A TWITTER-INTEGRATED WEB SYSTEM TO AGGREGATE AND PROCESS EMERGENCY-RELATED DATA

A major challenge when encountering time-sensitive, information critical emergencies is to source raw volunteered data from on-site public sources and extract information which can enhance awareness on the emergency itself from a geographical context. This research explores the use of Twitter in the emergency domain by developing a Twitter-integrated web system capable of aggregating and processing emergency-related tweet data. The objectives of the project are to collect volunteered tweet data on emergencies by public citizen sources via the Twitter API, process the data based on geo-location information and syntax into organized informational entities relevant to an emergency, and subsequently deliver the information on a map-like interface. The web system framework is targeted for use by organizations which seek to transform volunteered emergency-related data available on the Twitter platform into timely, useful emergency alerts which can enhance situational awareness, and is intended to be accessible to the public through a user-friendly web interface. Rapid Application Development (RAD) is the methodology of choice for project development. The developed system has a system usability scale score of 84.25, after results were tabulated from a usability survey on 20 respondents. Said system is best for use in emergencies where the transmission timely, quantitative data is of paramount importance, and is a useful framework on extracting and displaying useful emergency alerts with a geographical perspective based on volunteered citizen Tweets. It is hoped that the project can ultimately contribute to the existing domain of knowledge on social media-assisted emergency applications

On the Feasibility of Social Network-based Pollution Sensing in ITSs

Intense vehicular traffic is recognized as a global societal problem, with a multifaceted influence on the quality of life of a person. Intelligent Transportation Systems (ITS) can play an important role in combating such problem, decreasing pollution levels and, consequently, their negative effects. One of the goals of ITSs, in fact, is that of controlling traffic flows, measuring traffic states, providing vehicles with routes that globally pursue low pollution conditions. How such systems measure and enforce given traffic states has been at the center of multiple research efforts in the past few years. Although many different solutions have been proposed, very limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks, in general, provide direct feedback from people and, as such, potentially very valuable information. A post that tells, for example, how a person feels about pollution at a given time in a given location, could be put to good use by an environment aware ITS aiming at minimizing contaminant emissions in residential areas. This work verifies the feasibility of using pollution related social network feeds into ITS operations. In particular, it concentrates on understanding how reliable such information is, producing an analysis that confronts over 1,500,000 posts and pollution data obtained from on-the- field sensors over a one-year span.Comment: 10 pages, 15 figures, Transaction Forma

Identifying Diversity, Equity, Inclusion, and Accessibility (DEIA) Indicators for Transportation Systems using Social Media Data: The Case of New York City during Covid-19 Pandemic

The adoption of transportation policies that prioritized highway expansion over public transportation has disproportionately impacted minorities and low-income people by restricting their access to social and economic opportunities and thus resulting in residential segregation. Policymakers, transportation researchers, planners, and practitioners have started acknowledging the need to build a diverse, equitable, inclusive, and accessible (DEIA) transportation system. Traditionally, this has been done through survey-based approaches that are time-consuming and expensive. While there is recent attention on leveraging social media data in transportation, the literature is inconclusive regarding the use of social media data as a viable alternative to traditional sources to identify the latent DEIA indicators based on public reactions and perspectives on social media. This study utilized large-scale Twitter data covering eight counties around the New York City (NYC) area during the initial phase of the Covid-19 lockdown to address this research gap. Natural language processing techniques were used to identify transportation-related major DEIA issues for residents living around NYC by analyzing their relevant tweet conversations. The study revealed that citizens, who had negative sentiments toward the DEIA of their local transportation system, broadly discussed racism, income, unemployment, gender, ride dependency, transportation modes, and dependent groups. Analyzing the socio-demographic information based on census tracts, the study also observed that areas with a higher percentage of low-income, female, Hispanic, and Latino populations share more concerns about transportation DEIA on Twitter

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Geospatial data analysis in Russia’s geoweb

The chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online

Electrified: What Twitter Data Can Tell Us About Public Energy Discussions

Researchers and policy-makers interested in assessing public communication to better inform the decision-making process are increasingly utilizing data harvested from social media. Twitter is one of the largest online sources of near-instantaneous information about a myriad of topics socially relevant in the public sphere. Renewable energy (RE) is a socially relevant topic that has emerged in recent years as a critical and contentious public policy issue. Yet little is known about whether RE discussions are happening on Twitter and if so whether any of that information would be valuable to decision-makers or for the policy-making process. This research – as a proof of concept – indicates there are a multitude of substantive, public discussions about renewable- and other forms of energy occurring on Twitter. These discussions vary by energy-related topic (e.g., solar, wind, etc.) and through time. We conclude the energy-related Twitter discussions provide both challenges and opportunities for researchers and policy-makers, yet may be important for understanding the public discourse and how it shapes or is shaped by the agenda-setting process

Mobilizing User-Generated Content For Canada’s Digital Advantage

Executive Summary: The goal of the Mobilizing User-Generated Content for Canada’s Digital Content Advantage project is to define User-Generated Content (UGC) in its current state, identify successful models built for UGC, and anticipate barriers and policy infrastructure needed to sustain a model to leverage the further development of UGC to Canada\u27s advantage. At the outset, we divided our research into three domains: creative content, small scale tools and collaborative user-generated content. User-generated creative content is becoming increasingly evident throughout the technological ecology through online platforms and online social networks where individuals develop, create and capture information and choose to distribute content through an online platform in a transformative manner. The Internet offers many tools and resources that simplify the various UGC processes and models. Social networking sites such as Facebook, Twitter, YouTube, Vimeo, Flickr and others provide functionality to upload content directly into the site itself, eliminating the need for formatting and conversion, and allowing almost instantaneous access to the content by the user’s social network. The successful sites have been able to integrate content creation, aggregation, distribution, and consumption into a single tool, further eroding some of the traditional dichotomies between content creators and end-users. Along with these larger scale resources, this study also treats small scale tools, which are tools, modifications, and applications that have been created by a user or group of users. There are three main categories of small scale tools. The first is game modifications, or add-ons, which are created by users/players in order to modify the game or assist in its play. The second is modifications, objects, or tools created for virtual worlds such as Second Life. Third, users create applications and tools for mobile devices, such as the iPhone or the Android system. The third domain considers UGC which is generated collaboratively. This category is comprised of wikis, open source software and creative content authored by a group rather than a sole individual. Several highly successful examples of collaborative UGC include Wikipedia, and open source projects such as the Linux operating system, Mozilla Firefox and the Apache platform. Major barriers to the production, distribution and aggregation of collaborative UGC are unduly restrictive intellectual property rights (including copyrights, licensing requirements and technological protection mechanisms). There are several crucial infrastructure and policies required to facilitate collaborative UGC. For example, in the area of copyright policy, a careful balance is needed to provide appropriate protection while still allowing downstream UGC creation. Other policy considerations include issues pertaining to technological protection mechanisms, privacy rights, consumer protection and competition. In terms of infrastructure, broadband internet access is the primary technological infrastructure required to promote collaborative UGC creation. There has recently been a proliferation of literature pertaining to all three of these domains, which are reviewed. Assessments are made about the most effective models and practices for each domain, as well as the barriers which impede further developments. This initial research is used as a basis for generating some tentative conclusions and recommendations for further research about the policy and technological infrastructures required to best mobilize and leverage user-generated content to create additional value in the digital economy internal and external to Canada. Policy recommendations based on this research focus on two principles: balancing the interest of both content owners and users, and creating an enabling environment in which UGC production, distribution, aggregation, and re-use can flourish

Real-time road traffic events detection and geo-parsing

Indiana University-Purdue University Indianapolis (IUPUI)In the 21st century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis. Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents. Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis. Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture