About

Louiqa Raschid is the Dean’s Professor of Information Systems at the Robert H. Smith School of Business and holds a joint appointment with the Institute for Advanced Computer Studies and the Department of Computer Science at the University of Maryland in College Park. She received a Bachelor of Technology in electrical engineering from the Indian Institute of Technology, Madras, in 1980, and a Ph.D. in electrical engineering from the University of Florida, Gainesville, in 1987. Her research focuses on solving challenges related to data management, data integration, and performance for applications across various domains including the life sciences, Web data delivery, health information, financial information systems, humanitarian IT applications, and Grid computing. She is an expert in optimization, large-scale simulation, modeling, semantics, logic-based reasoning, and data analysis techniques. Raschid has made significant contributions through her publications in leading conferences and journals, and her research has received multiple awards and over 25 grants from agencies such as NSF and DARPA. She has organized working groups on information mediation and biological data management for NIH and DARPA, and is leading efforts on next-generation financial cyberinfrastructure sponsored by NSF and the Computing Research Association. Raschid has mentored over 30 Ph.D. and post-doctoral researchers, including many women and minority students, and has over 100 co-authors or co-editors. She played a key role in the Sahana FOSS project for disaster information management, serving as database architect, member of the Sahana Software Foundation, and Founding Chair of the Sahana Board. She has been a Distinguished Scientist of the ACM since 2008.

Research topics

• Computer science
• Information retrieval
• Database
• Data science
• Data mining

Selected publications

• A Flexible and Extensible Contract Aggregation Framework for Financial Data Stream Analytics

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study

  Journal of Medical Internet Research · 2024-12-10 · 7 citations

  articleOpen access

  BACKGROUND: Effective communication is crucial during health crises, and social media has become a prominent platform for public health experts (PHEs) to share information and engage with the public. At the same time, social media also provides a platform for pseudoexperts who may spread contrarian views. Despite the importance of social media, key elements of communication, such as the use of moral or emotional language and messaging strategy, particularly during the emergency phase of the COVID-19 pandemic, have not been explored. OBJECTIVE: This study aimed to analyze how PHEs and pseudoexperts communicated with the public during the emergency phase of the COVID-19 pandemic. We focused on the emotional and moral language used in their messages on various COVID-19 pandemic-related topics. We also analyzed their interactions with political elites and the public's engagement with PHEs to gain a deeper understanding of their influence on public discourse. METHODS: For this observational study, we gathered a dataset of >539,000 original posts or reposts from 489 PHEs and 356 pseudoexperts on Twitter (subsequently rebranded X) from January 2020 to January 2021, along with the replies to the original posts from the PHEs. We identified the key issues that PHEs and pseudoexperts prioritized. We also determined the emotional and moral language in both the original posts and the replies. This allows us to characterize priorities for PHEs and pseudoexperts as well as differences in messaging strategy between these 2 groups. We also evaluated the influence of PHEs' language and strategy on the public response. RESULTS: Our analyses revealed that PHEs focused more on masking, health care, education, and vaccines, whereas pseudoexperts discussed therapeutics and lockdowns more frequently (P<.001). PHEs typically used positive emotional language across all issues (P<.001), expressing optimism and joy. Pseudoexperts often used negative emotions of pessimism and disgust, while limiting positive emotional language to origins and therapeutics (P<.001). Along the dimensions of moral language, PHEs and pseudoexperts differed on care versus harm and authority versus subversion across different issues. Negative emotional and moral language tends to boost engagement in COVID-19 discussions across all issues. However, the use of positive language by PHEs increases the use of positive language in the public responses. PHEs act as liberal partisans: they express more positive affect in their posts directed at liberals and more negative affect in their posts directed at conservative elites. In contrast, pseudoexperts act as conservative partisans. These results provide nuanced insights into the elements that have polarized the COVID-19 discourse. CONCLUSIONS: Understanding the nature of the public response to PHEs' messages on social media is essential for refining communication strategies during health crises. Our findings underscore the importance of using moral-emotional language strategically to reduce polarization and build trust.

