Detecting and Characterizing Events

Chaney, Allison J. B.; Wallach, Hanna; Connelly, Matthew; Blei, David M.

doi:10.18653/v1/d16-1122

Cited by 11 publications

(10 citation statements)

References 36 publications

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“…As an additional part of our study, we look to use changepoint analysis to uncover more behavior change. Besides social media post behavior, changepoint detection has been used in a wide variety of other areas, such as detecting changes in individuals who are at-risk for suicide attempts by analyzing their Twitter posts [38], anomalies in race car driving [41], and sociopolitical events based upon historical documents [9].…”

Section: Changepoint Analysismentioning

confidence: 99%

Behavior Change in Response to Subreddit Bans and External Events

Thomas¹,

Riehm²,

Glenski³

et al. 2021

Preprint

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As more people flock to social media to connect with others and form virtual communities, it is important to research how members of these groups interact to understand human behavior on the Web. In response to an increase in hate speech, harassment and other antisocial behaviors, many social media companies have implemented different content and user moderation policies. On Reddit, for example, communities, i.e., subreddits, are occasionally banned for violating these policies. We study the effect of these regulatory actions as well as when a community experiences a significant external event like a political election or a market crash. Overall, we find that most subreddit bans prompt a small, but statistically significant, number of active users to leave the platform; the effect of external events varies with the type of event. We conclude with a discussion on the effectiveness of the bans and wider implications for the online content moderation.

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Section: Changepoint Analysismentioning

confidence: 99%

Behavior Change in Response to Subreddit Bans and External Events

Thomas¹,

Riehm²,

Glenski³

et al. 2021

Preprint

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“…Here we develop a Neyman-Scott process model to perform event detection in document streams. We study a dataset of US State Department declassified cables from the 1970s [Chaney et al, 2016], which spans a duration of T = 40 days (June 21-July 31, 1976) and totals N = 34, 732 cables. The cables can be thought of as archaic text messages between diplomatic entities, or nodes.…”

Section: Application: Detecting World Events In Streams Of Diplomatic...mentioning

confidence: 99%

“…We compared this Neyman-Scott process model for document streams to a baseline clustering model on this dataset similar to the model proposed by Chaney et al [2016]. Both models allow for background cables with sender-specific rates and word distributions.…”

Section: Application: Detecting World Events In Streams Of Diplomatic...mentioning

confidence: 99%

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models

Wang¹,

Degleris²,

Williams³

et al. 2022

Preprint

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Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of observed data points according to another Poisson process. This construction is similar to Bayesian nonparametric mixture models like the Dirichlet process mixture model (DPMM) in that the number of latent events (i.e. clusters) is a random variable, but the point process formulation makes the NSP especially well suited to modeling spatiotemporal data. While many specialized algorithms have been developed for DPMMs, comparatively fewer works have focused on inference in NSPs. Here, we present novel connections between NSPs and DPMMs, with the key link being a third class of Bayesian mixture models called mixture of finite mixture models (MFMMs). Leveraging this connection, we adapt the standard collapsed Gibbs sampling algorithm for DPMMs to enable scalable Bayesian inference on NSP models. We demonstrate the potential of Neyman-Scott processes on a variety of applications including sequence detection in neural spike trains and event detection in document streams.

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“…Many existing methods specialize in detecting (Chaney et al, 2016), tracking (Allan et al, 1998) and summarizing evolving topics in timestamped documents. Some systems focus specifically on summarizing event "spikes": both in news (e.g.…”

Section: Temporal Focusmentioning

confidence: 99%

Relational Summarization for Corpus Analysis

Handler¹,

O’Connor²

2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

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This work introduces a new problem, relational summarization, in which the goal is to generate a natural language summary of the relationship between two lexical items in a corpus, without reference to a knowledge base. Motivated by the needs of novel user interfaces, we define the task and give examples of its application. We also present a new queryfocused method for finding natural language sentences which express relationships. Our method allows for summarization of more than two times more query pairs than baseline relation extractors, while returning measurably more readable output. Finally, to help guide future work, we analyze the challenges of relational summarization using both a news and a social media corpus.

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Detecting and Characterizing Events

Cited by 11 publications

References 36 publications

Behavior Change in Response to Subreddit Bans and External Events

Behavior Change in Response to Subreddit Bans and External Events

Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models

Relational Summarization for Corpus Analysis

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