In this work we explore a simple influence problem using a novel variation of the Bounded Confidence Model (BCM) of opinion dynamics that can be applied to varying types of social media platforms with transient user populations. Via simulation-based analyses, we set out to understand the extent to which the opinions of a transient population that divides their attention across multiple channels is either more or less susceptible to progressively more extreme opinions.

In this paper, we present MATRICS, a humanmachine hybrid system that accurately performs geopolitical forecasting by combining crowdsourcing with ensemble machine learning on online data. The system employs a pair of parallel, but highly-interconnected processing pipelines to perform “machine-aided human forecasting” and “human-aided machine forecasting”. This configuration allows the machine to provide information to the human population, saving research time and reducing fatigue, while simultaneously allowing the human population to provide feedback to the machine learning components, allowing them to filter data sources and quickly adapt to a task via online machine learning. The final forecast for each question was computed as an aggregate of the human and machine responses. The system was evaluated using data collected during the IARPA Hybrid Forecasting Competition, in which it answered 187 forecasting questions with a mean Brier score of 0.27 using volunteers and participants that were recruited via Amazon Mechanical Turk and open-source “big” data scraped from online sources such as social media, search engine results, and online historical data.

We present the design and implementation of an automated event summarization system that leverages publicly available data from online sources. A novel Network of Networks (NoN) model is proposed to represent a multimodal data set comprising microblog posts, news articles, and images that describe current attitudes, trends, and events being shared by individuals and organizations. In this model, networks are arranged in layers that represent the different modalities, and the nodes within a given layer account for the information in that modality. Edges connect pairs of nodes - possibly from different modalities - based on the topical similarity of the content. A novel ranking algorithm is developed that selects a topically diverse collection of nodes across the different layers (i.e., tuples) that serve as representative highlights of the events described in the network. Tuples are subsequently stitched together in a temporal sequence in order to generate a consistent event storyline. This stitching is accomplished by framing the problem as a modified longest path problem in a directed acyclic graph. Our proposed system has been fully implemented using parallel computing paradigms for scalable data processing and real-time analysis, and initial experiments have been conducted on real-world events to demonstrate the effectiveness of the system.

The tides of sentiments expressed in online social media rise and fall. In recent years, the availability of big data has afforded researchers the ability to develop and evaluate techniques that allow us to identify, classify, aggregate, and even predict the sentiment dynamics for nearly any topic [1], [2]. The users of online social media platforms like Twitter are able to create, propagate, and consume information pertaining to any conceivable topic, and in doing so, they influence each other's opinions and behavior. Herding behavior and online sentiment are mutually reinforcing, and have been shown to influence consumer purchasing decisions [3], [4].

This paper introduces a model of strategic network formation grounded in two disparate modes of acquiring social capital - brokerage and closure - through the unification of a dual-level view of interactions between individuals and between groups of individuals referred to as structural autonomy. After motivating and introducing the model, we establish the existence of equilibrium and propose interesting open questions and extensions to the basic model for future research.

This paper proposes a Computational Intelligence framework for network security management. The framework uses reinforcement learning and Bayesian methods to achieve cross-layer optimization in heterogeneous, multi-layer wireless and wireline networks. Metrics based on the reputation of a node are used to measure performance. OPNET simulations results indicate that routing algorithms based on the newly proposed framework exhibit nearly four times improved network performance measured in terms of goodput.

Traditional hop-by-hop dynamic routing makes inefficient use of network resources as it forwards packets along already congested shortest paths while uncongested longer paths may be underutilized. To maintain network-wide load balancing, we propose Autonomous Network management with Team learning based Self-configuration (ANTS) which attempts to manage a feasible route for traffic flow with QoS constraints in heterogeneous networks. To enable cognitive intelligence for network-wide load balancing, we implement a cross-layer mechanism in which learning agents in middleware layer can monitor the queue sizes of MAC layer, thereby allowing for the discovery of optimal routes. We present OPNET simulation results illustrating that, in comparison to original OSPF and AODV (2.18 Mbits/s with 46.46% packet loss rate), ANTS dramatically achieves a higher packet delivery (9.57 Mbits/s with 0.53% packet loss rate).