Influence of augmented humans in online interactions during voting events

The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive voting event can be used to build an accurate map of real-world political parties and electoral ranks. We provide evidence that information flow and collective attention are often driven by a special class of highly influential users, that we name "augmented humans", who exploit thousands of automated agents, also known as bots, for enhancing their online influence. We show that augmented humans generate deep information cascades, to the same extent of news media and other broadcasters, while they uniformly infiltrate across the full range of identified groups. Digital augmentation represents the cyber-physical counterpart of the human desire to acquire power within social systems.Comment: 11 page

The Dynamics of Multi-Modal Networks

The widespread study of networks in diverse domains, including social, technological, and scientific settings, has increased the interest in statistical and machine learning techniques for network analysis. Many of these networks are complex, involving more than one kind of entity, and multiple relationship types, both changing over time. While there have been many network analysis methods proposed for problems such as network evolution, community detection, information diffusion and opinion leader identification, the majority of these methods assume a single entity type, a single edge type and often no temporal dynamics. One of the main shortcomings of these traditional techniques is their inadequacy for capturing higher-order dependencies often present in real, complex networks. To address these shortcomings, I focus on analysis and inference in dynamic, multi-modal, multi-relational networks, containing multiple entity types (such as people, social groups, organizations, locations, etc.), and different relationship types (such as friendship, membership, affiliation, etc.). An example from social network theory is a network describing users, organizations and interest groups, where users have different types of ties among each other, such as friendship, family ties, etc., as well as affiliation and membership links with organizations and interest groups. By considering the complex structure of these networks rather than limiting the analysis to a single entity or relationship type, I show how we can build richer predictive models that provide better understanding of the network dynamics, and thus result in better quality predictions. In the first part of my dissertation, I address the problems of network evolution and clustering. For network evolution, I describe methods for modeling the interactions between different modalities, and propose a co-evolution model for social and affiliation networks. I then move to the problem of network clustering, where I propose a novel algorithm for clustering multi-modal, multi-relational data. The second part of my dissertation focuses on the temporal dynamics of interactions in complex networks, from both user-level and network-level perspectives. For the user-centric approach, I analyze the dynamics of user relationships with other entity types, proposing a measure of the "loyalty" a user shows for a given group or topic, based on her temporal interaction pattern. I then move to macroscopic-level approaches for analyzing the dynamic processes that occur on a network scale. I propose a new differential adaptive diffusion model for incorporating diversity and trust in the process of information diffusion on multi-modal, multi-relational networks. I also discuss the implications of the proposed diffusion model on designing new strategies for viral marketing and influential detection. I validate all the proposed methods on several real-world networks from multiple domains

Characterizing interactions in online social networks during exceptional events

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events

Predicting Successful Memes using Network and Community Structure

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on Weblogs and social media (ICWSM 2014

Getting the word out on Twitter: the role of influentials, information brokers and strong ties in building word-of-mouth for brands

Using a sample of over 5300 tweets from top global brands, this study investigated how different types of users can influence brand content diffusion via retweets. Twitter users who influenced followers to retweet brand content were categorized as (1) influentials, because of their above average ability to influence others to retweet their tweets (in general), (2) information brokers, because of their position connecting groups of users or (3) having strong ties, because of their high percentage of friends in common and a mutual friend–follower relationship with the influenced follower. The results indicate that influentials and information brokers are associated with larger number of retweets for brand content. In addition, although information brokers have a larger overall influence on retweeting, they are more prone to do so when influentials are mentioned in the brand tweet, providing support for the strategy that aims to associate the brand with influential users

Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as "social sensors." Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We also show that these findings hold true across cultures by analyzing tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.Comment: 17 pages, 8 figure