Discovering and validating influence in a dynamic online social network

Abstract

Online human interactions take place within a dynamic hierarchy, where social influence is determined by qualities such as status, eloquence, trustworthiness, authority and persuasiveness. In this work, we consider topic-based twitter interaction networks, and address the task of identifying influential players. Our motivation is the strong desire of many commercial entities to increase their social media presence by engaging positively with pivotal bloggers and tweeters. After discussing some of the issues involved in extracting useful interaction data from a twitter feed, we define the concept of an active node subnetwork sequence. This provides a time-dependent, topic-based, summary of relevant twitter activity. For these types of transient interactions, it has been argued that the flow of information, and hence the influence of a node, is highly dependent on the timing of the links. Some nodes with relatively small bandwidth may turn out to be key players because of their prescience and their ability to instigate follow-on network activity. To simulate a commercial application, we build an active node subnetwork sequence based on key words in the area of travel and holidays. We then compare a range of network centrality measures, including a recently proposed version that accounts for the arrow of time, with respect to their ability to rank important nodes in this dynamic setting. The centrality rankings use only connectivity information (who tweeted whom, when), without requiring further information about the account type or message content, but if we post-process the results by examining account details, we find that the time-respecting, dynamic approach, which looks at the follow-on flow of information, is less likely to be ‘misled’ by accounts that appear to generate large numbers of automatic tweets with the aim of pushing out web links. We then benchmark these algorithmically derived rankings against independent feedback from five social media experts, given access to the full tweet content, who judge twitter accounts as part of their professional duties. We find that the dynamic centrality measures add value to the expert view, and can be hard to distinguish from an expert in terms of who they place in the top ten. These algorithms, which involve sparse matrix linear system solves with sparsity driven by the underlying network structure, can be applied to very large-scale networks. We also test an extension of the dynamic centrality measure that allows us to monitor the change in ranking, as a function of time, of the twitter accounts that were eventually deemed influential

Modern temporal network theory: a colloquium

“…In another paper [82], Grindrod and Higham propose a path-based centrality measure that downweigthts long and old paths. In yet another paper [155] these (and some other) authors validate centrality measures for a temporal Twitter network by (among other ways) a panel of experts.…”

Block Matrix Formulations for Evolving Networks

“…See [19] for a more detailed explanation. Numerical tests in [19] showed that these broadcast and receive centralities are generally very different from the measures that arise when we ignore time-dependency and consider only the aggregate adjacency matrix M k=1 A [k] , and subsequent work in [26] showed that they were better able to match the views of social media experts when applied to Twitter data. Motivated by the treatment of static networks in [10], the authors in [1] used the dynamic communicability matrix idea to introduce two kinds of dynamic betweenness: the nodal betweenness of a node and the temporal betweenness of a time point.…”

Time-Dependent Influence Measurement in Citation Networks

“…However, citations are time-sensitive -indeed, it would be expected for more recent papers to be cited more often. On the other hand, they are not very rapidly disappearing, in comparison to, for instance, tweets [7]. It is also important to notice that some of the considered pairs of conferences may not start operating at the same time (i.e.…”

“…Furthermore, as both influence is highly context-specific [9] and different network types present various dynamicity characteristics, time-dependency models are not universal. Work [7] creates time-respecting dynamic approach for capturing influence of Twitter, however highly time-sensitive, quickly decaying influence of a tweet differs greatly from influence concerning citation between scientific papers. There have been several research works dealing with issues within the citation networks scope, such as studying papers topic evolution [21], predicting paper influence [22] or ranking experts [23], however, not focusing neither on pairwise influence between communities nor on problem of time-sensitiveness of influence.…”