Jacob Ratkiewicz

Truthy: mapping the spread of astroturf in microblog streams

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We demonstrate a web service that tracks political memes in Twitter and helps detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We also present some cases of abusive behaviors uncovered by our service. Our web service is based on an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events.

Characterizing and modeling the dynamics of online popularity

Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire countrys Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics.

The role of information diffusion in the evolution of social networks

Every day millions of users are connected through online social networks, generating a rich trove of data that allows us to study the mechanisms behind human interactions. Triadic closure has been treated as the major mechanism for creating social links: if Alice follows Bob and Bob follows Charlie, Alice will follow Charlie. Here we present an analysis of longitudinal micro-blogging data, revealing a more nuanced view of the strategies employed by users when expanding their social circles. While the network structure affects the spread of information among users, the network is in turn shaped by this communication activity. This suggests a link creation mechanism whereby Alice is more likely to follow Charlie after seeing many messages by Charlie. We characterize users with a set of parameters associated with different link creation strategies, estimated by a Maximum-Likelihood approach. Triadic closure does have a strong effect on link formation, but shortcuts based on traffic are another key factor in interpreting network evolution. However, individual strategies for following other users are highly heterogeneous. Link creation behaviors can be summarized by classifying users in different categories with distinct structural and behavioral characteristics. Users who are popular, active, and influential tend to create traffic-based shortcuts, making the information diffusion process more efficient in the network.

Badvertisements: Stealthy Click-Fraud with Unwitting Accessories

ABSTRACT We describe a new type of threat to the Internet infrastructure, in the shape of a highly efficient but very well camouflaged click-fraud attack on the advertising infrastructure. The attack, which we refer to as a “badvertisement,” is described and experimentally verified on several prominent advertisement schemes. This stealthy attack can be thought of as a threatening mutation of spam and phishing attacks, with which it has many commonalities, except for the fact that it is not the targeted individual who is the victim in the attack, but the unwitting advertiser.

Traffic in Social Media II: Modeling Bursty Popularity

Online popularity has enormous impact on opinions, culture, policy, and profits, especially with the advent of the social Web and Web advertising. Yet the processes that drive popularity in our online world have only begun to be explored. We provide a quantitative, large scale, longitudinal analysis of the dynamics of online content popularity in two massive model systems, the Wikipedia and an entire countrys Web space. In these systems, we track the change in the number of links to pages, and the number of times these pages are visited. We find that these changes occur in bursts, whose magnitude and time separation are very broadly distributed. This finding is in contrast with previous reports about news-driven content, and has profound implications for understanding collective attention phenomena in general, and Web trends in particular. To make sense of these empirical results, we offer a simple model that mimics the exogenous shifts of user attention and the ensuing non-linear perturbations in popularity rankings. While established models based on preferential attachment are insufficient to explain the observed dynamics, our stylized model is successful in recovering the key features observed in the empirical analysis of our systems.

Traffic in Social Media I: Paths Through Information Networks

Wikipedia is used every day by people all around the world, to satisfy a variety of information needs. We cross-correlate multiple Wikipedia traffic data sets to infer various behavioral features of its users: their usage patterns (e.g., as a reference or a source); their motivations (e.g., routine tasks such as student homework vs. information needs dictated by news events); their search strategies (how and to what extent accessing an article leads to further related readings inside or outside Wikipedia); and what determines their choice of Wikipedia as an information resource. We primarily study article hit counts to determine how the popularity of articles (and article categories) changes over time, and in response to news events in the English-speaking world. We further leverage logs of actual navigational patterns from a very large sample of Indiana University users over a period of one year, allowing us unprecedented ability to study how users traverse an online encyclopedia. This data allows us to make quantitative claims about how users choose links when navigating Wikipedia. From this same source of data we are further able to extract analogous navigation networks representing other large sites, including Facebook, to compare and contrast the use of these sites with Wikipedia. Finally we present a possible application of traffic analysis to page categorization.