  PublisherDOI

• #EpiTwitter: Public Health Messaging During the COVID-19 Pandemic

  arXiv (Cornell University) · 2024-06-04

  preprintOpen access

  Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its importance, the role of emotional and moral language in PHEs' communication during COVID-19 remains under explored. This study examines how PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing on emotional and moral language and their engagement with political elites. Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to January 2021, alongside public responses, we identified key priorities and differences in messaging strategy. PHEs prioritize masking, healthcare, education, and vaccines, using positive emotional language like optimism. In contrast, pseudo-experts discuss therapeutics and lockdowns more frequently, employing negative emotions like pessimism and disgust. Negative emotional and moral language tends to drive engagement, but positive language from PHEs fosters positivity in public responses. PHEs exhibit liberal partisanship, expressing more positivity towards liberals and negativity towards conservative elites, while pseudo-experts show conservative partisanship. These findings shed light on the polarization of COVID-19 discourse and underscore the importance of strategic use of emotional and moral language by experts to mitigate polarization and enhance public trust.

  PublisherOA PDFDOI

• Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19

  arXiv (Cornell University) · 2024-05-28

  preprintOpen access

  Social media platforms like Twitter (now X) have been pivotal in information dissemination and public engagement. The objective of our research is to analyze the effect of localized engagement on social media conversations. This study examines the impact of geographic co-location, as a proxy for localized engagement. Our research is grounded in a COVID-19 dataset. A key goal during the pandemic for public health experts was to encourage prosocial behavior that could impact local outcomes such as masking and social distancing. Given the importance of local news and guidance during COVID-19, we analyze the effect of localized engagement, between public health experts (PHEs) and the public, on social media. We analyze a Twitter Conversation dataset from January 2020 to November 2021, comprising over 19 K tweets from nearly five hundred PHEs, and 800 K replies from 350 K participants. We use a Poisson regression model to show that geo-co-location is indeed associated with higher engagement. Lexical features associated with emotion and personal experiences were more common in geo-co-located conversations. To complement our statistical analysis, we also applied a large language model (LLM)-based method to automatically generate and evaluate hypotheses; the LLM results confirm the results using lexical features. This research provides insights into how geographic co-location influences social media engagement and can inform strategies to improve public health messaging.

  PublisherOA PDFDOI

• Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study (Preprint)

  2024-07-06

  preprintOpen access

  <sec> <title>BACKGROUND</title> Effective communication is crucial during health crises, and social media has become a prominent platform for public health experts (PHEs) to share information and engage with the public. At the same time, social media also provides a platform for pseudoexperts who may spread contrarian views. Despite the importance of social media, key elements of communication, such as the use of moral or emotional language and messaging strategy, particularly during the emergency phase of the COVID-19 pandemic, have not been explored. </sec> <sec> <title>OBJECTIVE</title> This study aimed to analyze how PHEs and pseudoexperts communicated with the public during the emergency phase of the COVID-19 pandemic. We focused on the emotional and moral language used in their messages on various COVID-19 pandemic–related topics. We also analyzed their interactions with political elites and the public’s engagement with PHEs to gain a deeper understanding of their influence on public discourse. </sec> <sec> <title>METHODS</title> For this observational study, we gathered a dataset of &amp;gt;539,000 original posts or reposts from 489 PHEs and 356 pseudoexperts on Twitter (subsequently rebranded X) from January 2020 to January 2021, along with the replies to the original posts from the PHEs. We identified the key issues that PHEs and pseudoexperts prioritized. We also determined the emotional and moral language in both the original posts and the replies. This allows us to characterize priorities for PHEs and pseudoexperts as well as differences in messaging strategy between these 2 groups. We also evaluated the influence of PHEs’ language and strategy on the public response. </sec> <sec> <title>RESULTS</title> Our analyses revealed that PHEs focused more on masking, health care, education, and vaccines, whereas pseudoexperts discussed therapeutics and lockdowns more frequently (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001). PHEs typically used positive emotional language across all issues (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001), expressing optimism and joy. Pseudoexperts often used negative emotions of pessimism and disgust, while limiting positive emotional language to origins and therapeutics (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001). Along the dimensions of moral language, PHEs and pseudoexperts differed on care versus harm and authority versus subversion across different issues. Negative emotional and moral language tends to boost engagement in COVID-19 discussions across all issues. However, the use of positive language by PHEs increases the use of positive language in the public responses. PHEs act as liberal partisans: they express more positive affect in their posts directed at liberals and more negative affect in their posts directed at conservative elites. In contrast, pseudoexperts act as conservative partisans. These results provide nuanced insights into the elements that have polarized the COVID-19 discourse. </sec> <sec> <title>CONCLUSIONS</title> Understanding the nature of the public response to PHEs’ messages on social media is essential for refining communication strategies during health crises. Our findings underscore the importance of using moral-emotional language strategically to reduce polarization and build trust. </sec>

