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Event Detection from Social Media Stream: Methods, Datasets and Opportunities
Social media streams contain large and diverse amount of information, ranging
from daily-life stories to the latest global and local events and news.
Twitter, especially, allows a fast spread of events happening real time, and
enables individuals and organizations to stay informed of the events happening
now. Event detection from social media data poses different challenges from
traditional text and is a research area that has attracted much attention in
recent years. In this paper, we survey a wide range of event detection methods
for Twitter data stream, helping readers understand the recent development in
this area. We present the datasets available to the public. Furthermore, a few
research opportunitiesComment: 8 page
Towards reproducible research of event detection techniques for Twitter
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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
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