Online Communication by Emergency Responders during Crisis Events

2018 ◽  
pp. 149-173
Author(s):  
Emma S. Spiro
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Online Communication ◽  
Computational Techniques ◽  
Data Sets ◽  
The Public ◽  
Emergency Responders ◽  
Related Information ◽  
Social Media Platforms ◽  
Critical Components

Social media have become critical components of all phases of crisis management, including preparedness, response, and recovery. Numerous recent events have demonstrated that during extreme occurrences (such as natural hazards, civil unrest, and domestic terrorist attacks), social media platforms are appropriated for response activities, providing new infrastructure for official responders to disseminate event-related information, interact with members of the public, and monitor public opinion. Emergency responders recognize the potential of social media platforms and actively use these technologies to share information and connect with constituents; however, many questions remain about the effectiveness of social media platforms in reaching members of the public during times of crisis. Moreover, there is a strong tendency for research to focus on the behavior of the public rather than on that of official emergency responders. This chapter reviews prior and ongoing work that contributes to our understanding of usage practices and the effectiveness of networked online communication during times of crisis. In particular, it focuses on empirically driven research that utilizes large-scale data sets of behavioral traces captured from social media platforms. Together this body of work demonstrates how computational techniques combined with rich, curated data sets can be used to explore information and communication behaviors in online networks.

scholarly journals The public accountability of social platforms: lessons from a study on bots and trolls in the Brexit campaign

2018 ◽  
Vol 376 (2128) ◽  
pp. 20180003 ◽  
Author(s):  
Marco Bastos ◽  
Dan Mercea
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Ethical Issues ◽  
Lessons Learned ◽  
Information Warfare ◽  
The Public ◽  
Social Media Platforms ◽  
Application Programming ◽  
Access To Data ◽  
The Uk

In this article, we review our study of 13 493 bot-like Twitter accounts that tweeted during the UK European Union membership referendum debate and disappeared from the platform after the ballot. We discuss the methodological challenges and lessons learned from a study that emerged in a period of increasing weaponization of social media and mounting concerns about information warfare. We address the challenges and shortcomings involved in bot detection, the extent to which disinformation campaigns on social media are effective, valid metrics for user exposure, activation and engagement in the context of disinformation campaigns, unsupervised and supervised posting protocols, along with infrastructure and ethical issues associated with social sciences research based on large-scale social media data. We argue for improving researchers' access to data associated with contentious issues and suggest that social media platforms should offer public application programming interfaces to allow researchers access to content generated on their networks. We conclude with reflections on the relevance of this research agenda to public policy. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

scholarly journals Unsupervised Detection of Sub-Events in Large Scale Disasters

2020 ◽  
Vol 34 (01) ◽  
pp. 354-361 ◽  
Author(s):  
Chidubem Arachie ◽  
Manas Gaur ◽  
Sam Anzaroot ◽  
William Groves ◽  
Ke Zhang ◽  
...  
Keyword(s):  
Social Media ◽  
Large Scale ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Irrelevant Information ◽  
Data Sets ◽  
Emergency Responders ◽  
Learning Framework ◽  
Emergency Event ◽  
2015 Nepal Earthquake

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.

scholarly journals Experiments in Social Media

AI Magazine ◽  
2019 ◽  
Vol 40 (4) ◽  
pp. 74-77
Author(s):  
Toby Walsh
Keyword(s):  
Social Media ◽  
Voting Behavior ◽  
Large Scale ◽  
Large Population ◽  
Cambridge University ◽  
The Public ◽  
Real Risk ◽  
Psychometric Data ◽  
Social Media Platforms ◽  
Use Of Social Media

Social media platforms like Facebook and Twitter permit experiments to be performed at minimal cost on populations of a size that scientists might previously have dreamed about. For instance, one experiment on Facebook involved more than 60 million subjects. Such large-scale experiments introduce new challenges as even small effects when multiplied by a large population can have a significant impact. Recent revelations about the use of social media to manipulate voting behavior compound such concerns. It is believed that the psychometric data used by Cambridge Analytica to target US voters was collected by Dr Aleksandr Kogan from Cambridge University using a personality quiz on Facebook. There is a real risk that researchers wanting to collect data and run experiments on social media platforms in the future will face a public backlash that hinders such studies from being conducted. We suggest that stronger safeguards are put in place to help prevent this, and ensure the public retain confidence in scientists using social media for behavioral and other studies.

