Biases and Accountability in Information Ecosystems

Digital Accountability
In the digital age, leaders often turn to online platforms to engage with the public. Their online presence can be powerful, inspiring positive change and rapid response during emergencies, but it can also be misused, potentially spreading misinformation or inciting violence. Recognizing the importance of understanding and ensuring responsible online engagement, this research aims to study the online behavior of over 10,000 U.S. officials across major platforms like X (formerly known as Twitter), YouTube, and Facebook. We will collect comprehensive data to analyze their interactions, responsiveness, and transparency, especially during critical times such as close elections or public crises. Our objective is to promote digital accountability among leaders and contribute to a healthier democracy. The work is supported by an NSF award.

The Normalization of Extremism and Communal Violence in Cyber-Social Space
Political extremism is posing a growing threat to national security and democracy around the world. This research project aims to investigate how modern democratic societies are exploited by extremist groups and individuals that make use of free speech and digital communications to promote illiberal ideas and target specific outgroups. The study will conduct a comparative case study of political extremist community formation and outgroup targeting in several democratic countries including Israel, Nigeria, France, and the United States. Using a mixed-methods approach that combines in-depth ethnography and quantitative analysis of social media sites, the research seeks to identify the relationships between online-offline hate-motivated speech acts, conspiracy theories, and instances of communal violence. This study will investigate how practices of normalizing hatred and group conflicts vary across cultural and sociopolitical contexts. The outcome of this research will provide a multi-scale understanding of hate transmissions and help identify the vulnerability of democracy at the local, national, and international levels through cross-country comparative studies. The study has significant implications for advancing U.S. and defense policy priorities. It will not only contribute to the understanding of how the impact of violent extremist organizations can be mitigated but also how extremist organizations can be neutralized in general. This work is supported by Minerva/ONR.

A Network Theory of Distrust
How do distrust and misinformation gain influence and affect society? There are inconsistencies between trust and influence, as evidenced by emerging opinion leaders who gain influence without fostering trust, despite the widespread belief that trust leads to increased trustworthiness and influence. The prevalence of misinformation and distrust has led to the rise of populism and the decline of democracy, implying that fostering distrust may be more effective than building trust. This project aims to develop a network theory to investigate how mistrust or distrust undermines trust and gains influence, as well as the relationship between individual choices and the collective diminishing of trust-influence. Through the development of a generative model, we capture and test trust-influence dynamics with both simulated and real-world datasets to offer a new theory and tools to predict group dynamics and conflicts in social networks. This work is supported by AFOSR.

Countering COVID-19 Misinformation
As the COVID-19 pandemic spreads, countries and cities around the globe have taken stringent measures including quarantine and regional lockdown. The increasing isolation, along with the panic and anxiety, creates challenges for countering misinformation -- people are increasingly tapping into online information sources already familiar to them with declining chances of accessing alternative stories. This project will develop mechanisms based on text and image analysis, social psychology, and crowd-sourcing that can be used to counter misinformation. We aim to contribute to the scientific understanding of misinformation in terms of which false information is most influential, who is most affected by it and how to "debunk" the problematic information automatically in social media. The work is supported by an NSF award.

Understanding Group Biases
The recent proliferation of digital human trace data and machine learning techniques together provide opportunities for revolutionizing the ways of understanding group biases. There have been works that apply techniques, ranging from text and network mining to deep learning, to study group biases; however, these existing works are mostly limited in the capacity of adding new qualitative understanding about a group, or lacking a rigorous, reproducible analysis procedure to verify and interpret the newly revealed patterns. In other words, the trade-off between qualitative, "thick" data and quantitative, "big" data has not been addressed. The goal of this project is to develop capabilities to capture cultural models that reflect different group biases at new speeds and scales. To this end, we propose to develop an automatic group bias analytic framework with two specific aims: (1) purposefully incorporate data and machine biases to characterize group biases, (2) allow for scalable and reproducible comparative analysis across groups. This project will have implications into building trust, and avoiding conflicts and misunderstandings within and across groups. The work is supported by the DARPA Understanding Group Biases (UGB) program

Disaster Analytics

Collective Sense Making of Terrorist Attacks
Social media has become central to the public's response to terrorism. From the transmission of breaking news, to the offering of social support, to the dissemination of radical, hateful messages, people increasingly turn to social media to both share and gain understandings of terrorist events. This project utilizes the social media data to investigate the reactions of individuals located in Paris during the November 2015 attacks. This research outcome will both improve responses to specific terrorist attacks as well as enhance public understanding of the specific means through which terrorism wields social influence. See the project page for more details. The work is in part supported by an NSF award.

