People often stick to their existing beliefs, ignoring contradicting evidence or only interacting with those who reinforce their views. This tendency, referred to as being rigid in one's beliefs, is often driven by one's emotional connection to their beliefs and worsened by social media platforms which promote highly personalized content to maximize user engagement. As increased belief rigidity can negatively impact decisions, it is crucial to study and design interventions to reduce belief rigidity in online social networks. We design an experimental framework to passively infer belief rigidity and empirically quantify the effects of introducing randomness into the network structure as an intervention strategy. We recruit 163 participants into two conditions: one emulating traditional online platforms and the other more random. Our results suggest that individuals' beliefs are influenced by peer opinions, regardless of whether those opinions are similar to or differ from their own. Moreover, people are more likely to incorporate peers with (slightly) differing opinions into their networks when the recommendations are more randomized. Our findings highlight the need for future exploration into how design interventions can shape people's psycho-social functionalities in online social platforms.

Despite various efforts to tackle online misinformation, people inevitably encounter and engage with it, especially on social media platforms.Recent advances in LLMs present an opportunity to develop personalized interventions to address misinformed beliefs, and potentially offer more effective approaches than existing nontailored methods.In this paper, we design and evaluate personalized LLM agent that can consider users' demographics and personalities to tailor responses to mitigate misinformed beliefs.Our pipeline is grounded in facts through an external Retrieval Augmented Generation (RAG) knowledge base and is able to generate diverse output as a result of the personalization, with an average cosine similarity of 0.538.Our pipeline scores an average rating of 3.99 out of 5 when evaluated by a GPT-4o-mini LLM judge for response persuasiveness.Our methods can be adapted to design similar personalized agents in other domains.

A climate network represents the global climate system as a network where nodes are geographical locations each represented by time-series and edges indicate the interactions of time-series. Network science has been applied to climate data to study the dynamics of a climate network. To enable network dynamics analysis on historical and real-time climate data, the core task is the efficient computation and update of correlation matrices and climate networks. We demonstrate tsupy, a Python library, which extends Jupyter Notebook as instrumentation for performing climate network construction and analysis at interactive speed. This demonstration focuses on how tsupy enables dynamic network analysis on climate data. We also show how tsupy can be applied to neuro-imaging to understand the functional connectivity between brain regions.

Nighttime lights satellite imagery has been used for decades as a uniform, global source of data for studying a wide range of socioeconomic factors. Recently, another more terrestrial source is producing data with similarly uniform global coverage: anonymous and aggregated smart phone location. This data, which measures the movement patterns of people and populations rather than the light they produce, could prove just as valuable in decades to come. In fact, since human mobility is far more directly related to the socioeconomic variables being predicted, it has an even greater potential. Additionally, since cell phone locations can be aggregated in real time while preserving individual user privacy, it will be possible to conduct studies that would previously have been impossible because they require data from the present. Of course, it will take quite some time to establish the new techniques necessary to apply human mobility data to problems traditionally studied with satellite imagery and to conceptualize and develop new real time applications. In this study we demonstrate that it is possible to accelerate this process by inferring artificial nighttime satellite imagery from human mobility data, while maintaining a strong differential privacy guarantee. We also show that these artificial maps can be used to infer socioeconomic variables, often with greater accuracy than using actual satellite imagery. Along the way, we find that the relationship between mobility and light emissions is both nonlinear and varies considerably around the globe. Finally, we show that models based on human mobility can significantly improve our understanding of society at a global scale.