Search

Online hate is a global problem, affecting diverse social conflicts around the world. Common techniques to understand online hate focus primarily on measuring the volume of hateful messages and identifying individual perpetrators. However, these approaches fail to capture how online hate draws on wider social narratives to attack its targets, and how online hate is spread and organized in networked groups. Our work posits that assessing online hate through the lens of narratives and networks facilitates a deeper understanding of its dynamics in specific contexts and points to more holistic directions for mitigating its potential manipulation. We demonstrate the utility of our framework across two case studies in the Philippines: (a) the 2020 outbreak of the COVID-19 pandemic, and (b) the 2022 presidential elections. Through a computational pipeline of social cyber-security tools, we characterize the online hate narratives used to attack gendered, political, and racial identities in these events, and the network structures of the online hate groups involved. We additionally quantify manipulation of online hate narratives and networks by bots and trolls. These findings offer insights into the design of effective counter-narratives and building more resilient communities against online hate. [ABSTRACT FROM AUTHOR]

As social media becomes more ingrained in political and social discourse around the world, the need for systematic measures and interpretations of online polarization grows. In this work, we introduce five major organizational forms of community-level polarization and nine subforms to support comparison of community dynamics in controversial discussions across time, platforms, languages, and domains. We propose a multi-dimensional social network analysis approach to characterize the structure within and between ideologically opposed communities and evaluate the organizational form. Through three case studies, we demonstrate applying the proposed methodology on two social media platforms (Twitter and Reddit) and levels of conflict (topic and partisanship). This work has implications for depolarization interventions and policies on social media. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this research is to (1) investigate the impact of nursing unit design on nursing staff communication patterns and, ultimately, on patient falls in acute care nursing units; and (2) evaluate whether differences in fall rates, if found, were associated with the nursing unit physical structure (shape) or size. Background: Nursing staff communication and nursing unit design are frequently linked to patient safety outcomes, yet little is known about the impact of specific nursing unit designs on nursing communication patterns that might affect patient falls. Method: An exploratory longitudinal correlational design was used to measure nursing unit communication structures using social network analysis techniques. Data were collected 4 times over a 7-month period. Floor plans were used to determine nursing unit design. Fall rates were provided by hospital coordinators. Results: An analysis of covariance controlling for hospitals resulted in a statistically significant interaction of unit shape and size (number of beds). The interaction occurred when medium- and large-sized racetrack-shaped units intersected with medium- and large-sized cross-shaped units. Conclusion: The results suggest that nursing unit design shape impacts nursing communication patterns, and the interaction of shape and size may impact patient falls. How those communication patterns affect patient falls should be considered when planning hospital construction of nursing care units. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The aim of this study was to compare information sharing and advice networks' relationships with patient safety outcomes. BACKGROUND: Communication contributes to medical errors, but rarely is it clear what elements of communication are key. METHODS: We investigated relationships of information-sharing and advice networks to patient safety outcomes in 24 patient care units from 3 hospitals over 7 months. Web-based questionnaires completed via Android tablets provided data to create 2 networks using ORA, a social network analysis application. Each hospital provided nurse-sensitive patient safety outcomes. RESULTS: In both networks, medication errors correlated positively with node count and average distance and negatively with clustering coefficient. Density and weighted density negatively correlated with medication errors and falls in both networks. Eigenvector and total degree centrality correlated negatively with both safety outcomes, whereas betweenness centrality positively related to falls in the information-sharing network. CONCLUSION: Technology-enabled social network analysis data collection is feasible and can provide managers actionable system-level information. [ABSTRACT FROM AUTHOR]

Social media is playing a growing role in disaster management and response. Expectations are that such media can be critical for sending alerts, identifying critical needs, and focusing response. However, for social media to be used in that way, it will be necessary to understand how social media is used during normal, i.e., non-disaster periods. Herein we examine the use of a particular social media, Twitter, and assess its value for disaster management with a focus on planning and early warnings. Our focus is Padang Indonesia, and the potential use of Twitter to support tsunami warning and response. We assess alternative collection strategies and analyze Twitter usage under normal conditions and then use this information to identify the strengths and weaknesses of this data in supporting disaster planning and early warning in terms of coverage, spatio-temporal patterns, and identification of opinion leaders. We find that while one can potentially leverage Twitter for disaster management, careful collection, assessment, and coordination with official disaster Twitter sites and local on-scene Twitter opinion leaders will be critical from a preparedness and early warning perspective. Guidelines for harnessing Twitter data for such uses are provided. [ABSTRACT FROM AUTHOR]

