UPDATE: This paper is ranked #3 most read among all articles published by Nature Communications in 2018
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→
Congratulations to Clayton A. Davis, who successfully defended his PhD dissertation titled “Collect, Count, and Compare”: Expanding Access and Scope of Social Media Analysis. Dr. Davis’ work explored ways to facilitate research using massive social data through tools that are friendly for non-technical users, robust to manipulation by social bots, and that offer strict anonymity guarantees. His work has been featured on the cover of Communications of the ACM and quoted in top worldwide media venues. Web interfaces for his projects, including Botometer, Kinsey Reporter, and the Observatory on Social Media, have served millions of queries to thousands of Internet users. Davis has also made key pedagogical contributions, and co-authored a textbook on network science to be published later this year by Cambridge University Press.
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.
A new paper published in Nature Reviews Physics by Professor Santo Fortunato and colleagues from Northwestern University features a detailed analysis of the careers of Nobel Prize Laureates. They found that the prize- winning works in the three main science categories (physics, chemistry and medicine) tend to occur early in the career of the Laureate.
This may be due to a selection effect — because the Nobel Prize in science has never been awarded posthumously, those who produced groundbreaking works early on in their careers were more likely to wait long enough to be recognized. Also, award-winning papers tend to be produced by small teams, on average. Apart from the prize-winning work, which may be subject to peculiarities of the Nobel, there is no known major difference that distinguishes patterns governing the careers of scientific elites from those of ordinary scientists.
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).
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.
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.
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.