We are looking for a postdoctoral fellow to join the Observatory on Social Media (OSoMe, pronounced awe•some) at Indiana University Bloomington (IUB) under the direction of Filippo Menczer. The research will address the intersection of computing, network, data, and media sciences with a focus on (mis/dis)information diffusion and the detection and countering of online manipulation.Continue reading OSoMe PostDoc Wanted
In the groundbreaking new PBS series “NetWorld,” Niall Ferguson visits network theorists, social scientists and data analysts (including at CNetS!) to explore the intersection of social media, technology and the spread of cultural movements. Reviewing classic experiments and cutting-edge research, NetWorld demonstrates how human behavior, disruptive technology and profit can energize ideas and communication, ultimately changing the world.
First announced in September 2019, the new BotSlayer software to expose disinformation networks is designed and developed by CNetS faculty and students in collaboration with IUNI staff and the Observatory on Social Media. BotSlayer is an application that helps track and detect potential manipulation of information spreading on Twitter. It can be used by journalists, researchers, civil society organizations, corporations, and political candidates to discover in real-time new coordinated disinformation campaigns. Read about how you can join the effort to spot the manipulation of social media.
Indiana University will establish a $6 million research center to study the role of media and technology in society. With leadership by CNetS faculty, the Observatory on Social Media will investigate how information and misinformation spread online. It will also provide students, journalists and citizens with resources, data and training to identify and counter attempts to intentionally manipulate public opinion. Major support for the center comes from the John S. and James L. Knight Foundation, which will contribute $3 million, as well as funds from the university. The center is a collaboration between the IU School of Informatics, Computing and Engineering, The Media School and the IU Network Science Institute. More…
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.
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 CNetS social media study shows how affect labeling can help moderate emotions
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…