Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Twitter”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

It's Time for Color Vision Deficiency Friendly Color Maps in the Radar Community

Color Vision Deficiency (CVD) is a decreased ability to discern between particular colors. 8% of men and 0.4% of women have some form of CVD. An informal poll of AGU and AMS twitter followers yielded 10% of 70 respondents self identifying as having CVD. When presenting data on a two-dimensional plane it is common to use colors to represent values, the mapping between values and colors is known as a colormap. Colormap choice is personal and is influenced by: (1) Ability to highlight scientifically interesting data. (2) Institutional choices (supervisor insists on a certain colormap). (3) Domain dominance of a particular colormap (common in the radar community). Colormap choice should be influenced by: (1) Ability to highlight scientifically interesting data. (2) Perceptual uniformity (thus not creating artificial structure). (3) Approachability by those with visual impairments (CVD). (4) Ideally, but not always achievable, reproducibility in greyscale.

Colormap↗
NASA NTRS2018

Lyman-Alpha Comes of Age

The Lyman-alpha (Lyα) line is the strongest recombination line of hydrogen, which constitutes the dominant share of normal matter in the Universe. Fifty-one years ago, it was predicted that Lyα emission should appear prominently in the spectra of young primordial galaxies. For the next 30 years, this prediction was tested by many surveys, but none found the predicted galaxies at the predicted luminosities. The blame for these non-detections was squarely cast on the resonant nature of the Lyα line, which requires Lyα photons to scatter off many hydrogen atoms before escaping from a galaxy. This can hugely increase the path that they traverse, and consequently also the probability that they will be absorbed by dust. This remained the dominant explanation of the missing high-redshift lines for decades, despite some dissenting opinions about the effect of dust. The spectra of nearby galaxies from the International Ultraviolet Explorer are inconclusive about what allows galaxies to be Lyα emitters. The ‘missing’ lines were finally detected 20 years ago, at flux levels much below the original predictions - a difference that we understand to be predominantly due to the hierarchical growth of galaxies, such that star-forming objects at high redshift are much less massive than anticipated in the original prediction. The discovery and study of Lyα galaxies has blossomed into an invaluable method for understanding the youngest stages of galaxy formation, the circumgalactic medium, and ultimately the epoch of reionization. The high equivalent widths of Lyα galaxies from the earliest high-redshift surveys show that they are low-mass galaxies dominated by young stellar populations. The Sakura CLAW workshop (which used the Twitter hashtag #SakuraCLAW) gathered more than 110 participants (Fig. 1) who showcased progress that pushed the frontiers of Lyα galaxy science in sample size, sensitivity, redshifts and physical understanding..

Malhotra, Sangeeta↗
DOE OSTI2014

Frequent Subgraph Discovery in Large Attributed Streaming Graphs

The problem of finding frequent subgraphs in large dynamic graphs has so far only consid- ered a dynamic graph as being represented by a series of static snapshots taken at various points in time. This representation of a dynamic graph does not lend itself well to real time processing of real world graphs like social networks or internet traffic which consist of a stream of nodes and edges. In this paper we propose an algorithm that discovers the frequent subgraphs present in a graph represented by a stream of labeled nodes and edges. Our algorithm is efficient and consists of tunable parameters that can be tuned by the user to get interesting patterns from various kinds of graph data. In our model updates to the graph arrive in the form of batches which contain new nodes and edges. Our algorithm con- tinuously reports the frequent subgraphs that are estimated to be found in the entire graph as each batch arrives. We evaluate our system using 5 large dynamic graph datasets: the Hetrec 2011 challenge data, Twitter, DBLP and two synthetic. We evaluate our approach against two popular large graph miners, i.e., SUBDUE and GERM. Our experimental re- sults show that we can find the same frequent subgraphs as a non-incremental approach applied to snapshot graphs, and in less time.

Dynamic graph, Frequent subgraph mining, pattern d↗
DOE OSTI2015

Text Stream Trend Analysis using Multiscale Visual Analytics with Applications to Social Media Systems

In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing these contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.

interaction↗
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 OSTI2015

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction, Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.

data visualization↗
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 OSTI2017

Understanding Social Media’s Take on Climate Change through Large-Scale Analysis of Targeted Opinions and Emotions

Social media is a powerful data source for researchers interested in understanding population-level behavior, having been successfully leveraged in a number of different application areas including flu and illness prediction models, detecting civil unrest, and measuring public sentiment towards a given topic of interest within the public discourse. In this work, we present a study of a large collection of Twitter data centered on the social conversation around global cli- mate change during the UN Climate Change Conference, held in Paris, France during December 2015 (COP21). We first developed a mechanism for distinguishing between personal and non-personal accounts. We then analyzed demographics and emotion and opinion dynamics over time and location in order to understand how the different user types converse around meaningful topics on social media. This methodology offers an in-depth insight into the behavior and opinions around a topic where multiple distinct narratives are present, and lays the groundwork for future work in studying narratives in social media.

social media research↗
DOE OSTI2017

Data-Driven Disease Forecasting

If disease outbreaks could be forecasted like the weather, communities could set up protective measures to mitigate their impact. At Los Alamos National Laboratory, scientists are improving disease-forecasting mathematical models by using clinical data--as well as internet data sources such as Wikipedia, Twitter, and Google--and coupling it with satellite imagery. The goal is to better understanding how diseases spread and, eventually, forecast disease outbreaks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Temporal Methods to Detect Content-Based Anomalies in Social Media

Here, we develop a method for time-dependent topic tracking and meme trending in social media. Our objective is to identify time periods whose content differs signifcantly from normal, and we utilize two techniques to do so. The first is an information-theoretic analysis of the distributions of terms emitted during different periods of time. In the second, we cluster documents from each time period and analyze the tightness of each clustering. We also discuss a method of combining the scores created by each technique, and we provide ample empirical analysis of our methodology on various Twitter datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Explaining Multimodal Deceptive News Prediction Models

Social media plays a valuable role in rapid news dissemination, but it also serves as a vehicle to propagate unverified information. For example, news shared on Facebook or Twitter may actually contain disinformation, propaganda, hoaxes, conspiracies, clickbait or satire. This paper presents an in-depth analysis of the behavior of suspicious news classification models including error analysis and prediction confidence. We consider five deep learning architectures that leverage combinations of text, linguistic and image input signals from tweets. The behavior of these models is analyzed across four suspicious news prediction tasks. Our findings include that models leveraging only the text of tweets outperform those leveraging only the image (by 3-13% absolute in F-measure), and that models that combine image and text signals with linguistic cues e.g., biased and subjective language markers can, but do not always, perform even better. Finally, our main contribution is a series of analyses, in which we characterize text and image traits of our classes of suspicious news and analyze patterns of errors made by the various models to inform the design of future deceptive news prediction models.

deep learning, social media, deception↗
DOE OSTI2021

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↗
DOE OSTI2022

Huntsman

SAND2021-15137 O The Huntsman library includes two main components: WebComms, which enhances Apache's HTTP Client by providing commonly needed capabilities in this domain to simplify web access as well as TwitterComms, which provides enhancements to the Twitter4J library for Twitter access. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Ridgway, Elliott↗
DOE OSTI2021

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one’s peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the “pattern” by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

97 MATHEMATICS AND COMPUTING↗
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↗