Nicholas A. Christakis, Sterling Professor of Social and Natural Science, Yale University

 

Abstract of the lecture:  Human beings choose their friends, and often their neighbors and co-workers, and we inherit our relatives; and each of the people to whom we are connected also does the same, such that, in the end, we assemble ourselves into face-to-face social networks that obey particular mathematical and sociological rules.  Why do we do this?  And how might a deep understanding of human social network structure and function be used to intervene in the world to make it better?  Here, I will review recent research from our lab describing three classes of interventions involving both offline and online networks: (1) interventions that rewire the connections between people; (2) interventions that manipulate social contagion, modifying the flow of desirable or undesirable properties; and (3) interventions that manipulate the position of people within network structures.  I will illustrate what can be done using a variety of experiments in settings as diverse as fostering cooperation or the diffusion of innovation in networked groups online, to fostering health behavior change in developing world villages and towns.  I will also discuss recent experiments with “hybrid systems” comprised of both humans and artificial intelligence (AI) agents interacting in small groups.  Overall, by taking account of people’s structural embeddedness in social networks, and by understanding social influence, it is possible to intervene in social systems to enhance desirable population-level properties as diverse as health, wealth, cooperation, coordination, and learning.

 

 

Laura Nelson, Assistant Professor of Sociology, the University of British Columbia

 

Abstract of the lecture:  In this talk a describe a three-step methodological framework called computational grounded theory, which combines expert human knowledge and hermeneutic skills with the processing power and pattern recognition of computers, producing a more methodologically rigorous but interpretive approach to content analysis. The first, pattern detection step, involves inductive computational exploration of text, using techniques such as unsupervised machine learning and word scores to help researchers to see novel patterns in their data. The second, pattern refinement step, returns to an interpretive engagement with the data through qualitative deep reading or further exploration of the data. The third, pattern confirmation step, assesses the inductively identified patterns using further computational and natural language processing techniques. The result is an efficient, rigorous, and fully reproducible computational grounded theory. This framework can be applied to any qualitative text as data, including transcribed speeches, interviews, open-ended survey data, or ethnographic field notes, and can address many potential research questions.

 

 

Esteban Moro, Researcher and data scientist at MIT Connection Science and Professor at Universidad Carlos III (UC3M), Spain

Abstract of the lecture:  The economic and social progress of our urban areas, institutions, and jobs depend on the diversity and resilience of the social fabric in cities. However, several major forces can erode these connections, such as income or racial segregation and differences in education and job access. In this talk, I will present recent research on understanding the fragility of the network of social connections and interactions in cities by analyzing behavioral mobility data and its relationship with networked inequalities, such as experienced segregation, access to healthy food, and adaptation to the recent pandemic.

Han Zhang, Assistant Professor, Division of Social Science, HKUST

 

Abstract of the lecture:  Visual information such as images and videos is abundant in the digital age. However, visual data are still underutilized in social science research.
In this talk, I will give an overview of large-scale visual data analysis, including historical developments and recent advances in image data analysis algorithms.
After that, I will give some practical demo of image analysis using Python.

 

Minsu Park, Assistant Professor of Social Research and Public Policy, New York University Abu Dhabi (NYUAD)

 

Abstract of the lecture:  While emotion research has successfully measured patterns of change in human affect using self-reports and text analysis, these techniques cannot capture people’s preferences for external stimuli that influence mood states and levels of emotional arousal. Hence, there is a gap in our understanding regarding how individuals actively shape their emotional states through their choices of external stimuli. Detailed activity data generated from popular services like Spotify give us an exciting new way to research the relationship between one such external stimulus –music– and affect. In this talk, I investigate diurnal and seasonal patterns of affective preference, drawing on a dataset of 765 million online music plays streamed by 1 million individuals in 51 countries. I show that diurnal patterns are similar across cultures and demographic groups. Individuals listen to more relaxing music late at night and more energetic music during normal business hours, including mid-afternoon when affective expression hits bottom. I find, however, differences in baselines: younger people listen to more arousing music; compared with other regions, music played in Latin America is more arousing, while music in Asia is more relaxing; and compared with other chronotypes, ‘night owls’ (people who are habitually active or wakeful at night) listen to more relaxing music. Seasonal patterns vary with distance from the equator and between the Northern and Southern hemispheres and are more strongly correlated with absolute day length than with changes in day length. Taken together with previous findings on affective expression in text, these results suggest that musical choice both shapes and reflects mood.