New York: An India-origin researcher has put together a system that would help analyse whether your tweet is credible or not, a report said.
 
A team led by Georgia Tech Ph.D. candidate Tanushree Mitra scanned 66 million tweets linked to nearly 1,400 real-world events to build a language model that identified words and phrases that lead to strong or weak perceived levels of credibility on Twitter.
 
“There have been many studies about social media credibility in recent years but very little is known about what types of words or phrases create credibility perceptions during rapidly unfolding events,” Mitra said in a statement.
 
The team looked at tweets surrounding events in 2014 and 2015, including the emergence of Ebola in West Africa, the Charlie Hebdo attack in Paris and the death of Eric Garner in New York City.
 
The team asked people to judge the posts on their credibility — from “certainly accurate” to “certainly inaccurate” — and then fed the words into a model that split them into 15 different linguistic categories.
 
The Georgia Tech computer then examined the words to judge if the tweets were credible or not. 
 
 
It matched the humans’ opinions about 68 per cent of the time. That’s significantly higher than the random baseline of 25 per cent, the research said.
 
“Tweets with booster words, such as ‘undeniable,’ and positive emotion terms, such as ‘eager’ and ‘terrific,’ were viewed as highly credible,” Mitra said.
 
“Words indicating positive sentiment but mocking the impracticality of the event, such as ‘ha,’ ‘grins’ or ‘joking,’ were seen as less credible. So were hedge words, including ‘certain level’ and ‘suspects,'” she added.
 
Higher numbers of retweets also correlated with lower credibility scores. Replies and retweets with longer message lengths were thought to be more credible.
 
“It could be that longer message lengths provide more information or reasoning, so they’re viewed as more trustworthy,” she said.
 
“On the other hand, a higher number of retweets, which was scored lower on credibility, might represent an attempt to elicit collective reasoning during times of crisis or uncertainty,” she added.
 
The system, when deployed, could eventually become an app that displays the perceived trustworthiness of an event as it unfolds on social media.