Questions for Nilam Ram about his 4/18 talk regarding "Modeling at Multiple Time-Scales"

[jamesallenevans]

Pose your questions for Nilam Ram for his talk Modeling at Multiple Time-Scales: Screenomics and Other Super-Intensive Longitudinal Paradigms. Abstract A decade ago, we used newly emerging smartphone technologies to obtain multiple time-scale data that facilitated study of new intraindividual variability constructs and how they changed over time. The recent merging of daily and digital life further opens opportunity to observe, probe, and modify every imaginable aspect of human behavior – at a scale we never imagined. Using collections of intensive longitudinal data from survey panels, experience sampling studies, social media, laboratory observations, and our new Screenomics paradigm, I illustrate how methodological invocation of zooms, tensions, and switches (ZOOTS) is transforming our understanding of human dynamics and development. Along the way, I develop calls for more flexible definitions of time, fluidity and diversity of methodological approach, and engagement with science that adds good into the world. Two short, related papers available here and here

[Kevin2330]

Good afternoon,
The first question is related to methodological challenges: What are some of the primary methodological challenges faced when integrating data across different sources and time scales, such as surveys, social media, and laboratory observations?

Second, in terms of practical applications, how can the findings from your Screenomics research be applied in practical settings? Are there particular domains or industries where this research could have a significant impact? Furthermore, could you discuss some examples of how your research has been or could be used to foster positive outcomes in communities or populations?

Thank you.

[secorey]

Hi Prof. Nam,
Thank you for presenting your research. The Screenome Project seems like an ambitious but incredibly valuable project. I'm specifically interested in the rationale behind collecting screenshots rather than implementing some other way to collect phone usage data. It seems like this would be computationally expensive to collect and analyze.

[ethanjkoz]

A large concern with collecting such granular data from participant's devices is privacy. Though the paper mentions mitigating privacy and security concerns using encryption, secure storage, and de identification, I was wondering if you could elaborate on these measures, with a particular emphasis on de-identification. How can we be sure that the data is truly de-identified? We have seen in the past that supposedly anonymized survey data can be re-identified, so I was curious what makes this data de-identified?

[alejandro-sarria-morales]

Hi proffesor Nam, thanks for sharing your research. How do you think the changes to Molenaars manifesto will affect how we study and apply findings to help people with their digital lives and well-being? Are these changes at risk of being coopted by ill-intentioned agents that may benefit from information with this level of granularity?

[QIXIN-LIN]

I'm eager to understand how findings and methodologies from screenomics can be applied in real-world scenarios. Could you highlight any specific domains or industries where this research might have a significant impact? If there are no clear applications yet, is there a particular issue we should deal with?

[lbitsiko]

Collecting fine-grained digital data for research is widely acknowledged and, in fact, creates an epistemological framework around the practice. Considering the granularity of your approach, how would you respond to the adverse effects of the project not in terms of privacy concerns but from a surveillance perspective (e.g., its normalization) and the role of power within scientific research? This seems especially important considering an unavoidable(?) feedback loop within science and industry.

[Weiranz926]

Thank you for your sharing! My question is that given the increasing integration of digital technologies in everyday life, how can the Screenomics paradigm be used to inform public policy or interventions aimed at addressing digital well-being or reducing screen time-related issues in various populations?

[isaduan]

Thank you for sharing your research with us. I am really interested in your idea of "screenome" - speaking of myself, I would love to see the analysis of what I see and do on screen just to learn about how digital content is shaping my mind and thought in ways I do not realize. I wonder how you think of the benefits of such research to participants? How can we make the project attractive for people to share the data?

[shaangao]

Thank you for sharing this exciting new dataset for studying human behaviors in naturalistic settings! If we represent each screenshot as an embedding vector in the representation space, we will obtain an average (daily) temporal trajectory for each participant. -- Will we observe shared trajectories across individuals? On the other hand, if we cluster these trajectories, will we obtain clusters that are interpretable in terms of, for example, personalities, attention patterns, decision patterns, etc.?

[Caojie2001]

Thank you for sharing your interesting research! My question is that with the developing innovative methods in fine-grained digital data collection and processing, from what perspective and how will the techniques and conclusions of Screenomics influence the relationship between electronics and people?

[fabrice401]

An interesting research! The paper presents a comprehensive analysis of human screen time through innovative methodologies like the Human Screenome Project. Yet, how might we refine these research methodologies to capture nuanced aspects of digital interactions, such as their context, duration, and emotional triggers, for a more holistic understanding of their impact on individuals' well-being?

[natashacarpcast]

Wow! This is very interesting, thank you! I have a question regarding the data collection part... From your experience, which kinds of people are willing to share such private data? Are there any patterns in demographics, etc.?

[zhuoqingli526]

Very interesting research! You mentioned applying foundational AI models to individual-specific behavior datasets to enhance model transferability and predictive capabilities. Could you elaborate on how these AI models' transferability is assessed and optimized in practical applications? Specifically, how do you address potential 'out-of-distribution' issues when transferring from one task or dataset to another that is very different?

[yuy123337]

Hi professor Nam,

Thank you for sharing this novel research!
Is it possible that individuals who are willing to share their screenomics data with academics might introduce a bias similar to that of self-report data, given that they need to be willing to share in the first place? Certainly! Also, how do you ensure that the screenshot data collected, which may contain private and sensitive information, are adequately protected and anonymized to maintain participants' privacy? Since based on my personal experience, whenever I take a screenshot, it's when I need to document some important information, but mostly private information or information that needs a context to be understood.