Lydia Manikonda

Experiences with evacuation route planning algorithms

Efficient tools are needed to identify routes and schedules to evacuate affected populations to safety in the event of natural disasters. Hurricane Rita and the recent tsunami revealed limitations of traditional approaches to provide emergency preparedness for evacuees and to predict the effects of evacuation route planning (ERP). Challenges arise during evacuations due to the spread of people over space and time and the multiple paths that can be taken to reach them; key assumptions such as stationary ranking of alternative routes and optimal substructure are violated in such situations. Algorithms for ERP were first developed by researchers in operations research and transportation science. However, these proved to have high computational complexity and did not scale well to large problems. Over the last decade, we developed a different approach, namely the Capacity Constrained Route Planner (CCRP), which generalizes shortest path algorithms by honoring capacity constraints and the spread of people over space and time. The CCRP uses time-aggregated graphs to reduce storage overhead and increase computational efficiency. Experimental evaluation and field use in Twin Cities Homeland Security scenarios demonstrated that CCRP is faster, more scalable, and easier to use than previous techniques. We also propose a novel scalable algorithm that exploits the spatial structure of transportation networks to accelerate routing algorithms for large network datasets. We evaluated our new approach for large-scale networks around downtown Minneapolis and riverside areas. This article summarizes experiences and lessons learned during the last decade in ERP and relates these to Professor Goodchilds contributions.

Modeling and Understanding Visual Attributes of Mental Health Disclosures in Social Media

Content shared on social media platforms has been identified to be valuable in gaining insights into peoples mental health experiences. Although there has been widespread adoption of photo-sharing platforms such as Instagram in recent years, the role of visual imagery as a mechanism of self-disclosure is less understood. We study the nature of visual attributes manifested in images relating to mental health disclosures on Instagram. Employing computer vision techniques on a corpus of thousands of posts, we extract and examine three visual attributes: visual features (e.g., color), themes, and emotions in images. Our findings indicate the use of imagery for unique self-disclosure needs, quantitatively and qualitatively distinct from those shared via the textual modality: expressions of emotional distress, calls for help, and explicit display of vulnerability. We discuss the relationship of our findings to literature in visual sociology, in mental health self disclosure, and implications for the design of health interventions.

Discourse Analysis of User Forums in an Online Weight Loss Application

Online social communities are becoming increasingly popular platforms for people to share information, seek emotional support, and maintain accountability for losing weight. Studying the language and discourse in these communities can offer insights on how users benefit from using these applications. This paper presents a preliminary analysis of language and discourse patterns in forum posts by users who lose weight and keep it off versus users with fluctuating weight dynamics. Our results reveal differences about how the types of posts, polarity of sentiments, and semantic cohesion of posts made by users vary along with their weight loss pattern. To our knowledge, this is the first discourse-level analysis of language and weight loss dynamics.

metoo Through the Lens of Social Media

Sexual abuse – a highly stigmatized topic in the society has spurred a revolution in the recent days especially through the shared posts on social media platforms via attaching the hashtag #metoo. Individuals from different backgrounds and ethnicities began sharing on the online venues about their personal experiences of getting sexually assaulted. This paper makes an initial attempt to asses the public reactions and emotions by utilizing the publicly shared #metoo posts by performing a comparative analysis of the tweets shared on Twitter as well as on Reddit. Though nearly equal ratios of negative and positive posts are shared on both platforms, Reddit posts are focused on the sexual assaults within families and workplaces while Twitter posts are on showing empathy and encouraging others to continue the #metoo movement. The data collected in this research helps in the preliminary analysis of the user engagement, discussion topics, word connotations and sentiment with respect to the #metoo movement.