Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Social Media”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Proceedings for Lunch and Learn: Making science fun and exciting through social media

Social media channels are vital for outreach and offer huge opportunities for scientists to directly engage with the public using nontraditional methods – including lots of creativity and humor. The physics community’s presence is growing more significant, and this session (designed for early career researchers) provided a lively discussion with experts in the domain. We covered how to best use social media to raise public awareness of science, share excitement and progress, and cultivate support from followers. We also discussed some of the thornier issues in social media, such as capturing the complexity of both the scientific process and the science itself.

Biron, Lauren↗
DOE OSTI2015

Social Sensor Analytics: Making Sense of Network Models in Social Media

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013 and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.

event detection↗
DOE OSTI2017

Studying Military Community Health, Well-being, and Discourse through the Social Media Lens

Social media can provide a resource for characterizing communities and targeted populations through activities and content shared online. For instance, studying the armed forces’ use of social media may provide insights into their health and wellbeing. In this paper, we address three broad research questions: (1) How do military populations use social media? (2) What topics do military users discuss in social media? (3) Do military users talk about health and well-being differently than civilians? Military Twitter users were identified through keywords in the profile description of users who posted geotagged tweets at military installations. These military tweets were compared with the tweets from remaining population. Our analysis indicate that military users talk more about military related responsibilities and events, whereas non-military users talk more about school, work, and leisure activities. A significant difference in online content generated by both populations was identified, involving sentiment, health, language, and social media features.

healthcare analytics↗
DOE OSTI2016

Discourse, Health and Well-being of Military Populations through the Social Media Lens

Social media can provide a resource for characterizing communities and small populations through activities and content shared online. For instance, studying the language use in social media within military populations may provide insights into their health and wellbeing. In this paper, we address three research questions: (1) How do military populations use social media? (2) What do military users discuss in social media? And (3) Do military users talk about health and well-being differently than civilians? Military Twitter users were identified through keywords in the profile description of users who posted geo-tagged tweets at military installations. The data was anonymized for the analysis. User profiles that belong to military population were compared to the nonmilitary population. Our results indicate that military users talk more about events in their military life, whereas nonmilitary users talk more about school, work, and leisure activities. We also found that the online content generated by both populations is significantly different, including health-related language and communication behavior.

analytics↗
DOE OSTI2015

Grandmaster: Interactive text-based analytics of social media

People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Predicting Foreign Language Usage from English-Only Social Media Posts

Social media is known for its multicultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 23 thousand multilingual users speaking 11 other languages besides English. We rely on this multilingual corpus to build predictive models for a novel task – inferring non- English languages that users speak exclusively from their English tweets. We contrast the predictive power of different linguistic signals and report that lexical content and syntactic structure of English tweets are the most predictive of non-English languages that users speak on Twitter. By analyzing cross-lingual transfer – the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 11 languages.

code-swicthing↗
DOE OSTI2018

Social media based NPL system to find and retrieve ARM data: Concept paper

Information connectivity and retrieval has a role in our daily lives. The most pervasive source of online information is databases. The amount of data is growing at rapid rate and database technology is improving and having a profound effect. Almost all online applications are storing and retrieving information from databases. One challenge in supplying the public with wider access to informational databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it may not be practical to make the public aware of the structure of the database. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide more intuitive method for generating database queries and delivering responses. Social media makes it possible to interact with a wide section of the population. Through this medium, and with the help of Natural Language Processing (NLP) we can make the data of the Atmospheric Radiation Measurement Data Center (ADC) more accessible to the public. We propose an architecture for using Apache Lucene/Solr [1], OpenML [2,3], and Kafka [4] to generate an automated query/response system with inputs from Twitter5, our Cassandra DB, and our log database. Using the Twitter API and NLP we can give the public the ability to ask questions of our database and get automated responses.

