Do I Care Enough? Using a Prosocial Tendencies Measure to Understand Twitter Users Sharing Behavior for Minor Public Safety Incidents

Social media has been used to assist victims of crises, especially large-scale disasters. Research describes the importance of the crowd who are the first witnesses to any sort of crime or disaster. Among others, this paper focuses on smaller scale public safety incidents such as suspicious activities, and minor robberies. We investigate whether prosocial tendencies affect Twitter users’ decisions to share minor public safety incidents on Twitter. The scale used has six subscales including: public, anonymous, dire, emotional, compliant, and altruism. The data (N=363) was collected through Mechanical Turk using an online anonymous survey. Initial results showed a positive relationship between being prosocial and sharing public safety incidents on Twitter. However, once additional variables related to Twitter use were introduced (number of public safety official accounts followed, news exposure on social media, and tweet/retweet frequency), these variables fully mediated the relationship. Limitations and design implications are discussed

Big Data Analytics in Humanitarian and Disaster Operations: A Systematic Review

By the outset of this review, 168 million people needed humanitarian aid, and the number grew to 235 million by the end of the completion of this review. There is no time to lose, definitely no data to lose. Humanitarian relief is crucial not just to contend with a pandemic once a century but also to provide help during civil conflicts, ever-increasing natural disasters, and other forms of crisis. Reliance on technology has never been so relevant and critical than now. The creation of more data and advancements in data analytics provides an opportunity to the humanitarian field. This review aimed at providing a holistic understanding of big data analytics in a humanitarian and disaster setting. A systematic literature review method is used to examine the field and the results of this review explain research gaps, and opportunities available for future research. This study has shown a significant research imbalance in the disaster phase, highlighting how the emphasis is on responsive measures than preventive measures. Such reactionary measures would only exacerbate the disaster, as is the case in many nations with COVID-19. Overall this research details the current state of big data analytics in a humanitarian and disaster setting

RAMPVIS: Answering the challenges of building visualisation capabilities for large-scale emergency responses

The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses

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

Context-Preserving Visual Analytics of Multi-Scale Spatial Aggregation.

Spatial datasets (i.e., location-based social media, crime incident reports, and demographic data) often exhibit varied distribution patterns at multiple spatial scales. Examining these patterns across different scales enhances the understanding from global to local perspectives and offers new insights into the nature of various spatial phenomena. Conventional navigation techniques in such multi-scale data-rich spaces are often inefficient, require users to choose between an overview or detailed information, and do not support identifying spatial patterns at varying scales. In this work, we present a context-preserving visual analytics technique that aggregates spatial datasets into hierarchical clusters and visualizes the multi-scale aggregates in a single visual space. We design a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates and explore visual encoding strategies including color, transparency, shading, and shapes, in order to illustrate the hierarchical and statistical patterns of the multi-scale aggregates. We also propose a transparency-based technique that maintains a smooth visual transition as the users navigate across adjacent scales. To further support effective semantic exploration in the multi-scale space, we design a set of text-based encoding and layout methods that draw textual labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each scale, but also indicates the spatial coverage of the aggregates and their hierarchical relationships. We demonstrate the effectiveness of the proposed approaches through real-world application examples and user studies

Ella Mills and Deliciously Ella on Instagram: The evolution of a successful entrepreneur and wellness brand

This thesis investigates from a visual social media studies perspective the increasingly popular practices of a highly active online actor, the influential plant-based food advocate and successful entrepreneur Ella Mills. Its aim is to document and analyse some ways in which popular Instagram visual narratives have contributed to the formation of plant-based eating practices, wellness discourses and lifestyle choices in the UK. The analysis proceeds in two steps. First, I use a combination of mixed methods to analyse 4,000 Instagram images from the Deliciously Ella account and their relevant captions from 17 January 2013 to 24 October 2018. The deliberate choice of a lengthy time frame involves a novel visual social media approach called “Comparative Instachronics”, which offers a new way of dealing with a single Instagram account. Second, I provide a deeper analysis of my visual sample by using several conceptual approaches such as a) authenticity, b) class, inequality and distinction, c) enrichment. Interpretation of the sample using these concepts allowed me to make an important discovery early enough in the research process: plant-based food, though integral to the nature of the Deliciously Ella business, is not the leading visual cue. On the contrary, food’s significance is mediated by other important factors, such as Mills’ personal health story and details of her family and personal life and those of people close to her. The central finding of this thesis is that the elements surrounding the visual imagery of plant-based food function as essential facilitators of the Deliciously Ella project and the brand’s evolution across time. By going beyond the visual material to analyse social media images as distinct items through lengthy periods of time, the thesis makes an original methodological contribution to visual social media studies