  PublisherDOI

• Modeling Financial Products and Their Supply Chains

  INFORMS Journal on Data Science · 2023-10-01

  articleSenior author

  The objective of this paper is to explore how novel financial datasets and machine learning methods can be applied to model and understand financial products. We focus on residential mortgage backed securities, resMBS, which were at the heart of the 2008 US financial crisis. These securities are contained within a prospectus and have a complex waterfall payoff structure. Multiple financial institutions form a supply chain to create the prospectuses. To model this supply chain, we use unsupervised probabilistic methods, particularly dynamic topics models (DTM), to extract a set of features reflecting community (topic) formation and temporal evolution along the chain. We then provide insight into the performance of the resMBS securities and the impact of the supply chain communities through a series of increasingly comprehensive models. First, models at the security level directly identify salient features of resMBS securities that impact their performance. We then extend the model to include prospectus level features and demonstrate that the composition of the prospectus is significant. Our model also shows that communities along the supply chain that are associated with the generation of the prospectuses and securities have an impact on performance. We are the first to show that toxic communities that are closely linked to financial institutions that played a key role in the subprime crisis can increase the risk of failure of resMBS securities. History: Olivia Sheng served as the senior editor for this article. Funding: This research was partially supported by National Science Foundation [Grant CNS1305368] and National Institute of Standards and Technology [Grant 70NANB15H194]. Data Ethics &amp; Reproducibility Note: No data ethics considerations are foreseen related to this article. The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.8845455.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2020.0006 ).

  PublisherDOI

• Framework to Study Migration Decisions Using Call Detail Record (CDR) Data

  IEEE Transactions on Computational Social Systems · 2022-06-08 · 2 citations

  articleOpen accessSenior author

  This article addresses the challenges of using call detail record (CDR) data to study migration. Repurposing CDR data for this task have many advantages, including the lower costs of data collection and the potential for contemporaneous analysis. We present a framework for the repurposing and analysis of CDR data. We identify the home location of a subscriber, with corresponding confidence measures, and determine if the subscriber is a definite migrant, likely migrant, likely nonmigrant, or definite nonmigrant. A predictive model then uses mobility and social network features, extracted from the CDR data, to predict the individual decision to migrate. We are the first to address the challenging task of predicting the migration decision at the individual level. We also provide insight into features that can have an impact on the decision to migrate. An in-depth evaluation using CDR data from two provinces in Sri Lanka provides a granular map of migrant inflow and outflow. The success of our prediction model and the insights gained from the evaluation prepare the way for the repurposing of CDR data for social good with a focus on migration.

  PublisherOA PDFDOI

• Future Link Prediction in the Blogosphere for Recommendation

  Proceedings of the International AAAI Conference on Web and Social Media · 2021-08-03 · 5 citations

  articleOpen access

  The phenomenal growth in both scale and importance of social media such as blogs, micro-blogs and user-generated content, has created a need for tools that monitor information diffusion and make recommendations within these platforms. An essential element of social media, particularly blogs, is the hyperlink graph that connects various pieces of content. There are two types of links within the blogosphere; one from blog post to blog post, and another from blog post to blog channel (an event stream of blog posts). These links can be viewed as a proxy for the flow of information between blog channels and to reflect influence. Given this assumption about links, the ability to predict future links can facilitate the monitoring of information diffusion, making recommendations, and word-of-mouth (WOM) marketing. We propose different methods for link predictions and we evaluate these methods on an extensive blog dataset.

