Twitter with 90 per cent accuracy.

Also Read: Facebook Announces Some New Features to its Video Capabilities

“In a nutshell, the algorithms ‘learn’ how to tell the difference between bullies and typical users by weighing certain features as they are shown more examples,” said Blackburn.

“Our research indicates that machine learning can be used to automatically detect users that are cyber-bullies, and thus could help Twitter and other social media platforms remove problematic users,” Blackburn added. (IANS)