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 shelter officials, who need to find the most influential homeless youth to raise awareness about HIV (and other STDs) among their peers, and to drive the homeless youth community towards safer behaviors and lifestyles.
Influence Maximization for Social Good
This project focuses on the study of diffusion processes 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.
On a humanitarian level, 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, etc.
On a scientific level, 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. Our primary focus in this project is to develop algorithms and tools which are actually usable and deployable in the real world, i.e., algorithms which can actually benefit society for good.
In fact, we strive to validate all our models, algorithms and techniques in the real world by testing it out with actual homeless youth (specifically youth in Los Angeles).
Over the past three years, we have been collaborating with social workers from Safe Place for Youth (SPY) and My Friend’s Place (homeless shelters in Los Angeles) to understand the problems that they face in raising awareness about HIV (and other STDs) among homeless youth, come up with innovative ways to solve their problems, and finally test out our algorithms by doing pilot deployment studies with actual homeless youth.
HIV/AIDS is a very dangerous disease 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. HIV/AIDS kills 2 million people worldwide every year.
In USA alone, AIDS kills around 10,000 people per year. 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.
As a result, many homeless shelters (including our collaborators Safe Place for Youth and My Friend’s Place) organize intervention camps for homeless youth in order to raise awareness about HIV prevention and treatment practices. These intervention camps consist of day-long educational sessions in which the participants are provided with information about HIV prevention measures. However, due to financial/manpower constraints, the shelters cannot intervene on the entire target (homeless youth) population. Instead, they try to maximize the spread of awareness among the target population (via word-of-mouth influence) using the limited resources at their disposal. To achieve this goal, the shelter uses the friendship based social network of the target population to strategically choose the participants of their limited intervention camps.
Therefore, the key question is how can we, as computer scientists, help these shelters in finding the most “influential” youth 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. Finding these “influential” youth will enable homeless shelters to focus their precious time/manpower on the correct youth, who are likely to achieve more information spread than any other possible choice of youth.
We, in our work, try to help homeless shelters 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. Specifically, there are 4 major challenges, out of which we highlight two here.
First, 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.
Second, 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 address the first challenge, we have developed a Facebook application which parses Facebook contact lists of homeless youth to create a first approximation of the social network.
On top of that, we use state-of-the-art link prediction techniques to infer additional friendships in between the homeless youth. To address the second and other 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 overcome these challenges. In order to find out more about our techniques and algorithms, please have a look at our publications.
Flowchart of techniques used in PSINET: Our solver
HEALER is an adaptive decision support system which has two components: a Facebook application and HEAL, the core algorithm that powers HEALER. Before the homeless shelter begins its interventions, HEALER’s Facebook application interacts with homeless youth to parse their contact lists on Facebook in order to generate an approximation of the friendship based social network that connects these youth.
This network is then refined by running state-of-the-art link prediction techniques, in order to infer potential friendships, which may not be present on Facebook. This refined network is then passed onto HEAL, the core algorithm that powers HEALER. It uses a combination of state-of-the-art techniques from Artificial Intelligence, Sequential Planning, Online Learning, Decision Theory and Mathematics (which we developed in our lab) to generate recommendations about which homeless youth should be chosen as intervention attendees.
The shelter official acts on that recommendation by conducting the intervention with the selected youth. After the intervention, HEALER incorporates real-time feedback provided by shelter officials (about what happened during the intervention) to update its decision making policy for future interventions. To learn more about how HEALER works, please refer to our publications.
We have deployed HEALER in the real world by conducting a pilot study recently with Safe Place for Youth, a homeless shelter 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. We are currently conducting a second pilot study to further test our algorithms. We also plan to conduct a much larger study with 900 homeless youth in Spring 2017. A very long term goal of ours is to introduce HEALER in homeless shelters across the country, which could potentially change the way health interventions work. If you are interested and would like to contribute, feel free to talk to us!