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Twitter Still Remains a Hotbed For Accounts Spreading Fake News: Study

Sixty-five percent of fake and conspiracy news links during the election period went to just the 10 largest sites, a statistic unchanged six months later

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Twitter India celebrates rising women achievers. Pixabay

Twitter still remains a hotbed for accounts spreading fake news, suggests a new study that looked into how fake and conspiracy news flourished on the microblogging site both before and after the 2016 US presidential election.

More than 80 per cent of the Twitter accounts linked to spread of disinformation during the 2016 election are still active, said the study by the Knight Foundation on Thursday.

These accounts continue to publish more than a million tweets in a typical day, the study said.

Using tools and mapping methods from Graphika, a social media intelligence firm, the researchers studied more than 10 million tweets from 700,000 Twitter accounts that linked to more than 600 fake and conspiracy news outlets.

Twitter, along with other social media platforms including Facebook came under intense scrutiny of policymakers in the US for their failure to stop the spread of misinformation on their platforms during the 2016 election.

The microblogging site since then has stepped up its efforts to curb the spread of divisive messages and fake news on its platform.

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Twitter on a smartphone device. Pixabay

To further protect the integrity of elections, Twitter earlier this week announced that it will now delete fake accounts engaged in a variety of emergent, malicious behaviours.

As platform manipulation tactics continue to evolve, the micro-blogging platform said it is expanding rules to better reflect how it identifies fake accounts and what types of inauthentic activity violate its guidelines before the US mid-term elections in November.

As part of the new rules, accounts that deliberately mimic or are intended to replace accounts were previously suspended for violating rules may be identified as fake accounts, Twitter said.

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The Knight Foundation study found more than 6.6 million tweets linking to fake and conspiracy news publishers in the month before the 2016 election.

Yet disinformation continues to be a substantial problem postelection, with 4.0 million tweets linking to fake and conspiracy news publishers found in a 30-day period from mid-March to mid-April 2017, the study said.

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Twitter on a smartphone device. VOA

Sixty-five percent of fake and conspiracy news links during the election period went to just the 10 largest sites, a statistic unchanged six months later.

“Machine Learning models estimate that 33 percent of the 100 most-followed accounts in our postelection map — and 63 percent of a random sample of all accounts — are “bots,” or automated accounts,” the study said.

“Because roughly 15 per cent of accounts in the postelection map have since been suspended, the true proportion of automated accounts may have exceeded 70 per cent,” it added. (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.

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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.

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“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)