Using Social Networks to Raise HIV Awareness among Homeless Youth

2017 ◽  
Vol 61 (6) ◽  
pp. 4:1-4:10 ◽  
Author(s):  
A. Yadav ◽  
H. Chan ◽  
A. Xin Jiang ◽  
H. Xu ◽  
E. Rice ◽  
...  

Author(s):  
Amulya Yadav ◽  
Bryan Wilder ◽  
Eric Rice ◽  
Robin Petering ◽  
Jaih Craddock ◽  
...  

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.


2014 ◽  
Vol 29 (12) ◽  
pp. 2172-2191 ◽  
Author(s):  
Robin Petering ◽  
Eric Rice ◽  
Harmony Rhoades ◽  
Hailey Winetrobe

2011 ◽  
Vol 41 (5) ◽  
pp. 561-571 ◽  
Author(s):  
Suzanne Wenzel ◽  
Ian Holloway ◽  
Daniela Golinelli ◽  
Brett Ewing ◽  
Richard Bowman ◽  
...  

Author(s):  
Amulya Yadav ◽  
Hau Chan ◽  
Albert Xin Jiang ◽  
Haifeng Xu ◽  
Eric Rice ◽  
...  

This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER's sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address two real-world issues: (i) they completely fail to scale up to real-world sizes; and (ii) they do not handle deviations in execution of intervention plans. HEALER handles these issues via two major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; and (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner. HEALER was deployed in the real world in Spring 2016 with considerable success.


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