Evidence map›Paper›PMID 26203397›Full record

ArticlePLoS currents2015

Twitter as a Potential Disaster Risk Reduction Tool. Part III: Evaluating Variables that Promoted Regional Twitter Use for At-risk Populations During the 2013 Hattiesburg F4 Tornado.

Guy Paul Cooper, Violet Yeager, Frederick M Burkle, Italo Subbarao

Open access · greenAbstract read
In one paragraph

Article in PLoS currents, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Guy Paul CooperCollege of Osteopathic Medicine, William Carey University, Hattiesburg, Mississippi, USA.
Violet YeagerCollege of Osteopathic Medicine, William Carey University, Hattiesburg, Mississippi, USA.
Frederick M BurkleHarvard Humanitarian Initiative, Harvard University, Cambridge, Massachusetts; The Woodrow Wilson International Center for Scholars, Washington, DC, USA.
Italo SubbaraoCollege of Osteopathic Medicine, William Carey University, Hattiesburg, Mississippi, USA.
William Carey University · USDana-Farber/Harvard Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionStudy goals attempt to identify the variables most commonly associated with successful tweeted messages and determine which variables have the most influence in promoting exponential dissemination of information (viral spreading of the message) and trending (becoming popular) in the given disaster affected region.

methodsPart II describes the detailed extraction and triangulation filtration methodological approach to acquiring twitter data for the 2013 Hattiesburg Tornado. The data was then divided into two 48 hour windows before and after the tornado impact with a 2 hour pre-tornado buffer to capture tweets just prior to impact. Criteria-based analysis was completed for Tweets and users. The top 100 pre-Tornado and post-Tornado retweeted users were compared to establish the variability among the top retweeted users during the 4 day span.

resultsPre-Tornado variables that were correlated to higher retweeted rates include total user tweets (0.324), and total times message retweeted (0.530).  Post-Tornado variables that were correlated to higher retweeted rates include total hashtags in a retweet (0.538) and hashtags #Tornado (0.378) and #Hattiesburg (0.254). Overall hashtags usage significantly increased during the storm. Pre-storm there were 5,763 tweets with a hashtag and post-storm there was 13,598 using hashtags.

conclusionsTwitter's unique features allow it to be considered a unique social media tool applicable for emergency managers and public health officials for rapid and accurate two way communication.  Additionally, understanding how variables can be properly manipulated plays a key role in understanding how to use this social media platform for effective, accurate, and rapid mass information communication.

Indexed as

CommunicationsDisaster analysisDisaster risk reductionPrevention and preparednessSocial mediaTwitter

Identifiers

PMID26203397
PMCPMC4494727
OpenAlexW2289071471

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.