Background: Online infodemics have represented a major obstacle to the offline success of public health interventions during the COVID-19 pandemic. Offline contexts have likewise fueled public susceptibility to online infodemics. We combine a large-scale dataset of Twitter conversations about face masks with high-performance machine learning tools to detect low-credibility information, bot activity, and stance toward face masks in online conversations. We match these digital analytics with offline data regarding mask-wearing and COVID-19 cases to investigate the bidirectional online-offline dynamics of the face mask infodemic in the United States. Results: Online prevalence of anti-mask over pro-mask stance predicts decreased offline mask-wearing behavior and subsequently increased COVID-19 infections. These effects are partially influenced by low-credibility information and automated bot activity, which consistently feature greater anti-mask stance online. Despite their purported controversy, mask mandates generally decrease anti-mask stance online and increase mask-wearing offline, thus reducing future COVID-19 infections. Notable asymmetries are observed, however, between states run by Democratic and Republican governors: the latter tend to see higher levels of low-credibility information and anti-mask stance online, and thus lower mask-wearing and higher infection rates offline. Conclusions: These findings contribute new insights around collective vulnerabilities to online infodemics and their links to evolving offline crises. We highlight the need to synergize and sustain targeted online campaigns from legitimate information sources alongside offline interventions in and beyond the pandemic. [ABSTRACT FROM AUTHOR]

Peter Blau was one of the first to define a latent social space and utilize it to provide concrete hypotheses. Blau defines social structure via social "parameters" (constraints). Actors that are closer together (more homogenous) in this social parameter space are more likely to interact. One of Blau's most important hypotheses resulting from this work was that the consolidation of parameters could lead to isolated social groups. For example, the consolidation of race and income might lead to segregation. In the present work, we use Foursquare data from New York City to explore evidence of homogeneity along certain social parameters and consolidation that breeds social isolation in communities of locations checked in to by similar users. [ABSTRACT FROM AUTHOR]

The recent COVID-19 outbreak has highlighted the importance of effective communication strategies to control the spread of the virus and debunk misinformation. By using accurate narratives, both online and offline, we can motivate communities to follow preventive measures and shape attitudes toward them. However, the abundance of misinformation stories can lead to vaccine hesitancy, obstructing the timely implementation of preventive measures, such as vaccination. Therefore, it is crucial to create appropriate and community-centered solutions based on regional data analysis to address mis/disinformation narratives and implement effective countermeasures specific to the particular geographic area. In this case study, we have attempted to create a research pipeline to analyze local narratives on social media, particularly Twitter, to identify misinformation spread locally, using the state of Pennsylvania as an example. Our proposed methodology pipeline identifies main communication trends and misinformation stories for the major cities and counties in southwestern PA, aiming to assist local health officials and public health specialists in instantly addressing pandemic communication issues, including misinformation narratives. Additionally, we investigated anti-vax actors' strategies in promoting harmful narratives. Our pipeline includes data collection, Twitter influencer analysis, Louvain clustering, BEND maneuver analysis, bot identification, and vaccine stance detection. Public health organizations and community-centered entities can implement this data-driven approach to health communication to inform their pandemic strategies. [ABSTRACT FROM AUTHOR]

Abstract: A high spatial resolution model of energy use in residential buildings was developed based on time use data. The development of this model was guided by a theoretical framework that explains the nature of a dwelling''s physical characteristics, the nature of its occupants’ energy use behaviors and the ways in which the dwelling and its occupants interact to determine the energy use of a household. Energy Plus was used as the model platform. In the model, the occupants’ domestic activity pattern was extracted from American Time Use Survey (ATUS) data via bootstrap sampling. The dwelling''s physical characteristics were based on real world scenarios. Virtual experiments with 3- to 5-occupant household compositions were conducted to examine the model properties. Simulation results show that (1) bell-shaped distributions were present in annual heating load demands for all household compositions, (2) the load demands for different batches of samples of the same household composition demonstrated a narrow range of variations, and (3) the simulated hourly appliance and lighting load profiles were in agreement with those generated from field-metered data at both whole-house and sub-house levels. These results indicate the model''s overall robustness and verify its ability to simulate realistic residential energy use load profiles. [Copyright &y& Elsevier]

