Do you know how happy people in your neighbourhood are? The answer lies in Twitter, say computer scientists including an Indian-American researcher who have developed an algorithm to detect life satisfaction on Twitter.
The team from from University of Iowa used two years of Twitter data to measure users’ life satisfaction.
According to researchers Chao Yang and Padmini Srinivasan, their study is different from most social media research on happiness because it looks at how users feel about their lives over time instead of how they feel in the moment.
“The traditional methods of studying happiness have been through surveys and observations and that takes a lot of effort,” Srinivasan said.
“But if you can actually tap into social media and get observations, I think it would be unwise to ignore that opportunity,” she added.
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.
For example, the template for the statement “my life is great” also would include statements such as “my life is wonderful,” “my life is fabulous,” etc.
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.