scholarly journals Not Just for Dancing? A Content Analysis of Concussion and Head Injury Videos on TikTok

2021 ◽  
Vol 3 ◽  
Author(s):  
Peyton N. Carter ◽  
Eric E. Hall ◽  
Caroline J. Ketcham ◽  
Osman H. Ahmed
Keyword(s):  
Social Media ◽  
Head Injury ◽  
Search Strategy ◽  
Sports Injuries ◽  
Healthcare Organizations ◽  
The Public ◽  
Related Information ◽  
Large Numbers ◽  
Social Media Platforms ◽  
Concussion Education

Social media platforms are an accessible and increasingly used way for the public to gather healthcare-related information, including on sports injuries. “TikTok” is currently one of the fastest-growing social media platforms worldwide, and it is especially popular amongst adolescents and young adults. The widespread use and popularity of TikTok suggests that this platform has potential to be a source for healthcare information for younger individuals. The aim of this study was to gain a preliminary understanding of the concussion/head injury-related information on TikTok, and to gauge if TikTok could serve as a platform for concussion education. This exploratory study used a systematic search strategy to understand more about how concussion is being portrayed through TikTok videos. Using the keywords “concussion” and “head injury,” 200 videos were downloaded from TikTok and 43 videos were excluded. Of the 92 videos retrieved using the keyword “concussion,” 95% (n = 88) had more than 100,000 views and 6% (n = 10) had been viewed more than 10 million times. Over half, 54% (n = 50) of the “concussion” videos depicted individuals “playing around” and getting hit in the head, whilst only 1% (n = 1) of the TikTok videos were categorized as “explaining concussion facts.” The large numbers of views of concussion-related TikTok videos demonstrates the popularity of this platform and indicates that healthcare organizations should consider TikTok as a potential means for concussion education amongst younger individuals.

scholarly journals Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 1612-1618
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
The Public ◽  
Social Media Platforms ◽  
Day By Day

Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

scholarly journals A new corpus-based convolutional neural network for big data text analytics

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Wedjdane Nahilia ◽  
Kahled Rezega ◽  
Okba Kazara
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Easy Access ◽  
Data Sets ◽  
Accuracy Score ◽  
Social Media Platforms

Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.

Kim Kardashian: the Leader of the Influencers

2019 ◽  
Author(s):  
Laila Fariha Zein ◽  
Adib Rifqi Setiawan
Keyword(s):  
Social Media ◽  
The United States ◽  
Armenian Genocide ◽  
The Public ◽  
Self Defense ◽  
Global Commerce ◽  
The World ◽  
Social Media Platforms ◽  
International Issues ◽  
Global Influence

In today’s world, it is easier and easier to stay connected with people who are halfway across the world. Social media and a globalizing economy have created new methods of business, trade and socialization resulting in vast amounts of communication and effecting global commerce. Like her or hate her, Kimberly Noel Kardashian West as known as Kim Kardashian has capitalized on social media platforms and the globalizing economy. Kim is known for two things: famous for doing nothing and infamous for a sex tape. But Kim has not let those things define her. With over 105 million Instagram followers and 57 million Twitter followers, Kim has become a major global influence. Kim has travelled around the world, utilizing the success she has had on social media to teach make-up master classes with professional make-up artist, Mario Dedivanovic. She owns or has licensed several different businesses including: an emoji app, a personal app, a gaming app, a cosmetics line, and a fragrance line. Not to be forgotten, the Kardashian family show, ‘Keeping Up with the Kardashians’ has been on the air for ten years with Kim at the forefront. Kim also has three books: ‘Kardashian Konfidential’, ‘Dollhouse’, and ‘Selfish’. With her rising social media following, Kim has used the platforms to show her support for politicians and causes, particularly, recognition of the Armenian genocide. Kim also recently spoke at the Forbes’ women’s summit. Following the summit, Kim tweeted out her support for a recent movement on Twitter, #freeCyntoiaBrown which advocated for a young woman who claimed to have shot and killed the man who held her captive as a teenage sex slave in self-defense. Kim had her own personal lawyers help out Cyntoia on her case. Kim has also moved beyond advocating for issues within the confines of the United States. As mentioned earlier, she is known for advocating for recognition of the Armenian genocide. In the last two years, her show has made it a point to address the Armenian situation as it was then and as it is now. Kim has been recognized as a global influencer by others across the wordl. We believe Kim has become the same as political leaders when it comes to influencing the public. Kim’s story reveals that the new reality creates a perfect opportunity for mass disturbances or for initiating mass support or mass disapproval. Although Kim is typically viewed for her significance to pop culture, Kim’s business and social media following have placed her deep into the mix of international commerce. As her businesses continue to grow and thrive, we may see more of her influence on international issues and an increase in the commerce from which her businesses benefit.

