Event Detection and Aspects in Twitter: A BoW Approach

Pradhan, Abhaya Kumar; Mohanty, Hrushikesha; Lal, Rajendra Prasad

doi:10.1007/978-3-030-05366-6_16

Cited by 11 publications

(2 citation statements)

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“…The four students agreed so rarely ( kappa = 0.233) on the annotations that the authors themselves refused to draw any conclusions. Few others measured inter-annotator agreement and only ever reached moderate levels (Mele et al, 2019 ; Pradhan et al, 2019 ). Look closer in event tracking literature, and you will find many more implicit examples.…”

Section: Event Tracking's Manual Evaluationsmentioning

confidence: 99%

The myth of reproducibility: A review of event tracking evaluations on Twitter

2023

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Event tracking literature based on Twitter does not have a state-of-the-art. What it does have is a plethora of manual evaluation methodologies and inventive automatic alternatives: incomparable and irreproducible studies incongruous with the idea of a state-of-the-art. Many researchers blame Twitter's data sharing policy for the lack of common datasets and a universal ground truth–for the lack of reproducibility–but many other issues stem from the conscious decisions of those same researchers. In this paper, we present the most comprehensive review yet on event tracking literature's evaluations on Twitter. We explore the challenges of manual experiments, the insufficiencies of automatic analyses and the misguided notions on reproducibility. Crucially, we discredit the widely-held belief that reusing tweet datasets could induce reproducibility. We reveal how tweet datasets self-sanitize over time; how spam and noise become unavailable at much higher rates than legitimate content, rendering downloaded datasets incomparable with the original. Nevertheless, we argue that Twitter's policy can be a hindrance without being an insurmountable barrier, and propose how the research community can make its evaluations more reproducible. A state-of-the-art remains attainable for event tracking research.

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Section: Event Tracking's Manual Evaluationsmentioning

confidence: 99%

The myth of reproducibility: A review of event tracking evaluations on Twitter

2023

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“…One aspect of crisis management is identifying sub-events as a significant crisis unfolds (Abhik and Toshniwal 2013). Studies have tried to detect sub-events from tweets using different methods, both supervised and unsupervised (Ardalan et al ;Chen, Xu, and Mao 2018;Pradhan, Mohanty, and Lal 2019). Some recent supervised methods, introduced the problem of event type detection as a sequence labeling task using a neural sequence model on a news corpus (Bekoulis et al 2019).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Detection of Sub-Events in Large Scale Disasters

Arachie

Anzaroot²,

Groves³

et al. 2020

AAAI

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Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

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