Kno.e.sis research in Social Media for Social Good
The Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) – Wright State University is widely known for its interdisciplinary research in big data working with more than 40 collaborators and partners.
Kno.e.sis center believes in Computing for Human Experience, therefore, our research spans many areas of Web 3.0 and its applications in the social media era which has a meaningful impact on the life of the individual. Our research provide solutions for the greatest challenges of our time such as disaster management and diplomacy (e.g., social media-based emergency coordination, water diplomacy), e-Sciences with a focus on health-care and life sciences (metabolomics, human parasite research, extra-medical use of painkillers, reducing hospital readmissions, cardiology data mining, and literature-based knowledge discovery).
"we are working on real data, we are working with people who help us understand the real need and then our technological solutions can bridge two things. So we are able to see if our work can actually make an impact"
~ Dr. Amit Sheth
- 1 Research Projects
- 1.1 Society Safety and Cause Engagement:
- 1.2 Social Media and Public Health:
- 2 Media
Society Safety and Cause Engagement:
Social and Physical Sensing Enabled Decision Support (Hazards SEES):
Hazards SEES is a NSF funded project involving a collaboration between Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) – Wright State University and Ohio State University.
This project allows people to engage with a cause to have a good impact on the lives of others. This kind of collaboration is especially important in the case of disasters where people from affected areas or who are familiar with the affected areas help to enrich social media contents. Emergency response systems were developed at kno.e.sis for the purpose of providing aid to help seekers throughout the disaster life. For more details look at the comprehensive Summary about Social Media Research in Disaster/Emergency Response Systems by Kno.e.sis team.
The ubiquitous presence of gender-based violence (GBV), primarily against women, is affecting both developed and developing countries. More than 35% of the world’s female population has experienced some form of gender-based violence in their lives (World Health Organization, 2013). According to the United Nations Population Fund (UNFPA), “GBV is a serious public health concern that also impedes the crucial role of women and girls in development.” However, anti-GBV sentiment is not universal.The European Union’s council report highlighted a persistent lack of comparable data across regions and over time (European Union, 2010), hampering both assessment and mitigation. Both the UNFPA and the European Union Agency for Fundamental Rights seek better data sourcing and policy design.
In this study, we examine the utility of Computational Social Science to address the problem of monitoring public views about GBV on a global scale. Mining large-scale online data from mobile technology and social media such as Twitter promises to complement traditional methods and provide greater insight with finer detail.
Context-Aware Harassment Detection on Social Media is an inter-disciplinary project among the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), the Department of Psychology, and Center for Urban and Public Affairs (CUPA) at Wright State University. The aim of this project is to develop comprehensive and reliable context-aware techniques (using machine learning, text mining, natural language processing, and social network analysis) to glean information about the people involved and their interconnected network of relationships, and to determine and evaluate potential harassment and harassers. An interdisciplinary team of computer scientists, social scientists, urban and public affairs professionals, educators, and the participation of college and high schools students in the research will ensure wide impact of scientific research on the support for safe social interactions.
Project Safe Neighborhood: Westwood Partnership to Prevent Juvenile Repeat Offenders is an interdisciplinary project involving the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) – Wright State University with other community partners including the City of Dayton (Dayton Police Department), Montgomery County Juvenile Justice and University of Dayton to prevent juvenile repeat offenders from committing crime in the Westwood neighborhood located in the City of Dayton, Ohio.
This project is sponsored by the Ohio Criminal Justice Services (OCJS) through the United States Attorney’s Office for the Southern District of Ohio for the Violent Gang and Gun Crime Reduction Program
Social Media and Public Health:
eDrugTrends is an inter-disciplinary project between the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) and the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University developed to monitor cannabis and synthetic cannabinoid use.
We use social media to monitor: Cannabis Use Trends and Associated Harms, Trends in Synthetic Cannabis Use, Effects of Cannabis Legalization Policies, and Social Media Data for Drug Abuse Epidemiology Research and Content Analysis (i.e. Sentiment and Emotion analysis). Using social media, we are able to identify and compare trends in cannabis and synthetic cannabinoid use, relative to regional variation of cannabis legalization policies.
Depression project is a relatively new project by which we plan to develop effective methods for detection of depressive behavior, not only at an individual-level, but also at a community-level. The latter is highly pertinent because depression is significantly influenced by variations in social determinants and socioecological factors. In particular, we will leverage robust and longitudinal electronic health record (EHR) systems at Mayo Clinic and private insurance (UnitedHealthCare/Optum Labs) reimbursement and claims data along with online social media data from Twitter and PatientsLikeMe as well as geo-coded neighborhood and environmental data to develop a “big data” platform for identifying combinations of online socio-behavioral factors and neighborhood environmental conditions to enable innovative ways for detection of depressive behavior within communities and identify patterns and changes in health care utilization for depression across different communities and geographies within U.S.