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NASA NTRS2010

Sun-Earth Day: Reaching the Education Audience by Informal Means

For ten years the Sun-Earth Day program has promoted Heliophysics education to ever larger audiences through events centered on attractive annual themes. What originally started out as a one day event quickly evolved into a series of programs and events that occur throughout the year culminating with a celebration on or near the Spring Equinox. The events are often formal broadcasts or webcasts seeking to convey the science behind the latest solar-terrestrial mission discoveries. This has been quite successful, but it is clear that the younger generation increasingly depends on social networking approaches and informal news transmission for learning what is happening in the world around them. For 2010, the Sun-Earth Day team put emphasis on using informal approaches to bring the theme to the audience. The main event, a webcast from the NASA booth at the National Science Teachers Association (NSTA) annual meeting by the NASA EDGE group, took a lighthearted and offbeat approach to interviewing scientists and educators about Heliophysics news. NASA EDGE programs are unscripted and unpredictable, and that represents a different approach to getting the message across. The webcast was supplemented by a number of social networking avenues. The Sun-Earth Day program explored a wide range of social media applications including Facebook, Twitter, NING, podcasting, iPhone apps, etc. Each of these offers unique and effective methods to promote Heliophysics content and mission related highlights. The facebook site was quite popular and message posting there told the Sun-Earth Day story piece by piece. The same could be said of twittering and the tweetup held at the NSTA site. Has all of this been effective? Results are still being gathered, but anecdotal responses from the world seem very positive. What other methods might be used in the future to bring the science to a personal hands-on, interactive experience? Outcomes: Participants will: (1) Be introduced to the Sun-Earth Day program and its evolution through a decade of programs; (2) Hear about the methods used to communicate and educate through the years and how well they have worked; and (3) Be acquainted with the latest usage of social networking and informal education approaches and how well they have worked

Thieman, J.↗
NASA NTRS2010

Stennis Space Center Salinity Drifter Project. A Collaborative Project with Hancock High School, Kiln, MS

Presentation topics include: importance of salinity of coastal waters, habitat switching algorithm, habitat switching module, salinity estimates from Landsat for Sabine Calcasieu Basin, percent of time inundated in 2006, salinity data, prototyping the system, system as packaged for field tests, salinity probe and casing, opening for water flow, cellular antenna used to transmit data, preparing to launch, system is launched in the Pearl River at Stennis Space Center, data are transmitted to Twitter by cell phone modem every 15 minutes, Google spreadsheet I used to import the data from the Twitter feed and to compute salinity (from conductivity) and display charts of salinity and temperature, results are uploaded to NASA's Applied Science and Technology Project Office Webpage.

Kalcic, Maria↗
NASA NTRS2015

When the Sky Falls: Performing Initial Assessments of Bright Atmospheric Events

The 2013 Chelyabinsk super bolide was the first "significant" impact event to occur in the age of social media and 24 hour news. Scientists, used to taking many days or weeks to analyze fireball events, were hard pressed to meet the immediate demands (within hours) for answers from the media, general public, and government officials. Fulfilling these requests forced many researchers to exploit information available from various Internet sources - videos were downloaded from sites like Youtube, geolocated via Google Street View, and quickly analyzed with improvised software; Twitter and Facebook were scoured for eyewitness accounts of the fireball and reports of meteorites. These data, combined with infrasound analyses, enabled a fairly accurate description of the Chelyabinsk event to be formed within a few hours; in particular, any relationship to 2012 DA14 (which passed near Earth later that same day) was eliminated. Results of these analyses were quickly disseminated to members of the NEO community for press conferences and media interviews. Despite a few minor glitches, the rapid initial assessment of Chelyabinsk was a triumph, permitting the timely conveyance of accurate information to the public and the incorporation of social media into fireball analyses. Beginning in 2008, the NASA Meteoroid Environments Office, working in cooperation with Western's Meteor Physics Group, developed processes and software that permit quick characterization - mass, trajectory, and orbital properties - of fireball events. These tools include automated monitoring of Twitter to establish the time of events (the first tweet is usually no more than a few seconds after the fireball), mining of Youtube and all sky camera web archives to locate videos suitable for analyses, use of Google Earth and Street View to geolocate the video locations, and software to determine the fireball trajectory and object orbital parameters, including generation of animations suitable for popular media and presentations. This presentation will give a short description of the characterization procedure and show applications of the tools, which have become vital to answering the question of "What was that bright light in the sky?" in the post-Chelyabinsk, 24/7 news world.

Cooke, William J.↗
DOE OSTI2021

“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study

Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to non-misinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will

5g↗
DOE OSTI2020

Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication

While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence; (ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure; and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors.

