Tag Archives: news

Two papers got accepted for ACM WebSci’23

Two of our latest works got accepted for the 15th ACM Web Science Conference (WebSci’23)!

Web Science is an interdisciplinary field to study socio-technical systems, particularly on the web, and ACM WebSci is the premier conference for Web Science research.

Political Honeymoon Effect on Social Media: Characterizing Social Media Reaction to the Changes of Prime Minister in Japan” by Kunihiro Miyazaki (The University of Tokyo, postdoctoral researcher at CNetS from March 1), Taichi Murayama (Osaka University), Akira Matsui (Yokohama National University), Masaru Nishikawa (Tsuda University), Takayuki Uchiba (Sugakubunka), Haewoon Kwak (Associate Professor of Informatics at IU Luddy), and Jisun An (Assistant Professor of Informatics at IU Luddy)

In this study, we examine how social media users respond to changes in political leadership to understand the honeymoon effect in politics better. In particular, we constructed a 15-year Twitter dataset on eight change timings of Japanese prime ministers consisting of 6.6M tweets and analyzed them in terms of sentiments, topics, and users.

Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020” by Hong Zhang (Singapore Management University), Haewoon Kwak (Associate Professor of Informatics at IU Luddy), Wei Gao (Singapore Management University), and Jisun An (Assistant Professor of Informatics at IU Luddy).

In this work, we look into an individual’s stance on three seemingly independent but related controversial topics: wearing masks, racial equality, and Donald Trump. These topics correspond to one’s behavior in three events that happened in 2020: mask adoption, racial unrest, and US Election. Our goal is to investigate how one’s behavior in a target event is associated with their behaviors in other related events, which we call connected behavior, beyond the context of COVID-19, and ultimately, predict one’s future behavior given their previous behaviors.

The conference will be Co-located with The Web Conference, Austin, Texas, USA, from April 30th to May 1st.

Congratulations to Dr. Rion Brattig Correia!

Luis Rocha and Rion Brattig Correia

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 Dr. Dimitar Nikolov

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.