Opportunities for Qualitative Analysis of Social Media Platforms in Dietetic Research and Practice

2020 ◽  
pp. 1-5
Author(s):  
Meghan Lynch ◽  
Irena Knezevic ◽  
Kennedy Laborde Ryan
Keyword(s):  
Social Media ◽  
Low Cost ◽  
Online News ◽  
News Article ◽  
Discussion Forums ◽  
Research And Practice ◽  
The Public ◽  
Related Data ◽  
Social Media Platforms ◽  
Article Comment

To date, most qualitative knowledge about individual eating patterns and the food environment has been derived from traditional data collection methods, such as interviews, focus groups, and observations. However, there currently exists a large source of nutrition-related data in social media discussions that have the potential to provide opportunities to improve dietetic research and practice. Qualitative social media discussion analysis offers a new tool for dietetic researchers and practitioners to gather insights into how the public discusses various nutrition-related topics. We first consider how social media discussion data come with significant advantages including low-cost access to timely ways to gather insights from the public, while also cautioning that social media data have limitations (e.g., difficulty verifying demographic information). We then outline 3 types of social media discussion platforms in particular: (i) online news article comment sections, (ii) food and nutrition blogs, and (iii) discussion forums. We discuss how each different type of social media offers unique insights and provide a specific example from our own research using each platform. We contend that social media discussions can contribute positively to dietetic research and practice.

scholarly journals Quantifying Media Effects, Its Content, and Role in Promoting Community Awareness of Chikungunya Epidemic in Bangladesh

Epidemiologia ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 84-94
Author(s):  
Mst. Marium Begum ◽  
Osman Ulvi ◽  
Ajlina Karamehic-Muratovic ◽  
Mallory R. Walsh ◽  
Hasan Tarek ◽  
...  
Keyword(s):  
Social Media ◽  
Web Based ◽  
Study Objective ◽  
The Public ◽  
Mainstream Media ◽  
Related Information ◽  
Vector Borne ◽  
Media Outlet ◽  
And Control

Background: Chikungunya is a vector-borne disease, mostly present in tropical and subtropical regions. The virus is spread by Ae. aegypti and Ae. albopictus mosquitos and symptoms include high fever to severe joint pain. Dhaka, Bangladesh, suffered an outbreak of chikungunya in 2017 lasting from April to September. With the goal of reducing cases, social media was at the forefront during this outbreak and educated the public about symptoms, prevention, and control of the virus. Popular web-based sources such as the top dailies in Bangladesh, local news outlets, and Facebook spread awareness of the outbreak. Objective: This study sought to investigate the role of social and mainstream media during the chikungunya epidemic. The study objective was to determine if social media can improve awareness of and practice associated with reducing cases of chikungunya. Methods: We collected chikungunya-related information circulated from the top nine television channels in Dhaka, Bangladesh, airing from 1st April–20th August 2017. All the news published in the top six dailies in Bangladesh were also compiled. The 50 most viewed chikungunya-related Bengali videos were manually coded and analyzed. Other social media outlets, such as Facebook, were also analyzed to determine the number of chikungunya-related posts and responses to these posts. Results: Our study showed that media outlets were associated with reducing cases of chikungunya, indicating that media has the potential to impact future outbreaks of these alpha viruses. Each media outlet (e.g., web, television) had an impact on the human response to an individual’s healthcare during this outbreak. Conclusions: To prevent future outbreaks of chikungunya, media outlets and social media can be used to educate the public regarding prevention strategies such as encouraging safe travel, removing stagnant water sources, and assisting with tracking cases globally to determine where future outbreaks may occur.

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