Gunaratne, Chathika↗
DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

User Role Identification in Software Vulnerability Discussions over Social Networks

Understanding and early awareness of software vulnerabilities is vital for preventing and mitigating potential impacts from cybersecurity events. One step toward early characterization of software vulnerabilities may involve analyzing discussion and spread of information in online social networks. Prior work has used information from such discussions over multiple online forums to develop dynamic networks among users followed by analysis of structure, spread, and information evolution. In this work, we advance the state-of-the-art by focusing on data-driven learning of types, roles, and transition of roles exhibited by users over time. In social networks, users take on particular roles based on their actions and structure of the network. Identifying “meaningful” roles can help separate potential users of interest from the larger community, and identify patterns in a network. We will identify and compare roles found in online forums (e.g., Twitter) using techniques such as feature-based Non-negative Matrix Factorization coupled with topological and influence-based measures of centrality. Since users’ activities change over time, we also analyze role evolution in dynamic networks.

Jones, Rebecca D.↗
DOE OSTI2021

Donald J. Trump’s Presidency in Cyberspace: A Case Study of Social Perception and Social Influence in Digital Oligarchy Era

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.

digital oligarchy↗
DOE OSTI2024

Generating Euler Diagrams Through Combinatorial Optimization

Abstract Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP‐hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity‐constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non‐trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.

Computer Science↗
DOE OSTI2022

Machine Learning Modeling Pipeline for Extracting Nuclear Proliferation Events of Interest from Open Data Sources (U)

In FY2020, the Savannah River National Laboratory (SRNL) and the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Polytechnic Institute and State University entered a collaboration funded by Department of Energy’s (DOE) Office of Defense Nuclear Nonproliferation Research and Development. The project’s mission was to take the first steps toward developing a demonstration prototype system that uses multiple machine learning and data analytics methods on largescale open data sources to identify new, developing, and/or undeclared nuclear programs. Given the SRNL team’s on-site perspective of events culminating in the DOE’s decision to pursue the Savannah River Plutonium Processing Facility (SRPPF), the team targeted the identification of events and indicators in retrospective datasets that pointed to the activity of “fissile core fabrication at the Savannah River Site” prior to the official announcement in May of 2018. A preliminary modeling pipeline was developed in FY20 that showed the datasets contained adequate signal for continuation of efforts. In FY21, a modular demonstration prototype modeling pipeline has continued in development for two text-based data sources: a broad internet archive (Webhose Ltd.) and a decahose Twitter database (i.e., a global sampling of one in every ten Tweets). The techniques that have been developed rely on graph theory and anomaly detection to identify contextual shifts in key words and phrases at various points in time such that indicators of events of interest could be identified and subsequently, events could be extracted from the corpuses. The foundational concept behind the approaches is that contextual shifts in key words and phrases can act as indicators of events of interest. Both datasets have proven successful in extracting events of interest related to pit production at the Savannah River Site prior to the official announcement. In addition, the pipelines have generated a wide range of events broadly summarized as: the awarding of DOE contracts at major sites, DOE investments in various programs, accidents at DOE national laboratories, speculations about the fate of pit production in the DOE complex, domestic and international shipments and receipts of nuclear materials at DOE sites, termination of non-proliferation agreements with Russia, termination of MOX, new weapons development approvals/testing, nuclear posture reviews, major DOE cleanup/production milestones, political opinions, and nuclear watch groups’ opinions, among many others.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

National Energy Technology Laboratory Resident Research Associateship and Methane Hydrate Fellowship Program

The promotional schedule to advertise the NRC Research Associateship Programs included the following: 1) attending meetings of major scientific and engineering professional societies; 2) advertising in society meeting programs as well as their online career centers; 3) emailing program announcements to the heads of appropriate science and engineering departments of U.S. academic institutions that grant doctoral degrees in STEM fields; 4) posting announcements on online job and career sites and ResearchGate; 5) advertising print ads in Science magazine; and 6) maintaining a presence on the program’s Twitter and Facebook pages.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

There's Enough Space for Everyone

Only a few fortunate people have the opportunity to go into space and experience the wonders of our universe first-hand. But thanks to social media and virtual worlds, many unique opportunities exist for us to learn, explore and experience what s out there from wherever we are. NASA and Marshall Space Flight Center (MSFC) are making this even easier to do. From blogs to Twitter messages, from Facebook pages to Flickr Photo sites, NASA is leveraging social media to share never-before-seen footage and inside information on spaceflight, scientific discoveries and other space activities. Over the last year, Marshall has reached more than half-a-million viewers through its high-profile, real-time blogs. Through its Watching a Launch blog, visitors were invited to take the "virtual" rides of their lives as they received a first-hand account of seeing a powerful shuttle launch - up close and personal. Through the Shuttle Ferry Flight blog, they got to experience the Shuttle Atlantis journey home to Kennedy Space Center as it rode "piggyback" on a modified Boeing 747 airplane. This year, Marshall s Flickr photo galleries have been visited over 700,000 times, Ares rocket videos have been viewed on iTunes, YouTube, TeacherTube and NASA Web sites more than 1.2 million times, and Marshall s Facebook Page has over 2,800 "friends" who regularly follow NASA. Social media tools have been a powerful way to reach and inspire the public, but NASA has also used these tools effectively to promote education and outreach. From events such as the Great Moonbuggy Race to the Student Launch Initiative, Marshall has used social networks to interest, excite and engage students. This presentation shares some of NASA s experiences on what has worked . . . and what hasn't . . . and seeks to spread the message that through social media "there's enough space for everyone."

