TEXT AND IMAGE CONTEXTUAL INCONGRUENCIES IN CLIMATE MISINFORMATION ON SOCIAL MEDIA: A NEURO-IS APPROACH
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Akinyemi, John-Patrick↗
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Detecting Emergent Discourse Dynamics in Online Social Media
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Doyle, Casey Lane↗
Detecting Emergent Discourse Dynamics in Online Social Media
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Sweitzer, Matthew Donald↗
Monsters in the Closet - A Case Study of Internal Social Media Implementation at Marshall Space Flight Center
No abstract available
Buzzell, Bernadette↗
Marrying Social Media Approaches and Space Flight Control - Eight Years at SpaceOps
This paper shows how Marshall Space Flight Center's (MSFC) ISS Payload Operations Integration Center (POIC) is realizing the above goals via a) enhancements to the Console Log Tool (CoLT) first deployed in 2012 and b) the Communications Dashboard (CommDash) software suite deployed in 2017. In addition to technical discussion, the paper chronicles CommDash's role in emergence of an Agile development process and a Human Factors community of practice within MSFC's Payload Mission Operations Division (PMOD).
Scott, D. W.↗
Marrying Social Media Approaches and Space Flight Control: Eight Years at SpaceOps
No abstract available
Scott, David W.↗
“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↗
Using Facebook to Recruit Urban Participants for Smartphone-Based Travel Surveys
Social media has become an integral part of everyday life for many individuals, serving as a platform to express opinions, share memories and lifestyles, follow news, and adapt to social trends and norms. The wealth of user information and analytics on these platforms has facilitated the development and sale of tailored products and services, benefiting advertisers and researchers seeking survey participants. Social media advertising has demonstrated its effectiveness in reaching hard-to-reach populations. However, transport researchers have yet to capitalise on this potential fully. This paper presents our experience using social media to recruit participants for two smartphone travel surveys conducted in Australia. We demonstrate that social media recruitment and smartphone-based travel surveys are highly effective, adaptable, and can be rapidly deployed in response to research opportunities, such as during the early phase of the COVID-19 pandemic when traditional methods may be less suitable. This approach also holds great potential for travel surveys targeting the general population. This paper shares several lessons from this experiment, including our administrative approach and detailed technical instructions to utilise open-source software tools for conducting smartphone travel surveys like ours. This approach significantly reduces study costs compared to most commercial solutions.
97 MATHEMATICS AND COMPUTING↗
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.↗
Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado
The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.
54 ENVIRONMENTAL SCIENCES↗
Behavior Change in Response to Subreddit Bans and External Events
As more people flock to social media to connect with others to form virtual communities, it is important to understand how members of these groups interact to understand human behavior on the Web. In response to the rise in hate speech, harassment and other antisocial behavior many social media companies have implemented different content and user moderation policies. On Reddit, for example, communities, \ie, subreddits, are occasionally banned for violating these policies. We study the effect of these regulatory actions as well as when a community experiences a significant external event like an election or a market crash. Overall, we find that subreddit bans prompt a small, but statistically significant, number of active users to leave the platform or change their posting behavior; the effect of external events varies with the type of event. We conclude with a discussion on the effectiveness of the bans and wider implications for the online content moderation.
60 APPLIED LIFE SCIENCES↗
AI-based Cyber Event OSINT via Twitter Data
Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes.
Dale, Dakota↗
Open Source Intelligence for Cybersecurity Events via Twitter Data
Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain insight into ongoing events and helping them prioritize vulnerabilities to deal with. To give examples of the possible uses, we present three case studies demonstrating the event discovery and investigation processes. We also examine the potential of OSINT for identifying the network protocols associated with specific events, which can aid in the mitigation procedures by informing operators if the vulnerability is exploitable given their system’s network configurations.
Dale, Dakota↗
Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment
Social media has enabled a new era of manipulation in the information and cognitive domains. Deceptive content—misleading, falsified, and fabricated—is routinely created and spread in the modern social media environment with the intent to create confusion and widen political and social divides, and exploit the societal conflict exacerbated by these divides in the real-world (aka physical domain). Such disinformation campaigns demonstrate a threat to the integrity of economic, political, cultural, public health, and national security institutions around the world. In this work we overview our artificial intelligence (AI) capabilities to detect, describe, and defend against information operations on Twitter as an example social platform to understand the influence of misleading and falsified content diffusion and better enable those charged with defending against such manipulation to enable responsive parties to counter it. We first present novel linguistically-informed deep learning (DL) models for misinformation and disinformation detection, and present an in-depth linguistic analysis of psycho-linguistic markers across broad deception categories. We then demonstrate how our models perform in the multilingual and multimodal setting and categorize falsified and misleading content based on the intent to deceive. We also provide a large-scale analysis to describe user behavior and spread patterns while engaging with deceptive content and report novel findings about the immediate diffusion of deceptive content by characterizing the vulnerable sub-populations and their demographics, and explicitly measuring speed and scale of deception spread to uncover who shares deceptive content, how quickly, how much, and how evenly. In addition, we measure audience reactions to misinformation and disinformation at scale, distinguishing the reactions of users identified as bots versus humans. Finally, we take advantage of deep translation and generation models to create unique solutions for real-time defense against digital deception and discuss how to apply causal inference to prescribe and intervene into strategic communications jointly across information, cognitive, and physical domains.
artificial intelligence, deep learning, neural lan↗
Improving Earth Science Data Literacy
The improvement of scientific literacy across the public can take on a myriad of forms and activities. The vast archives of NASA’s Earth science data, as one example, reflect the agency’s ongoing commitment to producing the highest quality data, services and tools intended for enabling the public to understand the complexity of Earth systems. The focus of this presentation is to explore and reveal the specific strategies the NASA Earth Science Data and Information System (ESDIS) Project uses to support our very large and diverse user communities access to and understanding of these science data and services. The diversity of our subject matter, spanning the whole Earth science domain, is mirrored by our user communities which are equally as broad in their needs and abilities. ESDIS and the supporting Communication Team leverages an array of internet-based tools and communication strategies to both understand actual user needs and develop optimum pathways for improving our users’ knowledge and understanding of the data. We will focus our limited time on a few of the high-impact activities including Webinars, or video-based, on-line, interactive discussions and presentations that since their introduction in 2013 have become a staple for user-instructor interaction via the internet. Here we discuss the types of content we produce and many lessons learned on how best to use this medium for improving user literacy on the subject at hand. In addition, we include discussion of our approach to using various social media platforms and tools to bolster the awareness of our users for data and services that are likely of interest. The ESDIS project remains active in various social media campaigns where our particular blend of science, data and tools engages new users curious of how to get their science or application started using NASA-available data. We anticipate that these examples may prove to be novel in their application to improving science literacy and skill development among the public.
Lindsay, Francis↗
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.↗
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↗