Devarakonda, Ranjeet↗
DOE OSTI2015

Global Social Media Directory. A Resource Guide

Social media platforms are internet-based applications focused on broadcasting user-generated content. While primarily web-based, these services are increasingly available on mobile platforms. Communities and individuals share information, photos, music, videos, provide commentary and ratings/reviews, and more. In essence, social media is about sharing information, consuming information, and repurposing content. Social media technologies identified in this report are centered on social networking services, media sharing, blogging and microblogging. The purpose of this Resource Guide is to provide baseline information about use and application of social media platforms around the globe. It is not intended to be comprehensive as social media evolves on an almost daily basis. The long-term goal of this work is to identify social media information about all geographic regions and nations. The primary objective is that of understanding the evolution and spread of social networking and user-generated content technologies internationally.

blogs↗
DOE OSTI2021

Prediction of social media postings as trusted news or as types of suspicious news

Disclosed are systems, techniques, and non-transitory storage media for predicting social media postings as being trusted news or a type of suspicious news. The systems, techniques, and non-transitory storage media are based on unique neural network architectures that learn from a combined representation including at least representations of social media posting content and a vector representation of communications among connected users.

Volkova, Svitlana↗
DOE OSTI2017

ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media

Analyzing and visualizing large amounts of social media communications and contrasting short-term conversation changes over time and geo-locations is extremely important for commercial and government applications. Earlier approaches for large-scale text stream summarization used dynamic topic models and trending words. Instead, we rely on text embeddings – low-dimensional word representations in a continuous vector space where similar words are embedded nearby each other. This paper presents ESTEEM,1 a novel tool for visualizing and evaluating spatiotemporal embeddings learned from streaming social media texts. Our tool allows users to monitor and analyze query words and their closest neighbors with an interactive interface. We used state-of- the-art techniques to learn embeddings and developed a visualization to represent dynamically changing relations between words in social media over time and other dimensions. This is the first interactive visualization of streaming text representations learned from social media texts that also allows users to contrast differences across multiple dimensions of the data.

social media dynamics↗
DOE OSTI2015

Using social media for actionable disease surveillance and outbreak management. A systematic literature review

Here, research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions: 1) Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health? 2) Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes? Examples of social media included are Facebook, MySpace, microblogs (e.g., Twitter), blogs, and discussion forums. For Question 1, 33 manuscripts were identified, starting in 2009 with topics on Influenza-like Illnesses (n=15), Infectious Diseases (n = 6), Non-infectious Diseases (n=4), Medication and Vaccines (n=3), and Other (n=5). For Question 2, 32 manuscripts were identified, the first in 2000 with topics on Health Risk Behaviors (n=10), Infectious Diseases (n = 3), Non-infectious Diseases (n=9), and Other (n=10). The literature on the use of social media to support public health practice has identified many gaps and biases in current knowledge. Despite the potential for success identified in exploratory studies, there are limited studies on interventions and little use of social media in practice. However, information gleaned from the articles demonstrates the effectiveness of social media in supporting and improving public health and in identifying target populations for intervention. A primary recommendation resulting from the review is to identify opportunities that enable public health professionals to integrate social media analytics into disease surveillance and outbreak management practice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns, emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2013

Internal Social Media at Marshall Space Flight Center - An Engineer's Snapshot

In the brief span of about six years (2004-2010), social media radically enhanced people's ways of maintaining recreational friendships. Social media's impact on public affairs (PAO) and community engagement is equally striking: NASA has involved millions of non-NASA viewers in its activities via outward-facing social media, often in a very two-way street fashion. Use of social media as an internal working tool by NASA's tens of thousands of civil servants, onsite contractor employees, and external stakeholders is evolving more slowly. This paper examines, from an engineer's perspective, Marshall Space Flight Center s (MSFC) efforts to bring the power of social media to the daily working environment. Primary emphasis is on an internal Social Networking Service called Explornet that could be scaled Agency-wide. Other topics include MSFC use of other social media day-to-day for non-PAO purposes, some specialized uses of social techniques in space flight control operations, and how to help a community open up so it can discover and adopt what works well.

