Tweets from mobile devices are more likely to be egocentric

Dhiraj Murthy (Goldsmiths, University of London), Sawyer Bowman (Bowdoin College), Alexander J. Gross (University of Maine), and Marisa McGarry (University of Maine) published their findings in the Journal of Communication. The researchers conducted an analysis of tweets to see if presentations of self are more likely to be more egocentric, negative/positive, gendered or communal based on whether users were on a mobile device or using a web based platform.

Over the course of six weeks, the researchers collected 235 million tweets. 90% of the top sources to access Twitter were coded to denote mobile, non-mobile and mixed sources. Drawing from social psychological methods, they then studied language use in tweets by analyzing the frequency and ratios of words traditionally associated with social and behavioral characteristics.

The researchers found that mobile tweets are not only more egocentric in language than any other group, but that the ratio of egocentric to non-egocentric tweets is consistently greater for mobile tweets than from non-mobile sources. They also did not find that mobile tweets were particularly gendered. Regardless of platform, tweets tended to employ words traditionally associated as masculine.

Previous studies have linked activities performed face-to-face (e.g. eating dinner) to tweets from a particular source. And there has been research that aims to classify tweets as belonging to a particular sentiment by using word lists. This is one of the first studies to take a look at how mobile versus non-mobile plays a part in the language used on social media.

"Very little work has been done comparing how our social media activities vary from mobile to non-mobile. And as we increasingly use social media from mobile devices, the context in which one uses social media is a critical object of study," said Murthy. "Our work is transformative in this understudied field as we found that not all tweets are the same and the source of tweets does influence tweeting patterns, like how we are more likely to tweet with negative language from mobile devices than from web-based ones."