The anatomy of tweet overload: How number of tweets received, number of friends, and egocentric network density affect perceived information overload

摘要

More than 21 million monthly active users (MAUs) in Japan read, communicate, and share information with others via Twitter (in May 2013). In this study, we focused on perceived information overload by analyzing the number of tweets received, number of friends, and density of a user's egocentric network. These three variables were examined using objective data collected through Twitter's open Application Programming Interfaces (APIs). We collected data concerning tweet overload through a web-based survey, and we used an ordered logistic regression analysis to examine the combined data (n = 1277). Results demonstrated that only the number of friends had a significantly positive effect on perceived tweet overload, while the number of tweets received did not produce a significant effect. Although the density of a user's egocentric network did not demonstrate any significant effect on perceived tweet overload, a significant interaction effect appeared between the number of friends and the density of this network. In other words, findings indicated that a large number of friends strengthened the network density's effect; by contrast, a smaller number of friends strengthened network density but reduced perceived tweet overload. The findings are discussed in detail in this article. (C) 2015 Elsevier Ltd. All rights reserved.