When: April 03, 2023 12pm EST

Where: Luddy Center for Artificial Intelligence (LU2005)

Speaker: Ankur Mani

Title: Investigating Churn in Online Wellness Programs: Evidence from a U.S. Online Social Network

Abstract:

Online wellness activity platforms increasingly utilize wellness programs and social support to motivate healthy activities and improve user engagement. However, many wellness programs suffer from high churn rates that discount their expected efficacy, and negative social influence may lead to a churn contagion that amplifies the churn speed and scale. Hence, a need arises to understand why users churn wellness programs and how social contagion contributes to the churns. Leveraging the exercise challenge setting, the exercise data, and a large social network on a renowned U.S. online fitness platform, we investigate the effect of peers’ behavior in exercise challenge churn on ego. To achieve the research goal, we employ an instrumental variable framework (shown in the figure below), using the exogenous variation of peers’ weather in locations that differ from the ego’s location as instruments. The framework untangles the endogeneity of the estimated effect using variations created by peers’ weather as a shock to the ego’s churn. We measure churn as a decision an ego makes after being inactive for one to two weeks and define peers as the ones an ego follows on the platform. We find that exercise challenge churn is socially contagious and demonstrates a complex contagion. Interestingly, our analyses reveal that the social contagion of churn diffuses from the sub-central or peripheral egos with fewer friends in the social network to central egos with more friends in the social network. Such churn contagion is mostly confined to low-density network communities with members who are poorly connected. Our findings have important implications for designing intervention plans to stop online wellness program churn based on social contagion.

This is joint work with my student Yi Zhu.

Biography:

Ankur Mani is an assistant professor in the Industrial and Systems Engineering department at the University of Minnesota. He is also an affiliate of the Data Science Initiative and Control Systems and Dynamics Group at the University of Minnesota. He received his Ph.D. from the Massachusetts Institute of Technology and B.Tech. degree from the Indian Institute of Technology, Delhi. Ankur’s research interests include networks, distributed experimentation, and game theory with applications in social networks, supply chain networks, transportation networks and health care. His research has been published in major journals including Management Science and Nature Human Behavior among others and has received recognitions from the INFORMS revenue management section, INFORMS aviation section, and POMS, among others.

When: November 11, 2022 12pm EST

Where: Remote Talk

Zoom link: https://iu.zoom.us/j/89873992894?pwd=QmxmaXZaakNFSUpnQm1jT1I3T1ZOZz09

Recording: Jonas Juul Cascades Talk

Speaker: Jonas Juul

Title: Harder, better, faster, stronger cascades — or simply larger?

Abstract:

Do some types of online content spread faster or further than others? In recent years, many studies have sought answers to such questions by comparing statistical properties of network paths taken by different kinds of content diffusing online. Here we demonstrate the importance of controlling for correlations in the statistical properties being compared. In particular, we show that previously reported structural differences between diffusion paths of false and true news on Twitter disappear when comparing only cascades of the same size; differences between diffusion paths of images, videos, news, and petitions persist. Paired with a theoretical analysis of diffusion processes, our results suggest that in order to limit the spread of false news it is enough to focus on reducing the mean “infectiousness” of the information.
Joint work with Johan Ugander (Stanford University)

Biography:

Jonas L. Juul is a Carlsberg Fellow postdoc at the Technical University of Denmark. His research focuses on spreading processes and networks. His recent interests include developing methods to infer how content spreads online from observed diffusion paths, and evaluating the efficiency of mitigation measures in epidemiology. Before joining the Technical University of Denmark he was a post-doctoral researcher at the Center for Applied Mathematics, Cornell University where he worked with Austin Benson, Jon Kleinberg and Steven Strogatz. He obtained his Ph.D. in Physics of Complex Systems from the Niels Bohr Institute in 2020.

