Research Summary
Social media and mobile technologies have now become an integral part of our everyday life, primarily guiding the interactions among individuals, organizations, and governments. Availability of data regarding individuals and the content they consume enables us to understand their preferences and engagement, which can be used to create personalized experiences for them. My dissertation focuses on deriving social analytics from both user generated data as well as an organization generated data. The core thesis of the dissertation is discover digital personas of individuals from both content, engagement, and social networks, and leverage that to provide a more personalized experience to the individual. Imagine a user tweeting about an activity, and then gaining access to the relevant and most appropriate articles whether in news or publications. Imagine a user browsing health related content, and based on both the content and the demographic attributes perceived from the content interests, getting recommendations on other appropriate health related articles. My work will develop algorithms to model user attributes and behavior in conjunction with the content. I am also developing models to identify a user across multiple devices using their fragmented actions as they switch between devices. My work will help fill the gap arising from the paucity and sparsity of content in social media by contextualizing it. My research is at the cross section of machine learning, natural language processing and Big Data tools and technologies, such as Spark.
Interests
- Data Mining
- Machine Learning
- Deep Learning
- Recommendation Systems
- User Online Experience
- Social Media
- Big Data
Research Projects
-
Peace processes are complex, protracted, and contentious involving significant bargaining and compromising among various societal and political stakeholders. In civil war terminations, it is pertinent to measure the pulse of the nation to ensure that the peace process is responsive to citizens' concerns. Social media yields tremendous power as a tool for dialogue, debate, organization, and mobilization thereby adding more complexity to the peace process. Using Colombia's final peace agreement and national referendum as a case study, we investigate the influence of two important indicators: inter-group polarization and public sentiment towards the peace process. We present a detailed linguistic analysis to detect inter-group polarization and a predictive model that leverages tweet structure, content, and user-based features to predict public sentiment towards the Colombian peace process. We demonstrate that had pro-accord stakeholders leveraged public opinion from social media the outcome of the Colombian referendum could have been different.
Related Publication:
- [JOURNAL] Aastha Nigam, Henry K. Dambanemuya, Madhav Joshi, and Nitesh Chawla, Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring, Big Data Journal, 2017
- [POSTER] Aastha Nigam, Henry K. Dambanemuya, Madhav Joshi, and Nitesh Chawla, Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring, Women in Statistics and Data Science, October 2017, La Jolla, 2017
- [POSTER] Aastha Nigam, Henry K. Dambanemuya, Madhav Joshi, and Nitesh Chawla, Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring, ACM Student Research Competition, October 2017, Orlando, Florida
-
Users are increasingly turning to the Internet as an essential source of health information. In this research, we study the health-seeking behavior of users on a national health and wellness-based knowledge sharing online platform. We begin by identifying the topical interests of users from different content consumption sources. Using these topical preferences, we explore information consumption and health-seeking behavior across three contextual dimensions: user-based demographic attributes, time-related features, and community-based socioeconomic factors. We then study how these context signals can be used to explain specific user health topic preferences. Our findings suggest that linking demographic features to user profiles is more effective in explaining health preferences than other features. Our work demonstrates the value of using contextual factors to characterize and understand the content consumption of users seeking health and wellness information online.
Related Publication:
- [BOOK CHAPTER] Aastha Nigam, and Nitesh Chawla, A Content-Driven Framework for Online User Understanding, Book chapter in Social Media Analytics: Advances and Applications, 2017
-
Twitter provides a platform for information sharing and diffusion, and has quickly emerged as a mechanism for organizations to engage with their consumers. A driving factor for engagement is providing relevant and timely content to users. We posit that the engagement via tweets offers a good potential to discover user interests and leverage that information to target specific content of interest. To that end, we have developed a framework that analyzes tweets to identify the interests of current followers and leverages topic models to deliver a personalized topic profile for each user. We validated our framework by partnering up with a local media company and analyzing the content gap between them and their followers. We also developed a mobile application that incorporates the proposed framework.
Related Publication:
- [PAPER] Aastha Nigam, Salvador Aguinaga and Nitesh Chawla, Connecting the Dots to Infer Followers’ Topical Interest on Twitter, IEEE International Conference on Behavioral, Economic, and Socio-Cultural Computing, 2016.
- [POSTER] Aastha Nigam, Salvador Aguinaga and Nitesh Chawla, Topic Models to Increase User Engagement on Twitter. Presented at Grace Hopper Conference (GHC) 2015, Houston - TX.
- [POSTER] Aastha Nigam, Salvador Aguinaga and Nitesh Chawla, Topic Models to Increase User Engagement on Twitter. Presented at Computing Research Association - Women (CRA-W) Workshop 2015, San Fransisco - CA
-
There is an increasing trend amongst users to consume information from websites and social media. With the huge influx of content it becomes challenging for the consumers to navigate to topics or articles that interest them. Particularly in health care, the content consumed by a user is controlled by various factors such as demographics and lifestyle. In this paper, we use a semi-bipartite network model to capture the interactions between users and health topics that interest them. We use a supervised link prediction approach to recommend topics to users based on their past reading behavior and contextual data associated to a user such as demographics.
Related Publication:
- [PAPER] Aastha Nigam and Nitesh Chawla, Link Prediction in a Semi-Bipartite Network for Recommendation. Accepted at Asian Conference on Intelligent Information and Database Systems (ACIIDS) 2016.
-
As robots enter human spaces, unique perception challenges are emerging. Sensing human activity, adapting to highly dynamic environments, and acting coherently and contingently is challenging when robots transition from structured environments to human-centric ones. We approach this problem by employing context-based perception, a biologically-inspired, low-cost approach to sensing that leverages noisy, global features. Across several months, our mobile robot collected real-world, multimodal data from multi-use locations; where the same space might be used for many different activities. We then ran a series of unimodal and multimodal classification experiments. We successfully classified several aspects of situational context from noisy data, and, to our knowledge are the first group to do so. This work represents an important step toward enabling robots that can readily leverage context to solve perceptual tasks.
Related Publication:
- [PAPER] Aastha Nigam and Laurel D. Riek , Context-Based Perception for Social Mobile Robot Navigation. Accepted at International Conference on Intelligent Robots and Systems (IROS) 2015.