In this research, we tackle both these challenges using co-training that jointly trains two classifiers: 1) a label propagation model on networks, and 2) a text classification model using text features. We use the weak stance signals given by two to four labeled hashtags for training the models. Though training examples obtained using hashtags are noisy, co-training effectively handles the noise, thereby enabling stance mining on new topics with minimal labeling effort. We show the advantages of our approach on a Twitter dataset that contains manually verified stance labels of over 400 users each on three issues.
Sumeet Kumar is an Assistant Professor of Information Systems at the Indian School of Business (ISB). He studies problems at the intersection of technology and society. He is interested in analysing user behaviour, quantifying polarisation on online forums , and finding advertisements disguised as regular content on online platforms. His current focus is on identifying implicit or hidden advertisements in videos posted on children’s platforms such as YouTube Kids.
Additionally, Professor Kumar has conducted research in software design and development, with particular emphasis on user experience. He has investigated the use of mobile phone sensors during emergencies to improve situational awareness. His study on the Wireless Emergency Alerts (WEA) service in the United States addressed several issues of critical importance to emergency alerts effectiveness and adoption. Notably, some of his research recommendations was included in the US Federal Communications Commission (FCC) proposed changes to WEA.
He completed his undergraduate education at Indian Institute of Technology (IIT) Kanpur. He holds two Master’s degrees—in Software Engineering and in Machine Learning--both from Carnegie Mellon University, where he also earned his doctorate degree.
