Analyzing Ideological Communities in Congressional Voting Networks

We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties

Unconscious Awareness of a Branded Life: Consumer Disillusionment and the Cultivated Commercialization of Public Health

By unraveling the intricately powerful influences of pharmaceutical funding, this project examines ways in which product marketing infiltrates and contaminates public awareness efforts in the healthcare industry. Specifically, the following work deconstructs ways in which Merck Pharmaceuticals & Co. crafted a product endorsement through social marketing and nationwide lobbying efforts to most efficiently profit from the company’s Gardasil vaccination. Through means of textual analysis, interviews, focus groups, and eyetracking experimentation, I use Merck’s product endorsement efforts to illuminate the complex dynamics muddling direct-to-consumer marketing and social marketing campaigns. Social cognitive theory (SCT) offers a strong supportive foundation from which to dissect viewer healthcare message processing. In conjunction with the behaviorally-oriented cannons of SCT, social trust theory and contemporary marketing scholarship further highlight the complicated ties uniting public policy, corporatized health-marketing operations, audience cognitions, and consumer behavior. By piecing together the various ways in which Merck Pharmaceuticals puppeteered public understanding of HPV and cervical cancer, this work encourages greater awareness for the corporate influence and political agendas that work hand in hand in delivering meaning to American reality. Results indicate viewer awareness of brand markings in Merck’s HPV social marketing campaign limit message effectiveness and negatively influence consumer trust. As such, my grounded analysis conceptualizes “unconscious awareness” as it relates to branded health communication. Emergent findings showcase broader societal implications by unveiling patterns of conditioned ambivalence toward commercialized messaging. This project speaks to the capitalized communications contaminating consumer trust and public health, and presents an argument for regulation realignment in the healthcare industry. Given the sensitive nature of public health message processing, and in light of the findings collected throughout this work, my multi-layered analysis petitions for regulatory guidelines which separately address and more clearly define executional protocols for social awareness efforts and direct-to-consumer marketing operations

From Sensor to Observation Web with Environmental Enablers in the Future Internet

This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities’ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)

Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections

Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels

Modeling and Analyzing Collective Behavior Captured by Many-to-Many Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Network communities and the foreign exchange market

Many systems studied in the biological, physical, and social sciences are composed of multiple interacting components. Often the number of components and interactions is so large that attaining an understanding of the system necessitates some form of simplication. A common representation that captures the key connection patterns is a network in which the nodes correspond to system components and the edges represent interactions. In this thesis we use network techniques and more traditional clustering methods to coarse-grain systems composed of many interacting components and to identify the most important interactions.\ud \ud This thesis focuses on two main themes: the analysis of financial systems and the study of network communities, an important mesoscopic feature of many networks. In the first part of the thesis, we discuss some of the issues associated with the analysis of financial data and investigate the potential for risk-free profit in the foreign exchange market. We then use principal component analysis (PCA) to identify common features in the correlation structure of different financial markets. In the second part of the thesis, we focus on network communities. We investigate the evolving structure of foreign exchange (FX) market correlations by representing the correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes and uncovers major trading changes that occurred in the market. Finally, we consider the community structure of networks from a wide variety of different disciplines. We introduce a framework for comparing network communities and use this technique to identify networks with similar mesoscopic structures. Based on this similarity, we create taxonomies of a large set of networks from different fields and individual families of networks from the same field

Hierarchical, Decentralized, or Something Else? Opposition Networks in the German Bundestag

Members of the German parliament may force government to publicly answer questions by issuing minor interpellations (kleine Anfragen). We use 3,608 interpellations from the session 2009-13 that have been signed by authoring and supporting members to construct the social network of support relations among members within the three opposition parties. We find that parties differ markedly in terms of internal structure. While social democrats organize hierarchically, Greens cooperate horizontally. The network for socialist Linke in contrast shows signs of homophily and social segregation. Our approach yields a novel perspective on intraparty politics in parliamentary systems which are notoriously difficult to analyze

AI approaches to understand human deceptions, perceptions, and perspectives in social media

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens\u27 perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups. This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine Learning approaches for fake news detection models, and a hybrid method for topic identification, whether they are fake or real. To understand the user\u27s perceptions or attitude toward some topics, this study analyzes the sentiments expressed in social media text. The sentiment analysis of posts can be used as an indicator to measure how topics are perceived by the users and how their perceptions as a whole can affect decision makers in government and industry, especially during the COVID-19 pandemic. It is difficult to measure the public perception of government policies issued during the pandemic. The citizen responses to the government policies are diverse, ranging from security or goodwill to confusion, fear, or anger. This dissertation provides a near real-time approach to track and monitor public reactions toward government policies by continuously collecting and analyzing Twitter posts about the COVID-19 pandemic. To address the social media\u27s overwhelming number of posts, content echo-chamber, and information isolation issue, this dissertation provides a multiple view-based summarization framework where the same contents can be summarized according to different perspectives. This framework includes components of choosing the perspectives, and advanced text summarization approaches. The proposed approaches in this dissertation are demonstrated with a prototype system to continuously collect Twitter data about COVID-19 government health policies and provide analysis of citizen concerns toward the policies, and the data is analyzed for fake news detection and for generating multiple-view summaries