We examine several approaches for inferring logit models from empirical margins of predictor covariates and conditional margins containing the means of a binary response for each covariate margin. One method is to fit proxy data to the conditional response using the beta distribution, a process we call 'margin analysis.' Proxy data can obtained using three approaches: (1) implementing the iterative proportional fitting (IPF) procedure on the margin totals, (2) sampling from a larger relevant data source such as the census, and (3) enumerating, or sampling from, the combinatoric space of all possible tables constrained by the margins. The first procedure is a well-studied approach for estimating contingency tables from margins, but it does not necessarily maintain the associations between the covariates unless seeded with an initial table containing those associations. In the second approach, which is appropriate for analyzing sociodemographic covariates, we can use a large census sample adjusting for sampling biases observed in the empirical margins. However, the appropriateness of using a census proxy depends substantially on how similar the sampling pools are. Our third approach entails exploring the combinatoric space of all contingency tables constrained by the margins while considering the associations among the covariates. We aggregate the logit models estimated from each table in that space into a single model. This approach is more robust than the first two as it considers multiple proxies. While the estimated logit models from each approach are generally similar to one another, for the low-dimensional tables we explore in this paper, the combinatoric approach incurs wider standard errors, which renders potentially significant coefficients insignificant. Finally, we suggest weighting the combinatoric models with evidence-relevant probabilities obtained using the multivariate Pólya distribution. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: We used ORA, a dynamic network analysis tool, to identify patient care unit communication patterns associated with patient safety and quality outcomes. Although ORA had previously had limited use in healthcare, we felt it could effectively model communication on patient care units. Methods: Using a survey methodology, we collected communication network data from nursing staff on seven patient care units on two different days. Patient outcome data were collected via a separate survey. Results of the staff survey were used to represent the communication networks for each unit in ORA. We then used ORA''s analysis capability to generate communication metrics for each unit. ORA''s visualization capability was used to better understand the metrics. Results: We identified communication patterns that correlated with two safety (falls and medication errors) and three quality (e.g., symptom management, complex self care, and patient satisfaction) outcome measures. Communication patterns differed substantially by shift. Conclusion: The results demonstrate the utility of ORA for healthcare research and the relationship of nursing unit communication patterns to patient safety and quality outcomes. [Copyright &y& Elsevier]

A fundamental aspect of human beings is that they learn. The process of learning and what is learned are impacted by a number of factors, both cognitive and social; that is, humans are boundedly rational. Cognitive and social limitations interact, making it difficult to reason about how to provide information to impact what humans know, believe, and do. Herein, we use a multi-agent dynamic-network simulation system, Construct, to conduct such reasoning. In particular, we ask, What media should be used to provide information to most impact what people know, believe, and do, given diverse social structures? All simulated agents are boundedly rational both at the cognitive and social level, and so are subject to factors such as literacy, education, and the breadth of their social network. We find that there is no one most effective intervention; rather, to be effective, messages and the media used to spread the message need to be selected for the population being addressed. Typically, a multimedia campaign is critical. [ABSTRACT FROM AUTHOR]

Abstract: Using multi-agent models to study social systems has attracted criticisms because of the challenges involved in their validation. Common criticisms that we have encountered are described, and for each one we attempt to give a balanced perspective of the criticism. A model of intra-state conflict is used to help demonstrate these points. We conclude that multi-agent models for social systems are most useful when (1) the connection between micro-behaviors and macro-behaviors are not well-understood and (2) when data collection from the real-world system is prohibitively expensive in terms of time or money or if it puts human lives at risk. [Copyright &y& Elsevier]

While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemioiogy, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms. [ABSTRACT FROM AUTHOR]

The past decade has witnessed the emergence of a new scientific discipline––computational social and organizational science. Within organization science in particular, and social science more generally, scientists and practitioners are turning to computational analysis to address fundamental socio-technical problems that are so complex and dynamic that they cannot be fully addressed by traditional techniques. Consequently, there is an explosion of computational models, computationally generated findings, interest in doing simulation, and a dearth of support for this enterprise. This paper contains discussions of the underlying fundamental perspective, the relation of models to empirical data and characteristics of necessary infrastructure. [Copyright &y& Elsevier]

Focuses on the synthetic adaptation in computational organization science. Assessment on the behavior of groups, organizations and societies; Use of intelligent adaptive artificial agents; Combination of social and knowledge approach with a multiagent approach.

Presents information on a study which examined the impact of emotions on organizational performance during crisis response. Emotions in organizational research; Method used in the emotion analysis; Results and discussion.

