Are you or people around you happy or sad? Twitter has the answer!

The team from University of Iowa used two years of Twitter data to measure users' life satisfaction – a key component of happiness.

Updated: Apr 27, 2016, 23:44 PM IST
Are you or people around you happy or sad? Twitter has the answer!

New York: How can you detect whether someone is happy or sad? No, we're not talking about your BFF or your intuition or gut instincts.

Apparently, the answer lies in the social media microblogging site, Twitter, which can detect life satisfaction of the users.

A team of computer scientists, which includes one of Indian origin, have developed an algorithm of sorts, with which to detect one's level of contentment with life.

The team from University of Iowa used two years of Twitter data to measure users' life satisfaction – a key component of happiness.

Chao Yang and Padmini Srinivasan, the lead researchers of the study, feel that their research is different from most social media studies on happiness, since it gives an insight to how users feel about their lives over time instead of how they feel in the moment.

Yang and Srinivasan mined data from about three billion tweets from October 2012 to October 2014.

They limited their data set to only first-person tweets with the words "I," "me," or "mine" in them to increase the likelihood of getting messages that conveyed self-reflection.

They developed algorithms to capture the basic ways of expressing satisfaction or dissatisfaction with one's life.

They used these statements to build retrieval templates to find expressions of life satisfaction and their synonyms on Twitter.

The team found that people's feelings of long-term happiness and satisfaction with their lives remained steady over time - unaffected by external events such as an election, a sports game, or an earthquake in another country.

The findings contrast with previous social media research on happiness, which typically has looked at short-term happiness (called "affect") and found that people's daily moods were heavily influenced by external events.

Yang and Srinivasan found satisfied users were active on Twitter for a longer period of time and used more hashtags and exclamation marks but included fewer URLs in their tweets.

Dissatisfied users were more likely to use personal pronouns, conjunctions and profanity in their tweets.

Dissatisfied users were at least 10 percent more likely than satisfied users to express negative emotion, anger and sadness and to use words such as "should," "would," "expect," "hope," and "need" that may express determination and aspirations for the future.

According to Srinivasan, research like this is significant because life satisfaction is a big component of happiness.

“With this research, we can get a better understanding of the differences between those who express satisfaction and those who express dissatisfaction with their life,” she noted in a study published in the journal PLOS One.

(With IANS inputs)