PROJECT OVERVIEW

Influence Maximization for Social Good Using Social Networks to Spread Health Based Information

This project focuses on the development of decision support systems for homeless youth drop-in center staff who need to find the most influential homeless youth to raise awareness about HIV (and other sexually transmitted infections) among their peers, and to drive the homeless youth community towards safer behaviors and lifestyles.

Over the past three years, we have been collaborating with drop-in agency staff and youth experiencing homelessness at Safe Place for Youth (SPY), the Los Angeles LGBT Center’s Youth Center on Highland, and My Friend’s Place.

ABOUT HIV/AIDS

HIV is a very serious infection that sees no race, no color, no gender, no economic background and not even a specific age group. It can affect anyone, at any time if they put themselves in a situation where they could be at risk. Internationally, 37 million people are living with HIV.

HIV has an extremely high incidence among homeless youth, as they are more likely to engage in high HIV-risk behaviors (e.g., unprotected sexual activity, injection drug use) than other sub-populations. In fact, previous studies show that homeless youth are at 16X greater risk of HIV infection than stably housed populations. Thus, any attempt at eradicating HIV crucially depends on our success at minimizing rates of HIV infection among homeless youth.

In order to reduce the transmission of HIV, we are using a peer leader social network-based intervention, for information dissemination about HIV. Therefore, the key question is how can we identify the most “influential” youth, to be trained as peer leaders, from the social network of homeless youth? We want to find the youth who can spread awareness about HIV and induce behavior change among their peers, in the quickest and most efficient possible manner.

INFLUENCE MAXIMIZATION FOR SOCIAL GOOD

Have You Heard? (HYH) focuses on the study of information diffusion in social networks of hard to reach populations (such as homeless youth) in order to spread information and raise general levels of awareness about dangerous diseases (such as HIV) among such populations.

The end goal of this project is to reduce rates of HIV infection among disadvantaged populations by influencing and inducing behavior change in homeless youth populations that drives them towards safer practices, such as regular HIV testing. The goal is not only to model these influence spread phenomena, but to also develop decision support systems (and the necessary tools/algorithms/mechanisms) using which information can be spread in the social networks of homeless youth in the most efficient manner. One of our primary foci in this project is to develop algorithms and tools that can be deployed in the real world.

Over the past three years, we have been collaborating with drop-in agency staff and youth experiencing homelessness at Safe Place for Youth (SPY), the Los Angeles LGBT Center’s Youth Center on Highland, and My Friend’s Place.

RESEARCH OVERVIEW

We, in our work, try to help homeless drop-in centers use their resources more effectively by modeling this entire problem of selecting the most influential youth as an Influence Maximization Problem, which is a widely studied problem in the field of Artificial Intelligence. However, most previous work in this area has failed at addressing some key challenges that show up in the real world. Two major challenges are:

1) Constructing social networks of homeless youth is a big challenge, since these youth are a hard-to-reach population, and mapping out their social circles requires a lot of time and money.  Their networks are constantly changing, and there is not a constant/consistent roster like a classroom of school children.

2) Even if we are able to construct these networks, there is always noise in the data collection procedure, which leads to uncertainty about the true structure of the social network. This uncertainty needs to be accounted before deciding who is “influential” in the social network and who isn’t.

To overcome these challenges, we use a combination of state-of-the-art techniques from Artificial Intelligence, Sequential Planning, Decision Theory and Mathematics (that we developed in our lab). In order to find out more about our techniques and algorithms, please have a look at our publications.

MAIN STUDY

We conducted a 3-year study, which enrolled 718 young people experiencing homelessness at 3 Los Angeles area drop-in centers–My Friend’s Place, Safe Place for Youth, and the Los Angeles LGBT Center’s Youth Center on Highland.  This study is using the CHANGE algorithm, created by our team members.

CHANGE uses social network data is that is gathered via in-person interviews with a subsample of the youth.  Specifically, we randomly sample about 10% of the youth and then, for each of them, randomly sample one of their friends.  This allows us to begin to identify the network of homeless young persons accessing services at each drop-in center.   We use a combination of state-of-the-art techniques from Artificial Intelligence, Sequential Planning, Decision Theory and Mathematics to then identify who are the most influential youth, based on their positions within the network, to then train as peer leaders.

Flowchart of techniques used in the CHANGE algorithm

The results from this trial show that youth in the CHANGE arm of the intervention experience statistically significant improvements in their risk of engaging in unprotected sex compared to youth enrolled in an observation-only control group. We also observe a tendency towards larger and faster decrease in rates of unprotected sex in youth in the CHANGE arm compared to youth who receive an intervention where the highest degree nodes in the network are chosen as peer leaders.

PILOT STUDY

We deployed an algorithm named HEALER in the real world at Safe Place for Youth–a homeless drop-in center in Venice Beach, Los Angeles. Safe Place for Youth provides free food and clothing to homeless youth of the ages 12-25, three times a week. We enrolled 62 homeless youth from this shelter into our study and we conducted three test interventions. We used HEALER’s Facebook application to generate the network (see figure below) that connected these youth.

Each number here is a homeless youth (their names have been replaced by numbers to protect their anonymity), and the edges between them represent their friendships. The results from this pilot were very promising. We found that HEALER was able to spread information to almost 66% of homeless youth in the network (one month after interventions had ended). This shows that HEALER is successful at finding the most influential youth in the network.

More importantly, we found that due to HEALER’s interventions, there was a 25% self-reported increase in the number of homeless youth who get tested for HIV regularly.

Thus, HEALER was successful in inducing behavior change among the youth as well.

