Researchers at the University of Iowa claim that happy and satisfied people are active on Twitter for a longer period of time, uses more hashtags and exclamation marks and uses fewer URLs in their tweets than dissatisfied ones. On the other hand, dissatisfied users were more likely to use conjunctions, personal pronouns, and profanity in their tweets.
Moreover, they also found that dissatisfied users are 10 percent more likely to express their negative feelings such as anger and sadness. They would have a tendency to use words that indicate determination and future aspirations such as “should,” “would,” “expect,” “hope,” and “need.”
These dissatisfied users also have the inclination to use sexual words and use these words in a negative context. They are also 10 percent more likely to use words associated with depression, anxiety and death.
Meanwhile, satisfied users were 10 percent more likely to use words associated with money and religion. The same users were also more likely to convey more positive emotions even when talking about health and sexuality.
However, those users who initially express satisfaction but later changed to expressing dissatisfaction posted more tweets about sadness, death, depression, anger and anxiety. Those who continued to tweet satisfaction were less likely to post these same things.
Since satisfaction is a major part of happiness, the researchers say that their study provided better insights into how people expressed their emotions unlike other research. Nevertheless, they admit that more studies are still needed to determine which users are at risk from being satisfied to becoming dissatisfied. The study could also explore how the use of medications and user’s linguistic capability affect one’s general life satisfaction.
The findings are from the study published on March 16 in the journal PLOS One that used algorithms to analyse three billion tweets from October 2012 to October 2014. Unlike other social media research regarding happiness and satisfaction, this one looked into how users feel about their lives over time instead of looking at the user’s short-term happiness at one moment.