Author List: Stieglitz, Stefan; Dang-Xuan, Linh;
Journal of Management Information Systems, 2013, Volume 29, Issue 4, Page 217-248.
As a new communication paradigm, social media has promoted information dissemination in social networks. Previous research has identified several content-related features as well as user and network characteristics that may drive information diffusion. However, little research has focused on the relationship between emotions and information diffusion in a social media setting. In this paper, we examine whether sentiment occurring in social media content is associated with a user's information sharing behavior. We carry out our research in the context of political communication on Twitter. Based on two data sets of more than 165,000 tweets in total, we find that emotionally charged Twitter messages tend to be retweeted more often and more quickly compared to neutral ones. As a practical implication, companies should pay more attention to the analysis of sentiment related to their brands and products in social media communication as well as in designing advertising content that triggers emotions.
Keywords: information diffusion; sentiment; social media; Twitter
Algorithm:

List of Topics

#234 0.257 social networks influence presence interactions network media networking diffusion implications individuals people results exchange paper sites evidence self-disclosure important examine
#131 0.132 media social content user-generated ugc blogs study online traditional popularity suggest different discourse news making anonymity marketing videos choices page
#290 0.130 emotions research fmri emotional neuroscience study brain neurois emotion functional neurophysiological distrust cognitive related imaging tools effects warnings magnetic turn
#220 0.080 research study different context findings types prior results focused studies empirical examine work previous little knowledge sources implications specifically provide
#133 0.063 data predictive analytics sharing big using modeling set power inference behavior explanatory related prediction statistical generated substantially novel building million
#4 0.051 characteristics experience systems study prior effective complexity deal reveals influenced companies type analyze having basis conducted determine complex comparative drive