Schumacher, Daniel M.↗
NASA NTRS2009

Use of Web 2.0 Technologies for Public Outreach on a Simulated Mars Mission

Recent advances in social media and internet communications have revolutionized the ways people interact and disseminate information. Astronauts are already taking advantage of these tools by blogging and tweeting from space, and almost all NASA missions now have presences on the major social networking sites. One priotity for future human explorers on Mars will be communicating their experiences to the people back on Earth. During July 2009, a 6-member crew of volunteers carried out a simulated Mars mission at the Flashline Mars Arctic Research Station (FMARS). The Mars Society built the mock Mars habitat in 2000-01 to help develop key knowledge and inspire the public for human Mars exploration. It is located on Devon island about 1600 km from the North Pole within the Arctic Circle. The structure is situated on the rim of Haughton Crater in an environment geologically and biologically analogous to Mars. Living in a habitat, conducting EVAs wearing spacesuits, and observing communication delays with "Earth,"the crew endured restrictions similar to those that will be faced by future human Mars explorers. Throughout the expedition, crewmembers posted daily blog entries, reports, photos, videos, and updates to their website and social media outlets Twitter, Facebook, YouTube, and Picasa Web Albums. During the sixteen EVAs of thier field science research campaign, FMARS crewmembers collected GPS track information and took geotagged photos using GPS-enabled cameras. They combined their traverse GPS tracks with photo location information into KML/KMZ files that website visitors can view in Google Earth.

Ferrone, Kristine↗
NASA NTRS2010

Securing Social Media

This slide presentation reviews some issues regarding the use of social media (e.g., Facebook, and Twitter) and security

Jones, Kevin D.↗
NASA NTRS2010

Sun-Earth Day - Teaching Heliophysics Through Education Technology

Sun-Earth Day (SED) is an Education and Outreach program supported by the U.S, National Aeronautics and Space Administration (NASA). The intent of the program is to teach students and the general public about Heliophysics (the science of the study of the Sun, how it varies, and how solar dynamics affect the rest of the solar system, especially the Earth). The program was begun ten years ago. Each year since that time a particular day has been designated as "Sun-Earth Day ,,. Usually the day of the spring equinox (March 20 or 21) is Sun-Earth Day, but other days have been used as well. Each year a theme is chosen relating to Heliophysics and events reflecting that theme are planned not only for Sun-Earth Day, but for the entire year. From the very beginning educational technology was emphasized in the events in order to effectively reach wide audiences with the SED message. The main approach has been to have a "webcast" related to each year's theme, often from a location that supports the theme as well. For example, a webcast took place from the Mayan pyramids at Chichen Itza, Mexico to highlight the theme of "Ancient Observatories, Timeless Knowledge". Webcasts were not the only technology employed, however. Many of the themes centered on the dynamic nature of the Sun and the effects that solar storms can have on interplanetary space and in our day-to-day life on Earth. Activities for tracking when solar storms happen and how they affect the Earth were developed and brought together in an educational package called Space Weather Action Centers. This project is explained in more detail in another presentation in this session being given by Norma Teresinha Oliveira Reis. Recent Sun-Earth Days have utilized "social networking" technologies to reach widespread groups on the internet. Podcasts, Vodcasts, Facebook, Twitter, and Second Life are the types of network technologies being employed now. The NASA Distance learning Network is another method for bringing Sun-Earth Day events and training to widespread educators and classrooms in order to magnify the reach of Sun-Earth Day. Examples of the technologies will be shown along with an assessment of their effectiveness.

Thieman, J.↗
NASA NTRS2011

Sharing the Adventure with the Public - The Value of Excitement: Summary of a Workshop

The premise of the workshop was that NASA and its associated science and exploration communities have not been as effective as they could be in communicating with the public about what NASA does or how its activities contribute to resolving critical problems on Earth. Although not explicitly stated, an underlying assumption seemed to be that if the public had a better understanding, it would be more supportive of NASA, which in turn could generate more political support for the organization. In the case of global climate change, the broader issue is how to convince the public of the magnitude of the problem and the need for solutions. The role of new social media tools like Facebook and Twitter in interacting with the public was an integral part of the discussion.

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