Eric B. Bell

Disentangling the Lexicons of Disaster Response in Twitter

People around the world use social media platforms such as Twitter to express their opinion and share activities about various aspects of daily life. In the same way social media changes communication in daily life, it also is transforming the way individuals communicate during disasters and emergencies. Because emergency officials have come to rely on social media to communicate alerts and updates, they must learn how users communicate disaster related content on social media. We used a novel information-theoretic unsupervised learning tool, CorEx, to extract and characterize highly relevant content used by the public on Twitter during known emergencies, such as fires, explosions, and hurricanes. Using the resulting analysis, authorities may be able to score social media content and prioritize their attention toward those messages most likely to be related to the disaster.

Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis

Social networks are dynamically changing over time e.g., some accounts are being created and some are being deleted or become private. This ephemerality at both an account level and content level results from a combination of privacy concerns, spam, and deceptive behaviors. In this study we analyze a large dataset of 180,340 accounts active during the Russian-Ukrainian crisis to discover a series of predictive features for the removal or shutdown of a suspicious account. We find that unlike previously reported profile and network features, lexical features form the basis for highly accurate prediction of the deletion of an account.

Assessment of user home location geoinference methods

This study presents an assessment of multiple approaches to determine the home and/or other important locations to a Twitter user. In this study, we present a unique approach to the problem of geotagged data sparsity in social media when performing geoinferencing tasks. Given the sparsity of explicitly geotagged Twitter data, the ability to perform accurate and reliable user geolocation from a limited number of geotagged posts has proven to be quite useful. In our survey, we have achieved accuracy rates of over 86% in matching Twitter user profile locations with their inferred home locations derived from geotagged posts.