  PublisherOA PDFDOI

• Modeling Complex Financial Products.

  arXiv (Cornell University) · 2021-02-03

  preprintOpen accessSenior author

  The objective of this paper is to explore how financial big data and machine learning methods can be applied to model and understand complex financial products. We focus on residential mortgage backed securities, resMBS, that were at the heart of the 2008 US financial crisis. The securities are contained within a prospectus and have a complex payoff structure. Multiple financial institutions form a supply chain to create the prospectuses. We provide insight into the performance of the resMBS securities through a series of increasingly complex models. First, models at the security level directly identify salient features of resMBS securities that impact their performance. Second, we extend the model to include prospectus level features. We are the first to demonstrate that the composition of the prospectus is associated with the performance of securities. Finally, to develop a deeper understanding of the role of the supply chain, we use unsupervised probabilistic methods, in particular, dynamic topics models (DTM), to understand community formation and temporal evolution along the chain. A comprehensive model provides insight into the impact of DTM communities on the issuance and evolution of prospectuses, and eventually the performance of resMBS securities.

  Publisher

• Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets

  arXiv (Cornell University) · 2021-03-12

  preprintOpen accessSenior author

  Trading in Over-The-Counter (OTC) markets is facilitated by broker-dealers, in comparison to public exchanges, e.g., the New York Stock Exchange (NYSE). Dealers play an important role in stabilizing prices and providing liquidity in OTC markets. We apply machine learning methods to model and predict the trading behavior of OTC dealers for US corporate bonds. We create sequences of daily historical transaction reports for each dealer over a vocabulary of US corporate bonds. Using this history of dealer activity, we predict the future trading decisions of the dealer. We consider a range of neural network-based prediction models. We propose an extension, the Pointwise-Product ReZero (PPRZ) Transformer model, and demonstrate the improved performance of our model. We show that individual history provides the best predictive model for the most active dealers. For less active dealers, a collective model provides improved performance. Further, clustering dealers based on their similarity can improve performance. Finally, prediction accuracy varies based on the activity level of both the bond and the dealer.

  PublisherOA PDFDOI

Recent grants

• COLLABORATIVE: ABI Development: Methodology for Pattern Creation, Imprint Validation, and Discovery from the Annotated Biological Web

  NSF · $777k · 2012–2018

• Collaborative Proposal: II+SEI Mediation Technology for Biological Pipeline Analysis

  NSF · $782k · 2005–2009

• COLLABORATIVE PROPOSAL: Semantic Map of Biological Data Sources: Entity Identity and Path Characterization

  NSF · $225k · 2003–2007

• CI-P: Developing the Next Generation of Community Financial CyberInfrastructure for Monitoring and Modeling Financial Eco-Systems and for Managing Systemic Risk

  NSF · $100k · 2013–2015

• III EAGER Collaborative Research: Exploratory Research on the Annotated Biological Web

  NSF · $131k · 2009–2013

Frequent coauthors

• María-Esther Vidal

  Leibniz University Hannover

  39 shared

• Avigdor Gal

  Technion – Israel Institute of Technology

  19 shared

• Zoé Lacroix

  17 shared

• Laura F. Bright

  15 shared

• Vladimir Zadorozhny

  University of Pittsburgh

  15 shared

• Guillermo Palma

  12 shared

• Shanchan Wu

  12 shared

• Timos Sellis

  Athena Research and Innovation Center In Information Communication & Knowledge Technologies

  12 shared

Awards & honors

• Distinguished Scientist of the ACM (2008)
• Founding Chair of the Sahana Board (2007-2009)