Social media offer platforms that anyone can use, giving the opportunity to share information among networks in an easy and interactive way. It is not a surprise that social media marketing has become a primary focus on both digital and traditional revenue models of businesses. In this work, information sharing by users in the context of Twitter is studied, by modeling message’s characteristics and users’ behavior about Greek 2015 January elections. A detailed data set about tweets’ characteristics such as length, existence of URLs or hashtags and mentioning of other users, is collected after the elections day, and the relationships between related users and network’s responses on the shared tweets, are examined. An unsupervised clustering model is implemented on tweets’ characteristics using CRISP-DM methodology. The empirical results suggest the existence of different content groups, such as tweets with extensive text, URLs and hashtags which can be characterized as “Linked” type of shared content.