I am a Doctoral candidate in Human Computer Interaction at the University of Maryland, College Park and I am member of the Human Computer Interaction Lab (HCIL). My research is on cyberbullying detection and mitigation through the study of social media and social networks, sentiment analysis of social network data, making predictions and recommendations based on available social media data, user experience evaluation, and understanding the different types of discourse that emerge due to anonymity on social networks.
I have worked on cross-disciplinary research collaborations with researchers in various fields like machine learning, human-computer interaction, and design. At Microsoft Research in Cambridge, I worked with an interdisciplinary team to develop a programming language for visually impaired children. I led several focus groups with visually impaired children and created a visual simulator for debugging the language. At IBM Research, I used machine learning in the Social Computing Watson team to lead a Natural Language Processing study on discourse discovery and cyberbullying detection on social media platform askFM. As a Data Science for Social good fellow, I completed a project to extract relevant information from tweets during disasters for Red Cross and UN Disaster relief workers. My responsibilities included feature engineering, using machine-learning techniques to build a classifier to identify tweets as pertaining to disaster. I've used both qualitative (surveys, interviews, focus groups) and quantitative (sentiment analysis and various other Natural Language Processing) methods to address my research questions.
I graduated from the University of Maryland with a double degree in Computer Science and Middle East Studies and received a MS degree in Human Computer Interaction at the University of Maryland. I am currently working on my dissertation and will be graduating with a PhD in Human Computer Interaction. I am interested in social computing, sentiment analysis of social network data, and research on the relationship between civic engagement and social media.
In my research and work up to date, I address the mitigation of adolescent cyberbullying through a three-pronged methodological approach: 1) data-centric exploratory study 2) human-centered participatory design and 3) an experimental design to evaluate the effectiveness of cyberbullying mitigation tools. In my research, I offer design recommendations for building and administering cyberbullying mitigation tools.
My research explores why users are motivated to post and interact through ASKfm, a social media platform that affords cyberbullying and how anonymity and the site's other affordances affect these interactions. In my research to date, I discuss the unique affordances specific to semi-anonymous Q&A social media platforms and how such affordances enable users to engage in self-disclosure and gaining social support on sensitive topics. I then use a human-centered approach methodology to co-design cyberbullying prototypes with teens. I use the design recommendations derived from the participatory design study to test the impact of a cyberbullying mitigation system. I address technological mechanisms to mitigate sadness and decline in well-being caused by negative online experiences and cyberbullying. I administer cyberbullying mitigation through technology-mediated memory; in other words, I use positive posts and images participants have previously shared on social media to remind them of existing social support in users’ social networks.
I have a strong interest in research on emergent social technologies and the effect they have on civic engagement. I have worked diverse projects at Data Science for Social Good, IBM Research, and Microsoft Research. I have published at venues like CHI and CSCW.