The Internet consists of about 13,000 Autonomous Systems (AS's) that exchange routing information using the Border Gateway Protocol (BGP). The operators of each AS must have control over the flow of traffic through their network and between neighboring AS's. However, BGP is a complicated, policy-based protocol that does not include any direct support for traffic engineering. In previous work, we have demonstrated that network operators can adapt the flow of traffic in an efficient and predictable fashion through careful adjustments to the BGP policies running on their edge routers. Nevertheless, many details of the BGP protocol and decision process make predicting the effects of these policy changes difficult. In this paper, we describe a tool that predicts traffic flow at network exit points based on the network topology, the import policy associated with each BGP session, and the routing advertisements received from neighboring AS's. We present a linear-time algorithm that computes a network-wide view of the best BGP routes for each destination prefix given a static snapshot of the network state, without simulating the complex details of BGP message passing. We describe how to construct this snapshot using the BGP routing tables and router configuration files available from operational routers. We verify the accuracy of our algorithm by applying our tool to routing and configuration data from AT&T's commercial IP network. Our route prediction techniques help support the operation of large IP backbone networks, where interdomain routing is an important aspect of traffic engineering.
Twitter is an efficient conduit of information for millions of usersaround the world. Its ability to quickly spread information to a largenumber of people makes it an efficient way to shape information and,hence, shape public opinion. We study the tweeting behavior of Twitter propagandists, users who consistently expressthe same opinion or ideology, focusing on two online communities: the2010 Nevada senate race and the 2011 debt-ceiling debate. We identify several extreme tweeting patterns thatcould characterize users who spread propaganda: (1) sending high volumesof tweets over short periods of time, (2) retweeting whilepublishing little original content, (3) quickly retweeting, and (4) colluding with other, seeminglyunrelated, users to send duplicate or near-duplicate messages on thesame topic simultaneously. These four features appear to distinguishtweeters who spread propaganda from other more neutral users and could serve asstarting point for developing behavioral-based propaganda detection techniquesfor Twitter.
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