Evidence map›Paper›PMID 25153889›Full record

ArticlePloS one2014

Twitter in the cross fire--the use of social media in the Westgate Mall terror attack in Kenya.

Tomer Simon, Avishay Goldberg, Limor Aharonson-Daniel, Dmitry Leykin, Bruria Adini

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
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

12 citing papers in PubMed.

  1. The Effect of Risk Communication on Public Behavior to Non-Conventional Terrorism-Randomized Control Trial.International journal of environmental research and public health · 2021
    Trial
  2. Article
  3. Municipal Risk Communication and Public Trust: Reducing Counterproductive Behaviors Across Emergency Scenarios.Risk analysis : an official publication of the Society for Risk Analysis · 2026
    Article
  4. Article
  5. Article
  6. Accident & Emergency Informatics and One Digital Health.Yearbook of medical informatics · 2022
    Article
  7. Article
  8. Article
  9. Predicting Infectious Disease Using Deep Learning and Big Data.International journal of environmental research and public health · 2018
    Article
  10. Article
  11. Article
  12. 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

5 authors.

Tomer SimonDepartment of Emergency Medicine, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheba, Israel; PREPARED Center for Emergency Response Research, Ben-Gurion University of the Negev, Beer Sheba, Israel; Ready.org.il - Emergency readiness and preparedness in Israel, Givatayim, Israel.
Avishay GoldbergDepartment of Health Systems Management, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheba, Israel.
Limor Aharonson-DanielDepartment of Emergency Medicine, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheba, Israel; PREPARED Center for Emergency Response Research, Ben-Gurion University of the Negev, Beer Sheba, Israel.
Dmitry LeykinDepartment of Emergency Medicine, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheba, Israel; PREPARED Center for Emergency Response Research, Ben-Gurion University of the Negev, Beer Sheba, Israel; Department of Psychology, Tel Hai Academic College, Kiryat Shmona, Israel.
Bruria AdiniDepartment of Emergency Medicine, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheba, Israel; PREPARED Center for Emergency Response Research, Ben-Gurion University of the Negev, Beer Sheba, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

On September 2013 an attack on the Westgate mall in Kenya led to a four day siege, resulting in 67 fatalities and 175 wounded. During the crisis, Twitter became a crucial channel of communication between the government, emergency responders and the public, facilitating the emergency management of the event. The objectives of this paper are to present the main activities, use patterns and lessons learned from the use of the social media in the crisis. Using TwitterMate, a system developed to collect, store and analyze tweets, the main hashtags generated by the crowd and specific Twitter accounts of individuals, emergency responders and NGOs, were followed throughout the four day siege. A total of 67,849 tweets were collected and analyzed. Four main categories of hashtags were identified: geographical locations, terror attack, social support and organizations. The abundance of Twitter accounts providing official information made it difficult to synchronize and follow the flow of information. Many organizations posted simultaneously, by their manager and by the organization itself. Creating situational awareness was facilitated by information tweeted by the public. Threat assessment was updated through the information posted on social media. Security breaches led to the relay of sensitive data. At times, misinformation was only corrected after two days. Social media offer an accessible, widely available means for a bi-directional flow of information between the public and the authorities. In the crisis, all emergency responders used and leveraged social media networks for communicating both with the public and among themselves. A standard operating procedure should be developed to enable multiple responders to monitor, synchronize and integrate their social media feeds during emergencies. This will lead to better utilization and optimization of social media resources during crises, providing clear guidelines for communications and a hierarchy for dispersing information to the public and among responding organizations.

Indexed as

Emergency RespondersTerrorismData MiningKenyaSecurity MeasuresSocial Media

Identifiers

PMID25153889
PMCPMC4143241

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.