London, Sep 8 (IANS) The "happy" or "sad" tweets may not accurately reflect a Twitter user's actual emotional state offline, suggests new research.
Rather, Twitter users have developed their own unique cultural behaviour, conversations and identities, which shape the ways in which they present their views online, said the study published in the journal PLOS ONE.
There is no evidence that social media content shared on Twitter is a truthful reflection of how its users feel, said study author Eric Jensen, Associate Professor at University of Warwick in Britain.
With increasing use of social media, there has been growing interest in tapping this existing source of easily accessible "big data" to develop social science knowledge.
With over 300 million monthly active users around the globe sharing their thoughts in 140 characters or less, studies based on Twitter data are "particularly alluring" to researchers and the media.
The new findings highlight the problems of drawing broader conclusions from a sample of Twitter users.
Twitter is an unreliable witness to the world's emotions, Jensen said.
"Twitter users present only one side of themselves on social media, shielding their true feelings for good reasons, such as professional reputation," Jensen added.
Social convention, power relationships and identity influence online conversation just as much as off-line interactions, but in ways that are not yet fully understood, the study said.
Moreover, it has been proven in several studies that Twitter users are not representative of the general population. In just one example, men are much more likely to use Twitter than women. Prolific users who tweet many times a day may be over-represented in any sample dataset.
"There is clearly a large gap between what people post on social media and how they really feel, but how exactly people manage the relationship between their offline and social media identities is still being uncovered," he added.
(This story has not been edited by Social News XYZ staff and is auto-generated from a syndicated feed.)
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