Why I Retweet? Exploring User’s Perspective on Decision-Making of Information Spreading during Disasters

The extensive use of social media during disasters raises an important issue concerning use of social media to spread information, including misinformation. This study explores the underlying behavioral context of disaster information sharing by Twitter users. We conducted a web survey with 999 respondents in Japan to determine what makes people retweet disaster information in disaster situations. As a result of factor analysis, four factors were identified from 36 questions, namely: 1) Willingness to provide relevant and updated information because the information is believable, 2) Want people to know the information they perceive as important, 3) Retweeter subjective feelings and interests, and 4) Want to get feedback and alert other people. The results suggest that two of the factors influenced different groups of people in the community differently; however, everybody can play their role to reduce the negative impact of social media used for future disaster. Based on the findings, we discuss practical and design implications of social media use during disasters

UN Global Pulse: Annual Report 2013

Through public-private partnerships, innovative analysis and the development of open-source methodologies, Global Pulse is strengthening public sector capacity to leverage digital Big Data for development and resilience. This report provides a brief overview of advances made during 2013

Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication

Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication

The Political Language of Disaster: Indonesian Government Communication for Handling the Covid-19 Pandemic

Indonesia is one of the most populous countries exposed to Covid-19 with a high mortality rate. The pandemic has been in Indonesia since March 2020, but before the Covid-19 virus was officially declared to enter Indonesia, the Indonesian government previously took this threat lightly. It showed that the Indonesian government was using political language such as disaster communication. Some disparaging comments left public officials. This research uses a qualitative approach with content analysis techniques. The primary data is derived from President Joko Widodo’s speech at Bogor Palace and the tweets of @kemenkes @BNPB and @Jokowi’s Twitter accounts related to Covid-19. This research examines how the Indonesian government communicates its policies in overcoming the Covid-19 pandemic. Furthermore, this research is intended to reveal the narrative developed by the Indonesian government in campaigning for policies to overcome the Covid-19 pandemic. The results show that there are doubts from the government in determining the policies to be taken to overcome the spread of Covid-19. The narrative developed by the government—like the phrase “new normal” and “enggak mudik” phrase—is a narrative that is intended to create calm, even though it can endanger public health

Tweeting Strategy: Military Social Media Use as Strategic Communication

Many Western militaries now actively engage with various social media platforms. The starting point for my dissertation research was this question: how does the military use social media? Considering the Canadian Armed Forces’ use of Twitter as a case study, I collected over 14,000 tweets from four Twitter accounts of the Canadian Armed Forces (CAF), the Canadian Army, the Royal Canadian Navy, and the Royal Canadian Air Force with some tweets as old as September 2012 and the most recent tweets from December 2015. I employed Grounded Theory Method to analyze these tweets, which revealed four themes — organization, history, preparedness, and partnership. These themes create an image of CAF as a Canadian institution and a military one, as they speak to the many war and other combat operations that the Canadian Armed Forces have engaged in at the behest of the government. A literature review conducted simultaneously with the analysis uncovered the International Relations literature on strategic narratives and the Organizational/Military Studies literature on strategic communication. The main finding is that the Canadian Armed Forces are using social media for the strategic communication of government strategic narratives because the norms of civil-military relations require the military to follow government orders and prevent the military from using social media as intended because social media tend to be political whereas the military has to be “apolitical.” The military, thus, maintains an “apolitical” image by communicating what the government wishes it to communicate, even though the government’s narrative can be political. Government strategic narratives frame organizational strategic communication, while organizational strategic communication supports government strategic narratives

Social media and public policy

Introduction: Government and public service delivery is taking place in a changed world. A significant level of social, economic and political activity is now happening on the internet.As people buy and sell goods, search for information, browse the web and share their day–to–day experiences with colleagues, friends and family through social networks, they produce an enormous amount of data.The use of this data to develop insights is growing rapidly. In the private sector it is being used to enhance decision making, understand customer behaviour, improve operational efficiency and identify new markets.The new information environment also obliges government to develop new capabilities to understand the information available and to compete for attention and influence within it.Part of the challenge in embracing the digital age is that, in the midst of rapid change, it’s very difficult to know where to place your bets. We do not yet know exactly what access to large volumes of social data will mean for our society. It certainly will not present a panacea for long–standing social problems; but it can add another dimension to our understanding of them.This report considers whether social media data can improve the quality and timeliness of the evidence base that informs public policy. Can the myriad of human connections and interactions on the web provide insight to enable government to develop better policy, understand its subsequent impact and inform the many different organisations that deliver public services?The report is based on an evaluation of available literature and interviews with 25 experts from a number of disciplines. Given that developments in this field are at such an early stage, it aims to provide helpful signposts rather than definitive answers

