SOCIAL media have become pervasive and ubiquitous and represent a source of valuable information. The literature on social media makes a distinction between influencers and influence. The former are social media users with a broad audience. For example, influencers can have a high number of followers on Twitter, or a multitude of friends on Facebook, or a broad array of connections on LinkedIn. The term influence is instead used to refer to the social impact of the content shared by social media users. The majority of these studies has focused on the role of influencers. Our claim is that while the information shared by influencers has a broader reach, the content of messages plays a critical role and can be a determinant of the social influence of the message irrespective of the centrality of the message’s author. This thesis starts from the observation that social networks of influence follow a power-law distribution function, with a few hub nodes and a long tail of peripheral nodes, consistent with the so-called small-world phenomenon. In social media, hub nodes represent social influencers, but influential content can be generated by peripheral nodes and spread along possibly multi-hop paths originated in peripheral network layers. This thesis provides a conceptual framework and related software tool to assess influence and identification of influencers. The assessment of influence and influencers is performed in two steps. First, an empirical analysis is conducted in order to verify the assumption that content can have an impact on influence. We propose a visual approach to the graphical representation and exploration of peripheral layers and clusters by exploiting the theory of k-shell decomposition analysis and power-law based modified force-directed method to clearly display local multi-layered neighborhood clusters around hub nodes. We put forward few hypotheses that tie specificity, frequency of tweets and frequency of retweets and are tested on data samples of roughly one million tweets. Overall, results highlight the effectiveness of our approach, providing interesting visual insights on how unveiling the structure of the periphery of the network can visually show the potential of peripheral nodes in determining influence and content relationship. Secondly, this thesis aims to provide a novel visual framework to analyze, explore and interact with Twitter ‘Who Follows Who’relationships, by visually browsing the friends’ network to identify the key influencers based upon the actual influence of the content they share. As part of this research, we have developed NavigTweet, a novel visualization tool for the influence-based exploration of Twitter network. The core concept of the proposed approach is to identify influencers by browsing through a user’s friends’ network. Then, a power-law based modified force-directed method is applied to clearly display the network graph in a multi-layered and multi-clustered way. To gather some insight into the user experience with the pilot release of NavigTweet, we have conducted a qualitative pilot user study. We report on the study and its results, with initial pilot release.