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Verifying baselines for crisis event information classification on Twitter
Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability
Crisis Communication Patterns in Social Media during Hurricane Sandy
Hurricane Sandy was one of the deadliest and costliest of hurricanes over the
past few decades. Many states experienced significant power outage, however
many people used social media to communicate while having limited or no access
to traditional information sources. In this study, we explored the evolution of
various communication patterns using machine learning techniques and determined
user concerns that emerged over the course of Hurricane Sandy. The original
data included ~52M tweets coming from ~13M users between October 14, 2012 and
November 12, 2012. We run topic model on ~763K tweets from top 4,029 most
frequent users who tweeted about Sandy at least 100 times. We identified 250
well-defined communication patterns based on perplexity. Conversations of most
frequent and relevant users indicate the evolution of numerous storm-phase
(warning, response, and recovery) specific topics. People were also concerned
about storm location and time, media coverage, and activities of political
leaders and celebrities. We also present each relevant keyword that contributed
to one particular pattern of user concerns. Such keywords would be particularly
meaningful in targeted information spreading and effective crisis communication
in similar major disasters. Each of these words can also be helpful for
efficient hash-tagging to reach target audience as needed via social media. The
pattern recognition approach of this study can be used in identifying real time
user needs in future crises
Explicit diversification of event aspects for temporal summarization
During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness
A Bayesian-Based Approach for Public Sentiment Modeling
Public sentiment is a direct public-centric indicator for the success of
effective action planning. Despite its importance, systematic modeling of
public sentiment remains untapped in previous studies. This research aims to
develop a Bayesian-based approach for quantitative public sentiment modeling,
which is capable of incorporating uncertainty and guiding the selection of
public sentiment measures. This study comprises three steps: (1) quantifying
prior sentiment information and new sentiment observations with Dirichlet
distribution and multinomial distribution respectively; (2) deriving the
posterior distribution of sentiment probabilities through incorporating the
Dirichlet distribution and multinomial distribution via Bayesian inference; and
(3) measuring public sentiment through aggregating sampled sets of sentiment
probabilities with an application-based measure. A case study on Hurricane
Harvey is provided to demonstrate the feasibility and applicability of the
proposed approach. The developed approach also has the potential to be
generalized to model various types of probability-based measures
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