Social and Information Systems

Social Sensing

The advent of online social media (e.g., Twitter and Flickr), the ubiquity of wireless communication capabilities (e.g., 4G/5G and WiFi), and the proliferation of a wide variety of sensors in the possession of common individuals (e.g., smartphones) allow humans to create a deluge of unfiltered, unstructured, and unvetted data about their physical environment. This opens up unprecedented challenges and opportunities in the field of social sensing, where the goal is to distill accurate and credible information from social sources (e.g., humans) and devices in their possession that accurately describes the state of the physical world. The problem requires multi-disciplinary solutions that combine data mining, statistics, network science and cyber physical computing. Our research addresses the aforementioned needs by building theories, techniques and tools for accurately extracting high quality information from data generated with humans in the loop, and for reconstructing the correct "state of the world" both physical and social.

Using Smart Devices to Capture the Emotionality of Offline Communication

The increasing prevalence of online interactions may be inhibiting the development of strong, reciprocal, and emotionally significant offline social ties. In order to address this issue we are developing an innovative system using smart devices that detects speech traits indicative of various emotional states and provides data on offline emotionality needed to understand changing social networks.

Advancing Media Literacy

At a time when communities around the world increasingly turn to digital source for information, online and social media systems play a critical role in affecting attitudes and behavior. A core problem is that social media channels are being manipulated by malicious groups to spread misinformation in low and middle income countries (LMIC) to exacerbate social divides and influence citizen involvement in democratic processes. The spread and adoption of misinformation through digital channels is especially problematic because many users of online and social media systems are not aware of how (mis)information is spread through these channels. Notre Dame’s goal is to improve media literacy in low and middle income countries (LMIC) especially among new digital arrivals through a targeted digital media literacy campaign. Specifically, the team research question is: if Notre dame can provide customized online media literacy content to segmented audiences of new digital arrivals, then the recipients of media literacy will be less likely to engage with and spread misinformation.

Understanding Peace Processes through Social Media

Colombia's final peace agreement was a culmination of a decade-long peace process that outlines significant social, political and economic reforms to end the longest fought armed conflict in the Western Hemisphere. Peace processes are complex, protracted, contentious and dynamic systems which involve significant bargaining and compromising among various societal and political stakeholders. Social media yields tremendous power in peace processes as a tool for dialogue, debate, organization, and mobilization thereby adding more complexity by opening the peace process to public influence. Various indicators such as renunciation of violence during talks, establishing a negotiating agenda and its sequences, public support, and external guarantees can enable us to better understand peace process dynamics and predicting their outcomes. In this paper, we study two important indicators: inter-group polarization and public sentiment towards the Colombian peace process. We present a detailed linguistic analysis to detect inter-group polarization and understand differences in signals emerging from polarized groups. We also present a predictive model which leverages tweet-based, content-based and user-based features to predict public sentiment towards the Colombian peace process as observed through social media.

Influence Drives the Emergence and Growth of Social Networks

Social influence has been a widely accepted phenomenon in social networks for decades. This includes influence maximization, influence selection and quantification, and influence validation. Different from existing work, our research focuses on the effects of social influence on the evolution of social networks, aiming to answer that whether social influence is a strong force shaping the network dynamics.

Dynamics of Human Behavior

Users with demographic profiles in social networks offer the potential to understand the social principles that underpin our highly connected world, from individuals, to groups, to societies. In this article, we harness the power of network and data sciences to model the interplay between user demographics and social behavior and further study to what extent users’ demographic profiles can be inferred from their mobile communication patterns. Our work sheds light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.

Science of Science

Scientific impact plays a pivotal role in the evaluation of the output of scholars, departments, and institutions. Scientific researchers generate scientific impact through novel discoveries and developments, which are traditionally disseminated to a wider community via publications. The impact of each of these findings and corresponding publications—both to a field of research and, by extension, to the reputation of the author—can be affected by a variety of factors, which may be directly or indirectly related to the findings themselves. Our work addresses two analogous questions asked by many academic researchers: “How will my h-index evolve over time, and which of my previously and newly published papers will contribute to my future h-index?

Online Health and Wellness Information Consumption

Users are rapidly leveraging the Internet as a viable source of health information. In this research, we study the health-seeking behavior of users on a national health and wellness-based knowledge sharing online platform. We begin by identifying the topical interests of users from different content consumption sources. Using these topical preferences, we explore information consumption and health-seeking behavior across three contextual dimensions: user-based demographic attributes, time-related features, and community-based socioeconomic factors. We then study how these context signals can be used to infer specific user health topic preferences.