A network analytic approach to the study of organizational response to crises is used to determine the existence and form of integrated crisis management units (ICMUs). ICMUs are network organizations that emerge in response to severe crises such as major oil spills, earthquakes, hurricanes and other natural and man made disasters. Although ICM Us have received considerable attention from scholars in the form of empirical case studies, much remains unclear as to their nascent interorganizational structure and structural evolution. In this paper, we outline a network analytic framework for the study of ICMUs and demonstrate how some of the predictions of previous research can he more rigorously quantified and compared. We then apply our framework to the 1989 Exxon Valdez oil spill. The Valdez case study provides an illustration of the analytic framework and suggests some interesting directions for further research on emergent organizational forms. Results demonstrate (1) the emergence of a network organization which grew more complex and more centralized over time. (2) the vulnerability of emergent structures to after shocks, and (3) the structuration of the organizational field through coordination of multiple network organizations, despite fluctuation in peripheral groups of stakeholder organizations. [ABSTRACT FROM AUTHOR]

Executives argue intuitively that trust is critical to effective organizational performance. Although articulated as a cognitive/ affective property ofindividuals, the collective effect of events influencing(and being influenced by) trust judgments must certainly impact organizational behavior. To begin to explore this, we conducted a simulation study of trust and organizational performance. Specifically, we defined a set ofcomputational agents, each with a trust function capable of evaluating the quality ofadvice from the other agents, and rendering judgments on the trustworthiness of the communicating agent. As agent judgments impact subsequent choices to accept or to generate communications, organizational performance is influenced. We manipulated two agent properties(trustworthiness, benevolence), two organizational variables (group size, group homogeneity/liar-to-honest ratio), and one environmental variable (stable, unstable). Results indicate that in homogeneous groups, honest groups did better than groups of liars, but under environmental instability, benevolent groups did worse. Under all conditions for heterogeneous groups, it only took one to three liars to degrade organizational performance. [ABSTRACT FROM AUTHOR]

We investigated how positive affectivity influences people's perception of the patterns of social relationships around them. Positive affectivity was measured as trait positive affect. The outcome variable was accuracy in the perception of informal patterns of social interaction in a group (i.e., the group's network structure). Data on the perception of the relationships of friendship and work-related advice at an Italian university were collected from 24 members. Positive affectivity improved the accuracy of people's perception of the friendship network linking all respondents to each other (global accuracy), but hindered the accuracy of the perception of one's personal advice ties (local accuracy). These results suggest that, although happy people may feed unrealistic images of their personal social connections, their superior ability to develop an accurate picture of the broader dynamics of social interaction among people around them may give them an important advantage for social and professional accomplishment in a given social setting. [ABSTRACT FROM AUTHOR]

A view of communication technologies as creating artificial agents and affecting the information-processing capabilities of agents is forwarded. The constructural theory is adapted to account for agents varying in their information-processing capabilities and, hence, to account for technology. Given this theoretical modification, the constructural model is used to examine the impact of different communication technologies and sociocultural landscapes on the rate at which information diffuses and the time it takes for the society to reach cultural homogeneity and consensus. The findings suggest that as the available communication technologies change, the role of the sociocultural landscape in effecting social change varies. Paradoxically, this research suggests that mass-communication technologies that enable greater competition among messages and greater message complexity will enable faster information diffusion than will those technologies that inhibit competition and message complexity. [ABSTRACT FROM AUTHOR]

Examines how the Federal Emergency Management Agency fared in its attempt to initiate change in its Federal Response Plan using the complex setting of Hurricane Andrew that struck South Florida and Louisiana on August 24, 1992. Discrepancy between the Federal Response Plan and the agency's ability to carry out the plan; Coordination with both state and local agencies; Organizational learning and catastrophic events.

This paper describes mathematical models for network evolution when ties (edges) are directed and the node set is fixed. Each of these models implies a specific type of departure from the standard null binomial model. We provide statistical tests that, in keeping with these models, are sensitive to particular types of departures from the null. Each model (and associated test) discussed follows directly from one or more socio-cognitive theories about how individuals alter the colleagues with whom they are likely to interact. The models include triad completion models, degree variance models, polarization and balkanization models, the Holland-Leinhardt models, metric models, and the constructural model. We find that many of these models, in their basic form, tend asymptotically towards an equilibrium distribution centered at the completely connected network (i.e., all individuals are equally likely to interact with all other individuals); a fact that can inhibit the development of satisfactory tests. [ABSTRACT FROM AUTHOR]

