Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations

By Kathleen Carley, Kumar Sumeet
57th Annual Meeting of the Association for Computational Linguistics | July 2019

Citation

Carley, Kathleen., Sumeet, Kumar. Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations 57th Annual Meeting of the Association for Computational Linguistics .

57th Annual Meeting of the Association for Computational Linguistics, 2019

Abstract

Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance+ rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve state-of-the-art performance, outperforming the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively

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.

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Sumeet Kumar