Congratulations to Rion Correia, who successfully defended his PhD dissertation on Prediction of Drug Interaction and Adverse Reactions, with data from Electronic Health Records, Clinical Reporting, Scientific Literature, and Social Media, using Complexity Science Methods. Dr. Correia’s research used network science, machine learning, and data science to uncover population-level associations of drugs and symptoms, useful for public health surveillance. His findings show that Social Media (Instagram and Twitter) and Electronic Health Records of an entire city in Southern Brazil, are very useful to reveal how the Drug interaction phenomenon varies across distinct groups. For instance, he identifying gender biases and specific communities of interest in chronic disease (e.g. Epilepsy and Depression). In addition to Complex Networks and Systems, his dissertation contributes to the fields of biomedical informatics and precision public health by leveraging heterogeneous data sources at multiple levels to understand population and individual pharmacology differences and other public health problems.
Congratulations to Dimitar Nikolov, who successfully defended his PhD dissertation on Information Exposure Biases in Online Behaviors. Dr. Nikolov’s research explored the unintentional biases introduced by filtering, ranking, and recommendation algorithms that mediate our online consumption of information. His findings show that our reliance on modern online technologies limits exposure to diverse points of view and makes us vulnerable to misinformation. In particular, he analyzed two massive Web traffic datasets to quantify the popularity and homogeneity bias of several popular online platforms including social media, email, personalized news, and search engines. He also leveraged Twitter data to characterize the link between political partisanship and vulnerability to online pollution, such as fake news, conspiracy theories, and junk science. His dissertation contributes to the field of computational social science by putting the study of bias in information consumption and derived phenomena like political polarization, echo chambers, and online pollution on a more firm quantitative foundation.
Luis Rocha, Katy Borner, Paul Macklin and other faculty from the School of Informatics, Computing and Engineering (SICE) were the awardees of the 2019 SICE Research Awards. Luis Rocha received the award in recognition of the NSF Research Traineeship (NRT) on Complex Networks and Systems and two NIH NLM R01 grants. The awards were handed by SICE Dean Raj Acharya and Associate Dean for Research Kay Connelly.
Alexander T. J. Barron, a PhD candidate in CNetS, and co-authors are recipients of the 2018 Cozzarelli Prize in Behavioral and Social Sciences for their paper, Individuals, institutions, and innovation in the debates of the French Revolution. Every year, six of these awards are given to PNAS publications according to their “outstanding scientific quality and originality.” Each of the papers selected were chosen from the more than 3,200 research articles that appeared in PNAS during the last year and represent the six broadly defined classes under which the National Academy of Sciences is organized. The paper is the product of an interdisciplinary research team across several universities: Alexander Barron (Informatics, IU), Simon DeDeo (Social and Decision Sciences, Carnegie Mellon and the Santa Fe Institute), Rebecca Spang (History, IU), and Jenny Huang (soon to be attending Oxford).Continue reading SICE/CNS PNAS article winning Cozzarelli prize
On the 7th of March 2019, CNETS Professor Luis Rocha will participate in a panel organized by Nova SBE’s Executive Education, Instituto Gulbenkian da Ciência and ISI Foundation with the theme of AI, society and organisations: experiences from applied projects in governments, companies and NGO’s, where the role of data science in today’s world will be discussed.
Other guest speakers, include Rayid Ghani, director of the Center for Data Science and Public Policy in the University of Chicago, founder of the Data Science for Social Good fellowship and Chief Scientist at the Obama for America 2012, Daniela Paolotti, Ciro Cattuto, Joana Gonçalves-Sá and Leid Zejnilovic.
CNetS Professor Luis Rocha, together with ISI foundation (ISI) scientist Ciro Cattuto organize a workshop with the Instituto Gulbenkian de Ciencia to explore synergies between data/computational science and the life, health and social sciences. More information on the workshop event page.Continue reading Charting interdisciplinary research opportunities between data and life sciences
Filippo Menczer, a professor of computer science and informatics at CNetS, appeared on a panel of experts to discuss the emergence and dissemination of misinformation, and how it threatens society at the annual meeting of American Association for the Advancement of Science in Washington, D.C., Feb. 15.
Menczer was a part of a three-person panel and presented a talk, “Eight Ways Social Media Makes Society Vulnerable to Misinformation.” The talk provided an overview of ongoing network analytics, modeling, and machine learning efforts to study the viral spread of misinformation and to develop tools for countering the online manipulation of opinions. Menczer has previously developed systems such as Botometer, which detects social media bots, and Hoaxy, which maps the diffusion of low-credibility content.Continue reading CNeTS member provides expertise on misinformation battle at AAAS conference
Your mother always told you that if something was bothering you, you should talk about it. It would make you feel better. Turns out she was right, and researchers at the School of Informatics, Computing, and Engineering have the science to prove it. Johan Bollen, a professor of informatics and computing, leads a team that analyzed the Twitter feeds of tens of thousands of users to study how emotions change before and after they were explicitly stated. In the study, “The minute-scale dynamics of online emotions reveal the effects of affect labeling,” published in the journal Nature Human Behaviour, Bollen and his colleagues used algorithms to measure how the positivity or negativity of tweets change before or after a user explicitly expressed having an emotion, e.g. saying “I feel bad” or “I feel good.” Their study not only reveals how emotions evolve over time, but also how their expression may change them, and how these changes differ between men and women.Continue reading Bollen social media study shows how affect labeling can help moderate emotions
Analysis by CNetS researchers of information shared on Twitter during the 2016 U.S. presidential election has found that social bots played a disproportionate role in spreading misinformation online. The study, published in the journal Nature Communications, analyzed 14 million messages and 400,000 articles shared on Twitter between May 2016 and March 2017 — a period that spans the end of the 2016 presidential primaries and the presidential inauguration on Jan. 20, 2017. Among the findings: A mere 6 percent of Twitter accounts that the study identified as bots were enough to spread 31 percent of the low-credibility information on the network. These accounts were also responsible for 34 percent of all articles shared from low-credibility sources. The study also found that bots played a major role promoting low-credibility content in the first few moments before a story goes viral. Continue reading Twitter bots play disproportionate role spreading misinformation
The National Institutes of Health, under the National Library of Medicine’s program on data science research, awarded a $1.55 million grant to an interdisciplinary team lead by Luis Rocha, a professor of informatics, member of CNETS and the director of the NSF-NRT complex networks & systems program at the School of Informatics, Computing, and Engineering. The four-year project, a collaboration between SICE and the Indiana University School of Nursing, will employ innovative data- and network-science methods to produce myAURA, an easy-to-use web service for epilepsy patients. myAURA will be based on a large-scale epilepsy knowledge graph built by integrating data from social media, electronic health records, patient discussion boards, scientific literature databases, advocacy websites, and mobile app data. The knowledge graph will, in turn, be used to fuel recommendation and visualization algorithms based on the automatic inference of relevant associations. The inference will follow algorithms developed by Rocha’s team to remove redundancy and extract factual information from large knowledge graphs as well as parsimonious network visualizations developed by Katy Börner, Distinguished Professor of Engineering & Information Science at SICE. Continue reading Team led by Luis Rocha awarded NIH grant to improve chronic-disease management with Data and Network Science