Organizational theorists have attributed organizational performance to a variety of factors including time pressure, training, organizational complexity, environment complexity, and the match between the organization and its environment. Using a computational model of organizational performance based on an information processing and resource dependency view of organizations the effects of these factors are simultaneously compared and their potential relative impact deduced. Following such an analysis we find that time pressure, training, organizational complexity, and organizational environment are stronger determinants of organizational performance than the match between the organization and its environment. [ABSTRACT FROM AUTHOR]

Radar-Soar, an artificial organization composed of intelligent agents, is crafted out of multiple interconnected modules--one of each agent, one of the organizational design (structure plus process), and one of the task. Each agent is based on a sophisticated model of cognition, i.e.. Soar. Using Radar-Soar we demonstrate how insight into organizational (and so social) behavior can be gained using computational models that take a meso-approach to organizations. [ABSTRACT FROM AUTHOR]

In an attempt to systematically address what factors affect organizational performance, we built a dynamic computational framework for examining organizational performance in which organizations are composed of intelligent adaptive agents. Using this framework the user can contrast organizations with different designs, existing in different task environments, and subject to different stresses. We demonstrate the value of this model by examining how training and stress affect organizational performance. [ABSTRACT FROM AUTHOR]

Organizations can be characterized as complex systems composed of adaptive and intelligent agents. Organizational adaptation occurs through restructuring and learning. Organizations can be modeled using a dual-level model in which restructuring is modeled as a simulated annealing process and individual learning is modeled using a stochastic learning model and boundedly rational agents. Such a model is presented, and its behavior is illustrated using a virtual experiment where the type of organizational adaptation is varied. Results suggest that the organizational design and performance relationship may be chaotic, despite the simple rules of change. Simple restructuring rules lead to a wide range of emergent organizational structures that increases with individual adaptation. Organizations locate good designs (through chance and slow change) regardless of the agents' intelligence; however, emergent designs depend on adaptability. Design features are not systematically related to performance; rather, small initial differences in design and environment can affect the emergent behavior. [ABSTRACT FROM AUTHOR]

The potential linkages between artificial intelligence and sociology are growing. This growth is due to importation of artificial intelligence techniques into methodological tools for data analysis, a growing interest among researchers in artificial intelligence in the socially situated agent, and a growing interest among sociologists in using artificial intelligence techniques for theorizing about social phenomena. Increasingly, researchers are addressing concerns of traditional importance within sociology, such as the bases for cooperation, the role of structure in affecting individual agency, and interaction using computational models of intelligent adaptive agents. This article provides an overview of the role that artificial intelligence currently plays within sociology. [ABSTRACT FROM AUTHOR]

Describes the use of the main path technique in understanding the development of scientific fields of studies. Advantage of the use of historical analysis in locating and describing institutional contexts; Characteristics of meta-main path articles; Popular articles and books' operation at the `grand theory' level.

Presents a comparison study of artificial and human organizations. Examination of organizational models; Simulation of human organizations; Cognitive limitations, adaptability, and general intelligence of models as compared to human organizations; Prediction of different levels of organizational performance; Determination of the flow of information within the organization.

In the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. Focusing on three event dimensions, i.e., employed weapons, deployed tactics and chosen targets, meta-graphs map the connections among temporally close attacks, capturing their operational similarities and dependencies. From these temporal meta-graphs, we derive 2-day-based time series that measure the centrality of each feature within each dimension over time. Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen. The paper makes two contributions. First, it demonstrates that engineering the feature space via temporal meta-graphs produces richer knowledge than shallow time-series that only rely on frequency of feature occurrences. Second, the performed experiments reveal that bi-directional LSTM networks achieve superior forecasting performance compared to other algorithms, calling for future research aiming at fully discovering the potential of artificial intelligence to counter terrorist violence. [ABSTRACT FROM AUTHOR]

A common theme among previously proposed models for network epidemics is the assumption that the propagating object (e.g., a pathogen [in the context of infectious disease propagation] or a piece of information [in the context of information propagation]) is transferred across network nodes without going through any modification or evolutionary adaptations. However, in real-life spreading processes, pathogens often evolve in response to changing environments and medical interventions, and information is often modified by individuals before being forwarded. In this article, we investigate the effects of evolutionary adaptations on spreading processes in complex networks with the aim of 1) revealing the role of evolutionary adaptations on the threshold, probability, and final size of epidemics and 2) exploring the interplay between the structural properties of the network and the evolutionary adaptations of the spreading process. [ABSTRACT FROM AUTHOR]

