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High Percentage of Robot-generated Fake Tweets likely to Influence Public Opinion before upcoming US Presidential Elections

Researchers have found that robots, rather than people have produced 3.8 million tweets

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US Presidential Candidates Donald Trump and Hillary Clinton. Wikimedia

Washington, November 5, 2016:  Just before US Election polls, a high percentage of the political discussion was created by software robots or social bots on popular social media site Twitter, that may be influencing public opinion, warned a new study.

According to PTI, researchers from the University of Southern California’s (USC) Viterbi School of Engineering in the US worry that these robot-generated tweets are likely to distort political online discussion as well as there is a possibility that it might impact election outcomes.

[bctt tweet=”Researchers found that Republican candidate Donald Trump’s robot-produced tweets were almost uniformly positive, boosting the candidate’s popularity. ” username=””]

“Software robots masquerading as humans are influencing the political discourse on social media as never before and could threaten the very integrity of the 2016 US presidential election,” said research leader at the USC, Emilio Ferrara.

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Ferrara and Alessandro Bessi, who are visiting research assistants at USC have analysed 20 million election-related tweets created between September 16 and October 21, by leveraging state-of-the art bot detection algorithms, mentioned PTI report.

While delving deep, they found that robots, rather than people have produced 3.8 million tweets, or 19 percent. Social bots also accounted for 400,000 of the 2.8 million individual users, or nearly 15 percent of the population under study.

After analysing, researchers have found that Republican Presidential candidate Donald Trump’s robot-produced tweets were almost uniformly positive, that is boosting the candidate’s popularity.

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On contrary to that, only half of his Democratic rival Hillary Clinton’s bot tweets were positive, with the other half criticising the nominee, mentioned PTI.

It is often impossible to determine who creates these tweets, due to the social bots’ sophistication.

According to the report, political parties, local, national and foreign governments and even single individuals with adequate resources could obtain the operational capabilities and technical tools to deploy armies of social bots and affect the directions of online political conversation, said the researchers.

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The “master puppeteers” behind influence bots, often create fake Twitter and Facebook profiles, they said, mentioned PTI.

“They do so by stealing online pictures, giving them fictitious names, and cloning biographical information from existing accounts,” they added.

“These bots have become so sophisticated that they can tweet, retweet, share content, comment on posts, ‘like’ candidates, grow their social influence by following legit human accounts and even engage in human-like conversations,” researchers further added.

– prepared by NewsGram with inputs from PTI.

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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)