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.
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
Onur Varol, a postdoctoral research associate at Northeastern University who earned his Ph.D. in Informatics from CNetS, has been honored with the University Distinguished Ph.D. Dissertation Award for 2018, which is the highest honor for research Indiana University bestows on its graduate students. “I am extremely happy to receive this award,” Varol said. “I would like to especially thank my advisor, Filippo Menczer, and the Informatics department for nominating me. I was lucky to be surrounded by the best advisors, collaborators, and research group I could imagine during my doctoral studies, and I am a proud IU alumni and a Hoosier.” Varol’s dissertation, “Analyzing Social Big Data to Study Online Discourse and Its Manipulation,” provided insights into analysis of online conversations and mechanisms used for their manipulation. Varol built machine learning frameworks like Botometer to detect social bots. More…
First global analysis of human birth-rate cycles reveals that post-holiday ‘baby boom’ persists across cultures, hemispheres. CNetS PhD student Ian Wood and Professors Luis Rocha and Johan Bollen, in collaboration with Joana Sá, used data science and computational social science methods to demonstrate that “Human Sexual Cycles are Driven by Culture and Match Collective Moods.” See full article at IU News and media coverage in many venues such as The Independent, Time, Newsweek, Publico, ScienceDaily, Phys.org, The National Post, DailyMail, The Hindustan Times, Men’s Fitness, Mother Jones, Drive with Yasmeen Khan (at 17:30) (audio of interview), etc. Discussion of the paper was a top trending topic on Reddit. Watch a short video about the research.
Thanks to support from the Indiana University Network Science Institute (IUNI) and Digital Science Center (DSC), the full content of the Twitter data repository from the Observatory on Social Media (OSoMe) is now available to all IU researchers. Many tools to detect social bots, study the spread of fake news, visualize meme diffusion networks, trends, and maps, as well as APIs to access this data, have been available to the general public since mid-2016. Now, however, the IU research community can access enhanced data and content from the large collection, based on a 10% sample of all public tweets. A dedicated portal allows IU faculty and students to submit queries to the OSoMe cluster based on hashtags, URLs, keywords, geo-coordinates, and other criteria. At any time the system can search and retrieve data from the previous 18 months. We hope this resource will spur and support new research projects in all areas of computing, natural, and social sciences. Click here to read how to get access and learn more about the data, or attend our Open Science Forum!
Fil Menczer, professor of computer science and informatics at the School of Informatics, Computing, and Engineering, is part of a group that has been awarded a $1.2 million grant from the Defense Advanced Research Projects Agency (DARPA) to study how and at what rate information spreads in a global information environment. The project, COSINE: Cognitive Online Simulation of Information Network Environments, also involves Professor of Informatics Alessandro Flammini and Assistant Professor of Informatics and Computing YY Ahn. The project is in collaboration with colleagues at the USC Information Sciences Institute (ISI) and the University of Notre Dame. Read more…
JAN 2018 UPDATE: THIS SEARCH HAS BEEN CLOSED.
The Center for Complex Networks and Systems Research (cnets.indiana.edu) at Indiana University, Bloomington has an open postdoctoral position to study how information spreads through complex online social networks. The position funded by the DARPA program on Computational Simulation of Online Social Behavior (SocialSim). The anticipated start date for this position is January 1, 2018 (negotiable). This is an annual renewable appointment for up to 3 years subject to performance and funding. Continue reading Postdoctoral Fellowship: Simulation of Information Diffusion in Online Social Networks
A project from NaN and IUNI was among 20 selected (out of over 800 applications) to address the spread of misinformation with support from the Knight Prototype Fund. Led by Fil Menczer, Giovanni Ciampaglia, Alessandro Flammini and Val Pentchev, the project will integrate the Hoaxy and Botometer tools and uncover attempts to use Internet bots to boost the spread of misinformation and shape public opinion. The tool aims to reveal how this information is generated and broadcasted, how it becomes viral, its overall reach, and how it competes with accurate information for placement on user feeds. The project will be supported by the Democracy Fund, which in March, along with partners Knight Foundation and Rita Allen Foundation, launched an open call for ideas around the question: How might we improve the flow of accurate information? The call sought projects that could be quickly built to respond to the challenges affecting the health of our news ecosystem and ultimately our democracy. The winning projects will receive a share of $1 million through the Knight Prototype Fund, a program focused on human-centered approaches to solving difficult problems.