When: January 19, 2022,  2:00pm

Where: Luddy Center for Artificial Intelligence (2044)

Zoom link: https://iu.zoom.us/j/89107137699

Speaker: Brett Buttliere

Abstract:

The talk will focus on the psychology of scientist in both doing science and diffusing science, focusing in particular on the role of cognitive conflict as a motivating factor for communicating and doing science, online. Studies will be presented suggesting that people are more motivated to respond to those they disagree with, and that they write longer and more negative responses, but also that these posts are considered of higher informational content and quality. These posts also receive the most attention, and is distinctly different then situations where users do not have the opportunity to respond, where users prefer to avoid information they disagree with (filter bubble). The affordances of the situation appear to solve this large contradiction in the literature between filter bubbles and negativity biases online. Within science, studies will be outlined where scientists write more papers about negative topics, and that negative topics are discussed more online. I will also briefly present ongoing work examining the Wikipedia profiles of the world’s scientists, and open a discussion about what can and should be learned from these data (e.g., tracing the growth and movement of various fields across space and time). I hope it will be of interest to you and that there will be much room for discussion after.

Biography:

When: October 15, 2021,  12:00pm

Where: Luddy 1106 (Dorsey Learning Hall)

Zoom link: https://iu.zoom.us/j/89107137699

Speaker: Francesco Pierri 

Title: Characterization and detection of disinformation spreading in online social networks

Abstract:

Online social media expose us to a variety of false and misleading information which erodes public trust towards institutions, with severe backlashes in the real world. One example is the on-going COVID-19 pandemic, as the world experiences an “infodemic”, an overabundance of information including false and misleading content, which undermines medical intervention to fight the disease.

In this talk, I will present results from the research carried out during the last three years as a Ph.D. candidate in Politecnico di Milano, and that will be part of my forthcoming Ph.D. dissertation. I will also present results from my on-going collaboration with the Observatory on Social Media.

I leveraged a computer science and network science approach to tackle the problem of disinformation spreading in online social networks from two perspectives: (1) characterization, i.e., understanding the mechanisms and the actors involved in the spread of false and misleading information on online social media during relevant events such as political elections and the on-going COVID-19 pandemic; (2) detection, i.e., building a methodology to accurately classify news articles based on the interactions between users that take place on platforms like Twitter.

Biography:

I am a 3^rd (last) year Ph.D. student in the “Data Analytics and Decision Sciences” Ph.D. program at Politecnico di Milano, under the supervision of prof. Stefano Ceri and prof. Fabio Pammolli.

The focus of my research (and my Ph.D. dissertation) is to understand the spread of disinformation in online social networks, with both a computer science and network science approach. I also recently investigated the socio-economic consequences of the COVID-19 pandemic by leveraging human mobility data from mobile phones.

I am currently a visiting scholar at the Observatory on Social Media (since September 2020), where we investigate the COVID-19 infodemic and the spread of vaccine-related misinformation.

When: Friday, May 14, 2021

Where: https://iu.zoom.us/j/87346711649

Speaker: Chris Connell

Title: The DynACPD network embedding algorithm for prediction tasks on dynamic networks

Abstract: Classical network embeddings create a low dimensional representation of the learned relationships between features across nodes. Such embeddings are important for tasks such as link prediction and node classification. We consider low dimensional embeddings of “dynamic networks” — a family of time varying networks where there exist both temporal and spatial link relationships between nodes. We present novel embedding methods for a dynamic network based on higher order tensor decompositions for tensorial representations of the dynamic network. Our embeddings are analogous to certain classical spectral embedding methods for static networks. We demonstrate the effectiveness of our approach by comparing our algorithms’ performance on the link prediction task against an array of current baseline methods across three distinct real-world dynamic networks. Finally, we provide a mathematical rationale for this effectiveness in the regime of small incremental changes. This is joint work with Yang Wang.

Biography:

Chris Connell is interested in problems at the interface between dynamical systems, random walks, and the topology and geometry surrounding nonpositive curvature. Some of his recent work emphasizes bringing tools from these disciplines to bear on network embedding problems. He received his PhD from the University of Michigan and is now a professor of mathematics at Indiana University Bloomington.