Threat Perception Following Mass Violence Events
How does experiencing mass violence, terrorism, or other traumatic events shape individuals' perceive and respond to their social world? Anecdotally, following extensive media coverage of mass violence events, many report perceiving objects, people, and situations as particularly threatening; and, as media coverage shifts to emphasize resilience and community cohesion. In this project, we empirically study how emotionally potent media coverage of a real-world threat alters threat perception. This work could reveal potential harmful real-world consequences of emotionally potent media reporting of a terrorism event, and will also help characterize the types of individuals who are at greatest risk of altered threat perception after a mass violence or terrorism event. See the project page for more details. The work is in part supported by an NSF award.

Other Projects

Diffusion Analytics for Public Policy Research
Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. Policy diffusion has been a topic of focus across the social sciences for several decades, but due to limitations of data and computational technology, researchers have not taken a comprehensive look at patterns of diffusion across many different policies. In this project, we combine cutting-edge methods of text and network analysis to understand how policies, as represented in digitized text, spread through networks connecting the American states. The project results will help researchers, public officials, advocacy groups, and other relevant stakeholders understand how to balance the competing goals of innovation and consistency in public policy. See the project page for more details. The work is in part supported by an NSF award.

Pitt Smart Living: Building a Smart City Ecosystem
Urban planners have begun to realize that a truly sustainable transportation and urban environment in general, requires a shift to multimodal transportation. This project is a pilot study to evaluate the benefits of making real­time transportation information available to city­dwellers and also the potential impact of incentives as a way to encourage pro-­social behavior. In this project, we will develop, deploy, and evaluate techniques that will integrate information for increasing the utilization and quality of public transportation. See the project website for more details. The work is in part supported by an NSF award.

Collective Behavior During Emergencies and Other Exogenous Events
Large-scale emergencies, including natural disasters, and terrorist attacks, are now among the largest threats to national security. There is an indisputably increasing need for new tools to strengthen disaster resilience at all levels of society. We have worked on issues and challenges relating to emergency understanding using large-scale data generated by hundreds of millions of users. Related resesarch venue: 2014 KDD Workshop on Learning about Emergencies from Social Information (KDD-LESI 2014). Related projects: rising stars (PLoS ONE), big bird (ICWSM), voice of victory (WWW), ripple (EPJ Data Science).

Scholarly Big Data
Academics and researchers worldwide continue to produce large numbers of scholarly documents including papers, books, technical reports, etc. and associated data such as tutorials, proposals, lab note books, and course materials. The abundance of data sources enables researchers to study scholarly collaboration at a very large scale. We have taken the lead on developing research methods and techniques for scholarly knowledge discovery. Related research venues: International Workshop on Collaborative Big Data (C-Big 2014), International Workshop on Challenges & Issues on Scholarly Big Data Discovery and Collaboration (SBD 2014). Related projects: ContexTour (SDM), progressive group mining (forthcoming), Twitter in academia (HyperText).

Urban and Local Community Analytics
In everyday urban life, individuals interact with each other not only through face-to-face meetings, but increasingly through mobile communication devices and Internet-based activities like emails and social media (e.g., Facebook and Twitter). Those types of technologically mediated communications leave fine-grained "digital traces" about when, where, and what one talks about and to whom, offering an unprecedented opportunity to examine the structure and dynamics of social and information behavior. We have taken the initiative to examine the social-spatial phenomena in a data-driven manner, and to connect the socio-spatial big data with the profound neighborhood effect observed in the social science literature. Related projects: habitat (PLoS ONE), sentiment segregation (SocialCom), sustainable local forums (forthcoming: HICSS). The work is in part supported by University of Pittsburgh Central Research Development Fund (CRDF): "Assessing the 'Neighborhood Effect' With Social Media Traces" (PI: Yu-Ru Lin).

Visual Data Mining
Data visualization has been increasingly important in this "big data" world. In our school, we have worked on new visualization techniques that enable different stakeholders to synthesize information and derive insight from massive, dynamic, multi-sourced, multi-faced, interrelated and sometimes conflicting data, and provide timely assessment for making decisions. With the explosion of different types of data generated from multiple sources, ranging from research groups, government agencies to participatory sensing data collected through social media and mobile devices, it is important to provide intelligent interface for all stakeholders in order to support actionable information seeking, particularly for supporting collective sense-making, situational awareness, and dissemination of credible crisis-relevant information. Related projects: Whisper (TVCG), FacetAtlas (TVCG), ContexTour (SDM), FluxFlow (TVCG), UnTangle (ICDM), SocialHelix (JoV).

Health Informatics via Crowdsourcing and Social Media
With the recent advances in Internet-based communication technologies, online social sites have become a prevailing cyberspace for people to seek health information or related supports. Our research uniquely leverages crowdsourced content analysis and machine learning techniques in developing new tools for users to express complex health-related questions or needs by including more personalized or contextual informations beyond simple queries. Related projects: health-related question intent characterization (forthcoming), health message diffusion (forthcoming).

Data Science, Network Science, Computational Social Science, Data Visualization

network metrics, graph analysis, data mining (esp. graph mining), visualization and visual analytics

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