Objective: We examine individuals' ability to detect social bots among Twitter personas, along with participant and persona features associated with that ability. Background: Social media users need to distinguish bots from human users. We develop and demonstrate a methodology for assessing those abilities, with a simulated social media task. Method: We analyze performance from a signal detection theory perspective, using a task that asked lay participants whether each of 50 Twitter personas was a human or social bot. We used the agreement of two machine learning models to estimate the probability of each persona being a bot. We estimated the probability of participants indicating that a persona was a bot with a generalized linear mixed-effects model using participant characteristics (social media experience, analytical reasoning, and political views) and stimulus characteristics (bot indicator score and political tone) as regressors. Results: On average, participants had modest sensitivity (d') and a criterion that favored responding "human." Exploratory analyses found greater sensitivity for participants (a) with less self-reported social media experience, (b) greater analytical reasoning ability, and (c) who were evaluating personas with opposing political views. Some patterns varied with participants' political identity. Conclusions: Individuals have limited ability to detect social bots, with greater aversion to mistaking bots for humans than vice versa. Greater social media experience and myside bias appeared to reduce performance, as did less analytical reasoning ability. Application: These patterns suggest the need for interventions, especially when users feel most familiar with social media. [ABSTRACT FROM AUTHOR]

This article presents information about Computational Organization Theory (COT). It is the study of constrained adaptation. This field is characterized by the study of organizations as collections of adaptive agents who are constrained both by their cognitive capabilities and position in the organization and who interact. One of the underlying issues for researchers in this area is the extent to which there are rules of organizing and dynamics that hold independent of the types of agents in the organization and the conditions under which uniquely human characteristics alter these rules. Organizational behavior emerges from the concurrent behavior of the members. Within such systems complex phenomena emerge from concurrent interactions among agents. In addition, and critically, what emerges, and how fast it emerges, is determined by the constraints on adaptation; specifically, the cognitive abilities of the agents, the type of agents present, their socio-cultural historical and organizational position, and the knowledge they have garnered through experience and from others. Finally, within the organization behavior affects and is affected by the organization's position in the external environment.

The Islamic State of Iraq and ash-Sham (ISIS) continues to use social media as an essential element of its campaign to motivate support. On Twitter, ISIS’ unique ability to leverage unaffiliated sympathizers that simply retweet propaganda has been identified as a primary mechanism in their success in motivating both recruitment and “lone wolf” attacks. The present work explores a large community of Twitter users whose activity supports ISIS propaganda diffusion in varying degrees. Within this ISIS supporting community, we observe a diverse range of actor types, including fighters, propagandists, recruiters, religious scholars, and unaffiliated sympathizers. The interaction between these users offers unique insight into the people and narratives critical to ISIS’ sustainment. In their entirety, we refer to this diverse set of users as an online extremist community or OEC. We present Iterative Vertex Clustering and Classification (IVCC), a scalable analytic approach for OEC detection in annotated heterogeneous networks, and provide an illustrative case study of an online community of over 22,000 Twitter users whose online behavior directly advocates support for ISIS or contibutes to the group’s propaganda dissemination through retweets. [ABSTRACT FROM AUTHOR]

Decision makers need capabilities to quickly model and effectively assess consequences of actions and reactions in crisis de-escalation environments. The creation and what-if exercising of such models has traditionally had onerous resource requirements. This research demonstrates fast and viable ways to build such models in operational environments. Through social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions, advisors can support practicing and execution of crisis de-escalation activities. We describe how we used this approach as part of a scenario-driven modeling effort. We demonstrate the strength of moving from data to models and the advantages of data-driven simulation, which allow for iterative refinement. We conclude with a discussion of the limitations of this approach and anticipated future work. [ABSTRACT FROM PUBLISHER]

Highlights: [⬢] Online accounts of behavior rapidly analyzed via semi-automated text coding. [⬢] Human-generated content dictionaries improve accuracy of text analysis. [⬢] Human-generated content dictionaries can code for more terms than human coders. [⬢] Network statistics quantify relationships among coded words. [⬢] Automated approaches may have errors in word-sense disambiguation. [Copyright &y& Elsevier]

The evolution of Internet topology is not always smooth but sometimes with unusual sudden changes. Consequently, identifying patterns of unusual topology evolution is critical for Internet topology modeling and simulation. We analyze IPv6 Internet topology evolution in IP-level graph to demonstrate how it changes in uncommon ways to restructure the Internet. After evaluating the changes of average degree, average path length, and some other metrics over time, we find that in the case of a large-scale growing the Internet becomes more robust; whereas in a top--bottom connection enhancement the Internet maintains its efficiency with links largely decreased. [ABSTRACT FROM AUTHOR]

