Stochastic Sampling and Machine Learning Techniques for Social Media State Production

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries\u27 use of the global information environment. Our hope is that this type of simulation can be used to predict and understand the spread of misinformation, false narratives, fraudulent financial pump and dump schemes, and cybersecurity threats. To do this, our research team has created an agent-based model that can handle a variety of prediction tasks. This dissertation introduces a set of sampling and deep learning techniques that we developed to predict specific aspects of the evolution of online social networks that have proven to be challenging to accurately predict with the agent-based model. First, we compare different strategies for predicting network evolution with sampled historical data based on community features. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over different datasets. Second, we introduce a deep learning model for burst prediction. Bursts may serve as a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross-platform social media data is valuable for predicting bursts within a single social media platform. An LSTM model is proposed in order to capture the temporal dependencies and associations based upon activity information. These volume predictions can also serve as a valuable input for our agent-based model. Finally, we conduct an exploration of Graph Convolutional Networks to investigate the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of targeted graph convolutional networks. Graph Convolutional Networks are important in the social network context as the sociological and anthropological concept of \u27homophily\u27 allows for the method to use network associations in assisting the attribute predictions in a social network

Aging Pipeline Infrastructure in the United States: How do a changing policy mix, issues of energy justice, and social media communication impact future risk analysis?

Over two and a half million miles of pipeline cross the United States today, half of which is over fifty years old and thus was designed, located, and debated without today’s modern environmental policies in place. Aging pipeline infrastructure, such as the (infamous in Michigan) Enbridge Line 5 pipeline underwater crossing at Michigan’s Straits of Mackinac, has undergone increased public scrutiny and risk analysis this past decade. This has led to the potential for policy changes in the historically stable energy services institution associated with pipeline infrastructure regulation. While policy process literature generally describes how policy changes over time, it is missing research on how new goals and new technology, such as energy justice and social media, impact agenda setting and decisions when added to the policy mix. This dissertation first investigates the evolving federal pipeline regime policy goals through an advanced policy mix analysis. Next, it argues that energy justice research can be advanced through deterministic approaches and analyses. Last, this dissertation uses a social network analysis to explain why aging pipelines are on today’s policy agenda through social network analysis. By understanding how the pipeline policy mix has changed over time, including through the addition of modern topics such as energy justice and modern technologies such as social media, policy and decision makers can improve prioritization of risk analysis for aging pipeline infrastructure

ICT and InnovationA Step Forward to a Global Society

ItAIS (www.itais.org) was established in 2003 as the Italian Chapter of the Association for Information Systems (AIS - www.aisnet.org) and has since then been promoting the exchange of ideas, experience, and knowledge among both academics and professionals committed to the development, management, organization and use of information systems. The itAIS conference is the major annual event of the Italian Information System community and it is thought as a forum to promote discussions and experiences exchanges among researchers in the field, both from the academy and the industry. Being the current the eleventh edition, in 2016 itAIS was held in Verona. The previous editions took place in Rome on 2015, Genova on 2014, Milan on 2013, Rome on 2012 and 2011, Naples on 2010, Costa Smeralda on 2009, Paris on 2008, Venice on 2007, Milan on 2006, Verona on 2005, and again Naples on 2004. itAIS 2016 aims to bring together researchers, scientists, engineers, and doctoral students to exchange and share their experiences, ideas, challenges, solutions, and research results about all aspects related to the impact of Information Technology and Innovation Trends in Organizations. The conference includes 16 tracks: (1) Organizational change and Impact of ICT; (2) Accounting Information Systems; (3) Advanced ICT support for innovation strategies, management, and implementations; (4) Human-computer interaction; (5) Continuous Redesign of Socio-Technical Systems; (6) Digitalization trends in Human Resources Management; (7) e-Services, Social Networks, and Smartcities; (8) ICT-enabled innovation in public services: co-production and collaborative networking; (9) The new era of digitalization in Healthcare and Public sector; (10) IS (lost) in the Cloud; (11) Internet of Things: exploring tensions in global information infrastructures; (12) Technology- enhanced learning: transforming learning processes in organizations; (13) Supply Chain Resilience and Security; (14) Digital Marketing and Analytics. The participation success that has been registered in the previous editions is confirmed this year. The conference attracted more than 80 submissions from Italian and foreigner researchers. Among them, more 6 than 68 contributions have been accepted for presentation at the conference following a double-blind review process. Among them, 19 are published in this book, the other will appear in a volume of the Springer Series Lecture Notes in Information Systems and Organisations1. The conference took place at Economics Department, University of Verona (Santa Marta campus) on October 7th \u2013 8th, 2016 and is organized in 5 parallel sessions. We would like to thank all the authors who submitted papers and all conference participants. We are also grateful to the chairs of the fourteen tracks and the external referees, for their thorough work in reviewing submissions with expertise and patience, and to the President and members of the itAIS steering committee for their strong support and encouragement in the organization of itAIS 2016. A special thanks to all members of the Organizing Committee for their precious support to the organization and management of the event and in the publication of the enclosed proceedings

Anticipating Activity in Social Media Spikes

We propose a novel mathematical model for the activity of microbloggers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of insight into human behaviour has many applications, as it identifies key players who can be targeted with information in real time when the network is most receptive. The model takes account of the fact that dynamic interactions evolve over an underlying, static network that records "who listens to whom". Our fundamental assumption is that, in the case where the entire community has become aware of an external news event, a key driver of activity is the motivation to participate by responding to incoming messages. We validate the resulting algorithm on a large scale Twitter conversation concerning the appointment of a UK Premier League football club manager. We also find that the half-life of a spike in activity can be quantified in terms of the network size and the typical response rate