CLINICAL TRIAL

_{Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness} 

Having developed an artificial intelligence system to optimize social network interventions in a community health setting, to maximize the spread of information through peer leaders, in this work,  we conducted a field trial enrolling 713 Youth Experiencing Homelessness (YEH) at drop-in centers in a large US city. The field trial compared interventions planned with the algorithm to those where the highest-degree nodes in the youths’ social network were recruited as peer leaders (the standard method in public health) and to an observation-only control group. Results from the field trial show that youth in the AI group experience statistically significant reductions in key risk behaviors for HIV transmission, while those in the other groups do not. This provides, to our knowledge, the first empirical validation of the usage of AI methods to optimize social network interventions for health. We also discuss lessons learned over the course of this project which may inform future attempts to use AI in community-level interventions.

Read more in our AAAI 2021 paper.

PROJECT PARTICIPANTS

SPONSORS

AWARDS

Pragnesh Jay Modi Best Student Paper Award at the International Conference on Autonomous Agents and Multi Agent Systems (AAMAS), 2016

Most Visionary Paper Award at the International Workshop on Issues with Deployment of Emerging Agent Based Systems (IDEAS) 2016

PREVIOUS WORK

MEDIA

Mashable News, December 20, 2015: Incredible innovations that improved the world in 2015.

Mashable News, February 6, 2015: How an algorithm could help spread HIV information among homeless teens.

My Friend’s Place, February 23, 2015: My Friend’s Place and USC Partner to Prevent Spread of HIV.

Next City News, February 18, 2015: Can an algorithm help prevent HIV from Spreading Among Homeless Young People?

Motherboard News, February 4, 2015: Artificial Intelligence Could Help Reduce HIV Among Homeless Youths.

USC Press Release, January 27, 2015: USC Social Work Professors Team With Computer Scientists to Prevent HIV Spread Among Homeless Youth.

RELATED PUBLICATIONS

Eric Rice, Bryan Wilder, Laura Onasch-Vera, Graham Diguiseppi, Robin Petering, Chyna Hill, Amulya Yadav, Sung-Jae Lee, and Milind Tambe. 6/21/2021. “A Peer-Led, Artificial Intelligence-Augmented Social Network Intervention to Prevent HIV among Youth Experiencing Homelessness.” to appear in the Journal of Acquired Immune Deficiency Syndrome (JAIDS).

Haipeng Chen, Wei Qiu, Han-Ching Ou, Bo An, and Milind Tambe. 7/25/2021. “Contingency-Aware Influence Maximization: A Reinforcement Learning Approach.” In Conference on Uncertainty in Artificial Intelligence.

Amulya Yadav, Leandro Marcolino, Eric Rice, Robin Petering, Hailey Winetrobe, Harmony Rhoades, Milind Tambe, and Heather Carmichael. 2015. “Preventing HIV Spread in Homeless Populations Using PSINET.” In Conference on Innovative Applications of Artificial Intelligence (IAAI-15).

Amulya Yadav, Ece Kamar, Barbara Grosz, and Milind Tambe. 2016. “HEALER: POMDP Planning for Scheduling Interventions among Homeless Youth (Demonstration).” In International conference on Autonomous Agents and Multiagent Systems.

Amulya Yadav, Hau Chan, Albert Jiang, Eric Rice, Ece Kamar, Barbara Grosz, and Milind Tambe. 2016. “POMDPs for Assisting Homeless Shelters – Computational and Deployment Challenges.” In AAMAS 2016 IDEAS Workshop.

Leandro Soriano Marcolino, Aravind Lakshminarayanan, Amulya Yadav, and Milind Tambe. 2016. “Simultaneous Influencing and Mapping Social Networks (Extended Abstract).” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, and Milind Tambe. 2016. “Using Social Networks to Aid Homeless Shelters: Dynamic Influence Maximization under Uncertainty.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2016.

Amulya Yadav, Aida Rahmattalabi, Ece Kamar, Phebe Vayanos, Milind Tambe, and Venil Loyd Noronha. 2017. “Explanations Systems for Influential Maximizations Algorithms.” In 3rd International Workshop on Social Influence Analysis.

Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, and Milind Tambe. 2017. “Maximizing Awareness about HIV in Social Networks of Homeless Youth with Limited Information.” In International Joint Conference on Artificial Intelligence (IJCAI).

Bryan Wilder, Amulya Yadav, Nicole Immorlica, Eric Rice, and Milind Tambe. 2017. “Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).

Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo. 2017. “Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).

Bryan Wilder, Nicole Immorlica, Eric Rice, and Milind Tambe. 2018. “Maximizing Influence in an Unknown Social Network.” In AAAI conference on Artificial Intelligence (AAAI-18).

Bryan Wilder, Laura Onasch-Vera, Juliana Hudson, Jose Luna, Nicole Wilson, Robin Petering, Darlene Woo, Milind Tambe, and Eric Rice. 2018. “End-to-End Influence Maximization in the Field.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).

Lily Hu, Bryan Wilder, Amulya Yadav, Eric Rice, and Milind Tambe. 2018. “Activating the ‘Breakfast Club’: Modeling Influence Spread in Natural-World Social Networks.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).

Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo. 2018. “Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth.” In International Joint Conference on Artificial Intelligence (IJCAI).

Eric Rice, Amanda Yoshioka-Maxwell, Robin Petering, Laura Onasch-Vera, Jaih Craddock, Milind Tambe, Amulya Yadav, Bryan Wilder, Darlene Woo, Hailey Winetrobe, and Nicole Wilson. 2018. “Piloting the Use of Artificial Intelligence to Enhance HIV Prevention Interventions for Youth Experiencing Homelessness.” Journal of the Society for Social Work and Research, Volume 9, Number 4., 9, 4.