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How Pro-Modi, Anti-Modi Twitter Bots went Berserk

The most active was @PhillyTdp, which posted on #GoBackModi 2,179 times as the hashtag took off staggering one tweet every 5.3 seconds for over three hours

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Twitter, tweets, India
The Twitter logo appears on a phone post on the floor of the New York Stock Exchange.. VOA

Two Twitter bots — one in support of and one in opposition to Prime Minister Narendra Modi — made a massive attempt to boost traffic on the platform in India in February as the world’s largest democracy prepared for the General Elections, a new report has found.

The US-based Atlantic Council’s Digital Forensic Research Lab (DFR Lab) revealed that the pro-Modi traffic was far more heavily manipulated than the anti-Modi traffic or any large-scale traffic flow the DFR Lab had analyzed.

The two hashtags were #GoBackModi and #TNwelcomesModi.

“The accounts were deployed on a massive scale on February 9-10 and boosted hashtags both in support of and in opposition to incumbent Prime Minister Narendra Modi, with small groups of accounts pushing out thousands of posts an hour,” said the DFR Lab report.

The accounts were domestic in origin and substance.

#TNwelcomesModi was mentioned over 777,000 times in two days. The hashtag referenced Modi’s visit to Tamil Nadu.

The DFR Lab analyzed the first 49,727 tweets in the flow to see whether the hashtag started to trend because of widespread interest or because it was pushed by a small group.

“Almost two-thirds of the posts that initiated #TNwelcomesModi and pushed it to trend came from just 50 accounts. This was an attempt at manipulation on an industrial scale, using a small number of hyper-tweeting bots to give the hashtag a massive boost,” the report explained.

Twitter, India, Smartphone
Twitter on a smartphone device. Pixabay

One such bot account, @priyamanaval6 tweeted around once every 17 seconds. This account, and the others amplifying the #TNwelcomesModi hashtag have been suspended.

On February 10, the hashtag #GoBackModi also trended.

This hashtag trended even faster, racking up 49,538 tweets in just over three hours in the early morning of February 10. It peaked at a lower rate, however, generating 447,000 posts on February 9-10.

Just like #TNwelcomesModi, #GoBackModi was heavily pushed by a small number of high-volume accounts that posted hundreds of times an hour.

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“Unlike #TNwelcomesModi, these accounts were still not suspended at the time of the report.

The most active was @PhillyTdp, which posted on #GoBackModi 2,179 times as the hashtag took off staggering one tweet every 5.3 seconds for over three hours.

The analysis used the Coefficient of Traffic Manipulation (CTM) method, which allows researchers to compare a given Twitter flow with known organic traffic, and traffic that was heavily gamed by small groups. (IANS)

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Are you an Avid Twitter User? Your Posts can Reveal How Lonely you are

If we are able to identify lonely individuals and intervene before the health conditions associated with the themes we found begin to unfold, we have a change

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Twitter, User, Posts
Loneliness can be a slow killer, as some of the medical problems associated with it can take decades to manifest. Pixabay

Researchers have found that users who tweet on loneliness are much more likely to write about mental well-being issues and things like struggles with relationships, substance use and insomnia on Twitter.

By applying linguistic analytic models to tweets, researchers were able to gain an insight into the topics and themes that could be associated with loneliness.

“Loneliness can be a slow killer, as some of the medical problems associated with it can take decades to manifest,” said the study’s lead author Sharath Chandra Guntuku, from University of Pennsylvania in the US.

“If we are able to identify lonely individuals and intervene before the health conditions associated with the themes we found begin to unfold, we have a change to help those much earlier in their lives. This could be very powerful and have long-lasting effects on public health,” Guntuku said.

Twitter, User, Posts
By applying linguistic analytic models to tweets, researchers were able to gain an insight into the topics and themes that could be associated with loneliness. Pixabay

By determining typical themes and linguistic markers posted to social media that are associated with people who are lonely, the team has uncovered some of the ingredients necessary to construct a ‘loneliness’ prediction system.

As part of the study, published in the journal BMJ, researchers analysed public accounts from users based in Pennsylvania and found that 6,202 accounts used words such as ‘lonely’ or ‘alone’ more than five times between 2012 and 2016.

Comparing the entire Twitter timelines of these users to a matched group who did not have such language included their posts, the researchers showed that ‘lonely’ users tweeted nearly twice as much and were much more likely to do so at night.

When the tweets were analysed via several different linguistic analytic models, the users who posted about loneliness had an extremely high association with anger, depression and anxiety, when compared to the ‘non-lonely’ group.

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Additionally, the lonely groups were significantly associated with tweeting about struggles with relationships (for example, using phrases like ‘want somebody’ or ‘no one to’) and substance use (‘smoke,’ ‘weed,’ and ‘drunk’)

“On Twitter, we found lonely users expressing a need for social support, and it appears that the use of expletives and the expression of anger is a sign of that being unfulfilled,” Guntuku said.

Users in the group that didn’t post about loneliness seemed to display some social connections, as they were found to be more likely to engage in conversations, especially by including others’ user names (using ‘@twitter_handle’) in their tweets.

In the future, the researchers hope to develop a better measure of the different dimensions of loneliness that online users are feeling and expressing. (IANS)