Definition acquisition is a necessary step in building an artificial cognitive assistant that helps military personnel to gain fast and precise understanding of the various terms and procedures defined in applicable legal documents. We approach the task of identifying definitional sentences from operations law documents by formalizing this task as a sentence-classification task and solving it by using machine-learning methods. This paper reports on a series of empirical experiments in that we evaluate and compare the performance of learning algorithms in terms of label-prediction accuracy. Using supervised techniques results in an F1 score of 95.93% and a 96.72% recall rate. However, for real-world applications, it would be too costly and unrealistic to ask personnel involved in military operations to label substantial amounts of data in order to build a new classifier for different types or genres of text data. Therefore, we propose and implement a semisupervised (SS) solution that trades off prediction accuracy to label efficiency. Our SS approach achieves a 90.47% F1 score and 93.44% recall rate by using only eight sentences labeled by a human expert. [ABSTRACT FROM PUBLISHER]

This research draws on longitudinal network data from an online community to examine patterns of users' behavior and social interaction, and infer the processes underpinning dynamics of system use. The online community represents a prototypical example of a complex evolving social network in which connections between users are established over time by online messages. We study the evolution of a variety of properties since the inception of the system, including how users create, reciprocate, and deepen relationships with one another, variations in users' gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the system. Results indicate that the system is a “small world” characterized by the emergence, in its early stages, of a hub-dominated structure with heterogeneity in users' behavior. We investigate whether hubs are responsible for holding the system together and facilitating information flow, examine first-mover advantages underpinning users' ability to rise to system prominence, and uncover gender differences in users' gregariousness, popularity, and local redundancy. We discuss the implications of the results for research on system use and evolving social networks, and for a host of applications, including information diffusion, communities of practice, and the security and robustness of information systems. [ABSTRACT FROM AUTHOR]

We sought to examine the growth of an interdisciplinary center using social network analysis techniques. Specific aims were to examine the patterns of growth and interdisciplinary connectedness of the Center and to identify the social network characteristics of its productive members. The setting for this study was The Center for Interdisciplinary Research on Antimicrobial Resistance (CIRAR) at Columbia University. Periodic surveys and social network analysis comprised the study design. The data for this study included a relational survey taken by all members of the Center at three time points over one year. Respondents confirmed whether or not they had "heard of," "met," or "know the work of," or had "worked with" each of the other Center members. Data were analyzed using the social networking software program Organizational Risk Analyzer (ORA). Over time the social network increased in size, density, centralization, and complexity. The density of connections among and between different disciplines in the Center varied from Time 1 to 2 to 3; some increased, some decreased, while others stayed the same. Finally, the total degree centrality and the betweeness centrality of Center members were highly correlated to productivity. The study shows that a number of characteristics of an interdisciplinary research center can be quantified and described using social network techniques. Data from these analyses can be used to evaluate a center's progress, identify important indicators of leadership, identify areas of strength and need for improvement, and inform decisions on strategic direction. [ABSTRACT FROM AUTHOR]

Objective/Aim: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. Background: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. Methods: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. Results: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. Conclusions: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors—and subsequently health. [ABSTRACT FROM AUTHOR]

Sociologists have begun to explore the gains for theory and research that might be achieved artificial intelligence technology: symbolic processors, expert systems, neural networks, genetic algorithms, and classifier systems. The first major accomplishments of artificial social intelligence (ASI) have been in the realm of theory, where these techniques have inspired new theories as well as helping to render existing theories more rigorous. Two application areas for which ASI holds great promise are the sociological analysis of written texts and data retrieval from the forthcoming Global Information Infrastructure. ASI has already been applied to some kinds of statistical analysis, but how competitive it will be with more conventional techniques remains unclear. To take advantage of the opportunities offered by ASI, sociologists will have to become more computer literate and will have to reconsider the place of programming and computer science in the sociological curriculum. ASI may be a revolutionary approach with the potential to rescue sociology from the doldrums into which some observers believe it has fallen. [ABSTRACT FROM AUTHOR]

