Country Characteristics, Internet Connectivity and Combating Misinformation: A Network Analysis of Global North-South

Analyzing data on 152 countries using network and regression analyses, this study examined how countries’ positions in the global Internet network are associated with their political, economic, and technological characteristics, and how those characteristics are related to media, information, and digital (MID) education programs in the countries. This research shows countries with higher levels of international Internet bandwidth capacity, Internet use, and press freedom status are more likely to have MID programs that are comprehensive. Differences between Global North and Global South countries were significant both in terms of Internet capacity and use and in terms of MID complexity and dimensions. MID literacy education is an important long-term solution to misinformation, as such education informs people’s epistemological beliefs which in turn have direct effects on their comprehension of various issues and topics. This study offers important scholarly and policy implications in the areas of digital connectivity, MID literacies, misinformation, and international communication. In particular, it offers guidance for comparative studies in this area

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (>18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables

Social Media for Situational Awareness: Joint-Interagency Field Experimentation

Humanitarian Technology: Science, Systems, and Global Impact 2015, HumTech2015. Boston, MA, 12-14 May 2015.Social Media for Situational Awareness (SMSA) — the identification, tracking, and analysis of online social computing data in collaboration with other kinds of sensor data towards the derivation of ‘actionable’ intelligence, has recently become one of the key focuses of the Joint Interagency Field Experiment (JIFX), a regularly held event developed and facilitated by the Naval Postgraduate School. In this paper we describe: the structure of SMSA experimentation at JIFX, a situational awareness capabilities assessment, the outcomes of a SM shared task, and an integrated-technology scenario focusing on pandemic outbreak. We discuss an outline of potential future avenues for SMSA experimental designs to aid in the assessment and promotion of the use of technology for the derivation of intelligence from non-traditional sources during crises

Beyond Skill: Predictive Modeling with Individual and Team Attributes in Leagueof Legends

The goal of this study is to explore the predictive capability ofseveral psychosocial variables, such as personality and groupcohesion, towards determining multiplayer online battle arenagame outcomes - namely diversity, cohesion, and resilience, oncollective performance. Our study finds that measures ofindividual and team perceptions of qualities provided a usefulprecursor for match victory. Using individual-level attributes,our cohesion survey questions provided the highest predictivevalue, and higher levels of perceived cohesion were associatedwith higher victory odds. In light of our results, we discuss theimplications of using behavioral data derived from onlinegames and opportunities for future large-scale game datacollection

The Role of Integrated Scenario Experimentation in Improving Humanitarian Response Outcome

GTRI in support of the Naval Postgraduate School’s Joint Interagency Field Experimentation JIFX event carries out quarterly Integrated Scenario Experimentation. Integrated scenario experimentation brings technologists together to collaborate on shared humanitarian response scenario to gain working knowledge outside of the laboratory. Scaffolding scenarios are offered to experimenters in a series of pre-planning calls that happen before the week of JIFX. Through discussion with experimenters, the scenarios are expanded to allow for in situ experimentation that is beneficial to individuals experimenters outside of their traditionally isolated and laboratory settings. The qualitative knowledge and empirical data gathered through integrated scenario experimentation is invaluable to evolving experimenters’ technology

Combining resources for open source machine translation

In this paper, we present a Japanese - English machine translation system that combines rule-based and statistical translation. Our system is unique in that all of its components are freely available as open source software. We describe the development of the rule-based translation engine including transfer rule acquisition from an open bilingual dictionary. We also show how translations from both translation engines are combined through a simple ranking mechanism and compare their outputs.Accepted versio

Social Media for Situational Awareness: Joint-Interagency Field Experimentation

AbstractSocial Media for Situational Awareness (SMSA) — the identification, tracking, and analysis of online social computing data in collaboration with other kinds of sensor data towards the derivation of ‘actionable’ intelligence, has recently become one of the key focuses of the Joint Interagency Field Experiment (JIFX), a regularly held event developed and facilitated by the Naval Postgraduate School. In this paper we describe: the structure of SMSA experimentation at JIFX, a situational awareness capabilities assessment, the outcomes of a SM shared task, and an integrated-technology scenario focusing on pandemic outbreak. We discuss an outline of potential future avenues for SMSA experimental designs to aid in the assessment and promotion of the use of technology for the derivation of intelligence from non-traditional sources during crises

Semantic Analysis of Open Source Data for Syndromic Surveillance

ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p<0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events

Semantic Analysis of Open Source Data for Syndromic Surveillance

ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p<0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events