Online news, microblogs and other media documents all contain valuable insight regarding events and responses to events. Underlying these documents is the concept of framing, a process in which communicators act (consciously or unconsciously) to construct a point of view that encourages facts to be interpreted by others in a particular manner. As media discourse evolves, how topics and documents are framed can undergo change, shifting the discussion to different viewpoints or rhetoric. What causes these shifts can be difficult to determine directly; however, by linking secondary datasets and enabling visual exploration, we can enhance the hypothesis generation process. In this paper, we present a visual analytics framework for event cueing using media data. As discourse develops over time, our framework applies a time series intervention model which tests to see if the level of framing is different before or after a given date. If the model indicates that the times before and after are statistically significantly different, this cues an analyst to explore related datasets to help enhance their understanding of what (if any) events may have triggered these changes in discourse. Our framework consists of entity extraction and sentiment analysis as lenses for data exploration and uses two different models for intervention analysis. To demonstrate the usage of our framework, we present a case study on exploring potential relationships between climate change framing and conflicts in Africa.

In an effort to reduce overhead in proactive protocols, rather than use a global period for transmission of control information, we propose to transmit based on changes in a node's local conditions. For OLSR, a proactive link-state routing protocol, we use a significant change in the number of edges in a node's two-hop neighbourhood and topological graph as indicators of topology change warranting an update. When the associated exponentially weighted moving average (EWMA) charts and multivariate EWMA chart signal out-of-control, a node transmits a HELLO, a topology control (TC), or both HELLO and TC messages. We use ns-2 simulations to compare OLSR to our TS-OLSR, and other variants of OLSR. We find that TS-OLSR obtains a statistically significant reduction in overhead compared to the other protocols while maintaining the packet delivery ratio. The approach is general and may find use in other applications requiring response to changes in local conditions.

Abstract-Response surface methodology (RSM) is a collection of statistical design and numerical optimization techniques traditionally used to optimize industrial processes. In this paper, we demonstrate that the methodology can be successfully applied to the domain of networking. Specifically, we obtain increased throughput with a significant decrease in delay in a ns-2 simulation model of a mobile ad hoc network (MANET) by using RSM to optimize protocol interaction found by factor screening. Whether the experimentation is with a stochastic simulation model or a physical system, such as a MANET or a wireless sensor network test-bed, RSM provides a general and practical methodology to screen factors and robustly and jointly optimize responses.