Background: Our previous studies suggest that the training history of an investigator, termed "medical academic genealogy", influences the outcomes of that investigator's research. Here, we use meta-analysis and quantitative statistical modeling to determine whether such effects contribute to systematic bias in published conclusions.Methods: A total of 108 articles were identified through a comprehensive search of the high-grade glioma (HGG) surgical resection literature. Analysis was performed on the 70 articles with sufficient data for meta-analysis. Pooled estimates were generated for key academic genealogies. Monte Carlo simulations were performed to determine whether the effects attributed to genealogy alone can arise due to chance alone.Results: Meta-analysis of the HGG literature without consideration for academic medical genealogy revealed that gross total resection (GTR) was associated with a significant decrease in the odds ratio (OR) for the hazard of death after surgery for both anaplastic astrocytoma (AA) and glioblastoma (AA: log [OR] = − 0.04, 95% CI [− 0.07 to − 0.01]; glioblastoma log [OR] = − 0.36, 95% CI [− 0.44 to − 0.29]). For the glioblastoma literature, meta-analysis of articles contributed by members of a genealogy consisting of mostly radiation oncologists revealed no reduction in the hazard of death after GTR [log [OR] = − 0.16, 95% CI [− 0.41 to 0.09]. In contrast, meta-analysis of published articles contributed by members of a genealogy consisting of mostly neurosurgeons revealed that GTR was associated with a significant reduction in the hazard of death [log [OR] = − 0.29, 95% CI [− 0.40 to 0.18]. Monte Carlo simulation revealed that the observed discrepancy between the articles contributed by the members of these two genealogies was unlikely to arise by chance alone (p < 0.006).Conclusions: Meta-analysis of articles contributed by authors belonging to the different medical academic genealogies yielded distinct and contradictory pooled point-estimates, suggesting that genealogy contributes to systematic bias in the published literature. [ABSTRACT FROM AUTHOR]

Background The paradigm of evidence-based medicine dictates that clinical practice should reflect the shifting landscape of the peer-reviewed literature. Here, we examined the extent to which this premise is fulfilled as it pertains to the surgical resection of high-grade gliomas (HGGs). Objective We assessed trends in published literature regarding HGG survival after resection in conjunction with trends in clinical practice patterns of HGG resection. Methods We performed a comprehensive PubMed search to identify articles that examined whether gross total resection (GTR) improves HGG survival. Temporal trends in the literature were compared with rates of GTR in the Surveillance Epidemiology and End Results (SEER) database, the Veterans Health Administration database, and published data series from academic neuro-oncology centers. Results Before 2000, the ratio of articles supporting survival benefit of GTR relative to those not supporting it ranged from approximately 1:5 to 1:1. Since 2000, this ratio has steadily increased such that by the post-2013 period, 32 of the 33 published articles (>30:1) supported the survival benefit of GTR. Although the frequency of GTR increased during the 2000–2004 period in the SEER and Veterans Health Administration database, no further increase in the frequency of GTR was observed thereafter. In contrast, resection rates in academic neuro-oncology centers continued to increase subsequent to 2004. Conclusions Our results indicate that clinical practice patterns mirror publication patterns for HGG resection, suggesting that neurosurgical oncology is a field in which clinical practice is informed by the peer-reviewed literature. [ABSTRACT FROM AUTHOR]

Twitter is increasingly of interest as a social media that can be used to help mitigate disasters. In this paper, we describe the strength and limitations of Twitter in this context, and identify the features needed in a twitter system to support disaster planning, warning and response. To orient the discussion, we describe a local context, Padang Indonesia. We then illustrate the kind of system needed by describing our implementation of TWRsms – a twitter tracking and analysis system for Padang Indonesia. Our implementation collects and analyzes tweets from the shoreline city of Padang Indonesia. This area is exposed to considerable risk from tsunamis, and the purpose of our system is to provide Indonesians information to plan for response to tsunamis, to provide immediate feedback about relevant tweeters in the wake of tsunami warnings, to support an early warning system, and to allow long-term research into how Indonesians tweet about disasters and other topics. We conclude by reviewing ongoing challenges: considering technical, social and policy issues and the challenge for science and the practical challenges of deploying such a system. [ABSTRACT FROM AUTHOR]

We describe a new methodology to infer sentiments held toward identities and behaviors from social events that we extract from a large corpus of newspaper text. Our approach draws on affect control theory, a mathematical model of how sentiment is encoded in social events and culturally shared views toward identities and behaviors. While most sentiment analysis approaches evaluate concepts on a single, evaluative dimension, our work extracts a three-dimensional sentiment "profile" for each concept. We can also infer when multiple sentiment profiles for a concept are likely to exist. We provide a case study of a large newspaper corpus on the Arab Spring, which helps to validate our approach. [ABSTRACT FROM AUTHOR]