Scott, David W.↗
DOE OSTI2014

Using Social Media for Biosurveillance: Gap between Research and Action

There is a significant body of literature on the use of social media for biosurveillance. However, less is known about the impact of integrating social media into public health practice, and resulting interventions. This motivated the ISDS Social Media for Disease Surveillance Workgroup to conduct a systematic literature review on the use of social media for actionable disease surveillance. Based on the preliminary results, there is little published literature on this topic, possibly suggesting that it is particularly challenging to translate research using social media for disease surveillance into practice.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

EXAMINING THE ROLE AND RESEARCH CHALLENGES OF SOCIAL MEDIA AS A TOOL FOR NONPROLIFERATION AND ARMS CONTROL TREATY VERIFICATION

Traditional arms control treaty verification activities typically involve a combination of technical measurements via physical and chemical sensors, state declarations, political agreements, and on-site inspections involving international subject matter experts. However, the ubiquity of the internet, and the electronic sharing of data that it enables, has made available a wealth of open source information with the potential to benefit verification efforts. Open source information is already being used by organizations such as the International Atomic Energy Agency to support the verification of state-declared information, prepare inspectors for in-field activities, and to maintain situational awareness . The recent explosion in social media use has opened new doors to exploring the attitudes, moods, and activities around a given topic. Social media platforms, such as Twitter, Facebook, and YouTube, offer an opportunity for individuals, as well as institutions, to participate in a global conversation at minimal cost. Social media data can also provide a more data-rich environment, with text data being augmented with images, videos, and location data. The research described in this paper investigates the utility of applying social media signatures as potential arms control and nonproliferation treaty verification tools and technologies, as determined through a series of case studies. The treaty relevant events that these case studies touch upon include detection of undeclared facilities or activities, determination of unknown events recorded by the International Monitoring System (IMS), and the global media response to the occurrence of an Indian missile launch. The case studies examine how social media can be used to fill an information gap and provide additional confidence to a verification activity. The case studies represent, either directly or through a proxy, instances where social media information may be available that could potentially augment the evaluation of an event. The goal of this paper is to instigate a discussion within the verification community as to where and how social media can be effectively utilized to complement and enhance traditional treaty verification efforts. In addition, this paper seeks to identify areas of future research and development necessary to adapt social media analytic tools and techniques, and to form the seed for social media analytics to aid and inform arms control and nonproliferation policymakers and analysts. While social media analysis (as well as open source analysis as a whole) will not ever be able to replace traditional arms control verification measures, they do supply unique signatures that can augment existing analysis.

nonproliferation, arms control, social media↗
DOE OSTI2018

Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media

Deceptive information in online news and social media has had dramatic effect on our society in recent years. This study is the first to gain deeper insights into writers' intent behind digital misinformation by analyzing psycholinguistic signals: moral foundations and connotations extracted from different types of deceptive news ranging from strategic disinformation to propaganda and hoaxes. To ensure consistency of our findings and generalizability across domains, we experiment with data from: (1) confirmed cases of disinformation in news summaries, (2) {propaganda}, hoax, and disinformation news pages, and (3) social media news. We first contrast lexical markers of biased language, syntactic and stylistic signals, and connotations across deceptive news types including disinformation, propaganda, and hoaxes, and {deceptive} strategies including misleading or falsification. We then incorporate these insights to build machine learning and deep learning predictive models to infer deception strategies and deceptive news types. Our experimental results demonstrate that unlike earlier work on deception detection, content combined with biased language markers, moral foundations, and connotations leads to better predictive performance of deception strategies compared to syntactic and stylistic signals (as reported in earlier work on deceptive reviews). Falsification strategy is easier to identify than misleading strategy. Disinformation is more difficult to predict than to propaganda or hoaxes. Deceptive news types (disinformation, propaganda, and hoaxes), unlike deceptive strategies (falsification and misleading), are more salient, and thus easier to identify in tweets than in news reports. Finally, our novel connotation analysis across deception types provides deeper understanding of writers' perspectives and therefore reveals the intentions behind digital misinformation.

deception↗