BIG DATA APPLICATIONS AND CHALLENGES IN GISCIENCE (CASE STUDIES: NATURAL DISASTER AND PUBLIC HEALTH CRISIS MANAGEMENT)

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic characteristics and the digital divide on social media engagement during such crises. In addressing the opioid crisis, the dissertation delves into the spatial dynamics of opioid overdose deaths, utilizing Multiscale Geographically Weighted Regression to discern local versus broader-scale determinants. This analysis foregrounds the necessity for targeted public health responses and the importance of localized data in crafting effective interventions, especially within communities that are ethnically diverse and economically disparate. Using Hurricane Irma as a case study, this dissertation analyzes social media activity in Florida in September 2017, leveraging Multiscale Geographically Weighted Regression to explore spatial variations in social media discourse, its correlation with damage severity, and the disproportionate impact on racialized communities. It integrates social media data analysis with political-ecological perspectives and spatial analytical techniques to reveal structural inequalities and political power differentials. The dissertation also tackles the dissemination of false information during the COVID-19 pandemic, examining Twitter activity in the United States from April to July 2020. It identifies misinformation patterns, their origins, and their association with the pandemic\u27s incidence rates. Discourse analysis pinpoints tweets that downplay the pandemic\u27s severity or spread disinformation, while spatial modeling investigates the relationship between social media discourse and disease spread. By concentrating on the experiences of racialized communities, this research aims to highlight and address the environmental and social injustices they face. It contributes empirical and methodological insights into effective policy formulation, with an emphasis on equitable responses to public health emergencies and natural disasters. This dissertation not only provides a nuanced understanding of crisis responses but also advances GIScience research by incorporating social media data into both traditional and critical analytical frameworks

Exploring, understanding, then designing: twitter users’ sharing behavior for minor safety incidents

Social media has become an integral part of human lives. Social media users resort to these platforms for various reasons. Users of these platforms spend a lot of time creating, reading, and sharing content, therefore, providing a wealth of available information for everyone to use. The research community has taken advantage of this and produced many publications that allow us to better understand human behavior. An important subject that is sometimes discussed and shared on social media is public safety. In the past, Twitter users have used the platform to share incidents, share information about incidents, victims and perpetrators, and used it to provide help in distressed locations after an attack or after a natural disaster. Public safety officials also used Twitter to disseminate information to maintain and improve safety and seek information from the crowds. The previous focus of the research is mainly on significant public safety incidents; but, incidents with less severity matter too. The focus of this dissertation is on minor incidents and the aim is to understand what motivates social media users to share those incidents to maintain and increase public safety through design suggestions.This dissertation is comprised of three completed studies. The first study attempts to understand motivations to share public safety incidents on social media under the collective action theory lens. Collective action theory assumes that rational people will not participate in a public good unless there is a special incentive or an external motivation for them. In this study, public safety is considered as the public good. This study tests people’s willingness to share incidents on social media if: the victim is someone they know, if the location of the incident is close, and if there is some coercion to influence users willingness to share. General support is found for the hypotheses and collective action theory.In the second study, the focus is on internal motivations that stem from being prosocial. An established scale that measures six different traits of prosocial behavior is used. It is hypothesizes that prosocial behavior is positively related to decisions to share incidents on social media. The study also tests other mediating variables, namely: following news outlets on Twitter, following public safety officials on social media, frequency of tweeting/retweeting. Partial support for prosocial tendencies effect on decisions to share is found. The study also discoveres that the three mediating variables (number of public safety official accounts followed, news exposure on social media, and tweet/retweet frequency) fully mediates the relationship and that they have a significant positive effect on decisions to share. The third and final study complements the previous two and helps conclude the previous findings. A 2X2X2 online experiment design is conducted. The three manipulations are the availability of location information, platform authority availability, and availability of sender authority. The study hypothesizes that the three interventions will produce a significant positive relationship with decisions to share on Twitter. It is found that location information has no effect on sharing minor incidents on Twitter, however, participants are more likely to use a fictitious button that increases local exposure to minor public safety tweets. It is also found that the authority of the sender has a significant effect on decisions to share. On the other hand, platform authority does not show an effect on decisions to share public safety incidents on Twitter