When: Wednesday, October 2, 2019, 2pm

Where: Informatics East, room 322

Speaker: Jean-Gabriel Young

Efficient and fully Bayesian inference of complex networks from noisy data

Abstract: Rarely do we have access to error-free measurements of networks. Instead, we typically get to observe sequences of states that are, at best, indirect observations of a system’s structure. Recent research has led to a formalization of just how much, and in which ways, these measurements can inform us on the structure of real complex networks. It is now understood that domain-agnostic models are not silver bullets—one can come up with many different models of how a data set maps to a network, all leading to different inferences. The motivation for the work presented here is the realization that this leads to a tension preventing a broad adoption of these network reconstruction methods by practitioners.  On the one hand, domain expertise must necessarily go into devising good models, as to avoid erroneous conclusions. But at the same time, designing models can be challenging because one has to: derive a complete inference procedure from scratch; implement this procedure; verify the inference; and start anew if the results are not correct—for every model.

In this presentation, I will introduce a Bayesian framework that abstracts away most of the computational work, putting flexible model design center stage. The crucial modeling task that our framework leaves to the practitioner is that of determining how individual pair-wise measurements of interaction are explained by the presence or absence of an edge between two nodes. The method is broadly applicable in that these measurements can be anything, from a straightforward number of observed interactions, to time-series, or pairs comprising of a number of attempted and successful observations.

Biography:

Jean-Gabriel Young is broadly interested in problems at the intersection of statistics and complex systems. His recent work focuses on new exciting inference problems in network science, including the inference of the past of dynamical networks, network reconstruction from noisy data; and the inference of high-order interactions from pairwise data. He received his Ph.D. in Physics from Université Laval (2018), and is now a James S. McDonnell postdoctoral Fellow in complexity at the University of Michigan.

When: Friday, October 4, 2019, 11am

Where: Informatics East, room 322

Speaker: Eun Lee

Homophily and minority-group size explain perception biases in social networks

Abstract:  People’s perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show that these biases depend on the level of homophily, its asymmetric nature and on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations from six real-world networks. We also identify circumstances under which individuals can reduce their biases by relying on perceptions of their neighbours. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society.

Biography: Eun Lee is a post-doctoral researcher in the Department of Mathematics at the University of North Carolina (Chapel Hill) with an interest in contributing to a deeper understanding of the interplay among social network structure, the dynamics on and of that structure, perception, and collective behavior. My research interest lies in four topics:  The effects of the social network structure on perceptions, the effect of perceptions on the collective behavior, the co-evolution of social networks and human behavior, and understanding dynamics on and of temporal and social networks.

When: Tuesday, May 7, 2019, 2:00 pm

Where: Informatics West, Room 232

Speaker: Jinhyuk Yun

Inequality in the formation of collaborative knowledge – the case of Wikipedia

See: J. Yun et al., Nature Human Behaviour 3, 155 (2019)

Abstract:  Wikipedia and its sibling projects have served as a representative medium of worldwide knowledge market to share individuals’ knowledge in the information age. It has been commonly believed that such an open-editing communal data set accelerates democratization of knowledge, shifting the possession of knowledge from privileged class to general public. However, recent studies have observed inequality in authority and power distributions among the editors. One essential question is the underlying mechanism behind abiogenesis of such an unexpected authority. Naturally, lacking consideration of data sets other than English Wikipedia, had obscured such communal data set’s genuine nature of editing dynamics. In this study, we propose unbiased framework encompassing every element of Wikimedia projects. Our analysis, using the complete edit history of 267,304,095 articles from the entire 863 Wikimedia projects, reveals universality in growing regardless its category and language. The interplays between number of edits, number of editors, number of articles, and total length of text is characterized by a single set of exponents. Moreover, we observe the rapid increasing of the Gini coefficient, and suggest that this entrenched inequality stems from the nature of such open-editing communal data sets. We introduce a generative model accompanied with short-term and long-term memories, which successfully elucidates the mechanism behind the oligarchy in Wikipedia.

Biography:  Jinhyuk Yun is a senior research scientist in KISTI (Korea Institute of Science and Technology Information).  He worked as a data scientist at the Search Division in Naver Cooperation after receiving his Ph.D. in statistical physics from KAIST in 2016. His research aims to reveal the hidden structure and dynamics of complex systems with the large-scale dataset and mathematical frameworks focusing on the society, culture, media, collective knowledge, and so on.