Title: Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms
Authors: Thang Nguyen, Yuening Hu, Jordan Boyd-Graber
Speaker: Thang Nguyen (Oral presentation practice at ACL 2014)
Time: 30 minutes

Abstract: Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expectation maximization. However, these new methods lack the rich priors associated with probabilistic models. We examine Arora et al.’s anchor words algorithm for topic modeling and develop new, regularized algorithms that not only mathematically resemble Gaussian and Dirichlet priors but also improve the interpretability of topic models. Our new regularization approaches make these efficient algorithms more flexible; we also show that these methods can be combined with informed priors.

Title: Our grief is unspeakable: Automatically measuring the community impact of a tragedy
Authors: Kimberly Glasgow, Clayton Fink, Jordan Boyd-Graber
Speaker: Kimberly Glasgow (Oral presentation practice at ICSWM 2014)
Time: 30 minutes

Abstract: Social media offer a real-time, unfiltered view of how disasters affect communities. Crisis response, disaster mental health, and—more broadly—public health can benefit from automated analysis of the public’s mental state as exhibited on social media. Our focus is on Twitter data from a community that lost members in a mass shooting and another community—geographically removed from the shooting—that was indirectly exposed. We show that a common approach for understanding emotional response in text: Linguistic Inquiry and Word Count (LIWC) can be substantially improved using machine learning. Starting with tweets flagged by LIWC as containing content related to the issue of death, we devise a categorization scheme for death-related tweets to induce automatic text classification of such content. This improved methodology reveals striking differences in the magnitude and duration of increases in death-related talk between these communities. It also detects subtle shifts in the nature of death-related talk. Our results offer lessons for gauging public response and for developing interventions in the wake of a tragedy.