Proactive content moderation requires platforms to rapidly and continuously evaluate the credibility of websites. Leveraging the direct and indirect paths users follow to unreliable websites, we develop a website credibility classification and discovery system that integrates both webgraph and large-scale social media contexts. We additionally introduce the concept of dredge words—terms or phrases for which unreliable domains rank highly on search engines—and provide the first exploration of their usage on social media. Our graph neural networks that combine webgraph and social media contexts generate to state-of-the-art results in website credibility classification and significantly improves the top-k identification of unreliable domains. Additionally, we release a novel dataset of dredge words, highlighting their strong connections to both social media and online commerce platforms.

The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to reliable domains. We build these interventions on the principles of fairness (penalize sites for what is in their control), generality (label/fact-check agnostic), targeted (increase the cost of adversarial behavior), and scalability (works at webscale). We refine our methods on small-scale webdata as a testbed and then generalize the interventions to a large-scale webgraph containing 93.9M domains and 1.6B edges. We demonstrate that our methods penalize unreliable domains far more than reliable domains in both settings and we explore multiple avenues to mitigate unintended effects on both the small-scale and large-scale webgraph experiments. These results indicate the potential of our approach to reduce the spread of misinformation and foster a more reliable online information ecosystem. This research contributes to the development of targeted strategies to enhance the trustworthiness and quality of search engine results, ultimately benefiting users, and the broader digital community.

Website reliability labels underpin almost all research in misinformation detection. However, misinformation sources often exhibit transient behavior, which makes many such labeled lists obsolete over time. We demonstrate that Search Engine Optimization (SEO) attributes provide strong signals for predicting news site reliability. We introduce a novel attributed webgraph dataset with labeled news domains and their connections to outlinking and backlinking domains. We demonstrate the success of graph neural networks in detecting news site reliability using these attributed webgraphs, and show that our baseline news site reliability classifier outperforms current SoTA methods on the PoliticalNews dataset, achieving an F1 score of 0.96. Finally, we introduce and evaluate a novel graph-based algorithm for discovering previously unknown misinformation news sources.

To influence the information landscape preceding and during the military invasion of Ukraine in February 2022, Russia initiated a disinformation campaign portraying Ukraine as a Nazi state. This study aims to compare discussions related to this campaign on Twitter and Telegram. The analysis reveals that the Nazis and Ukraine narrative was constant on Twitter but only emerged on Telegram after the invasion in channels that had previously focused on a broader set of conspiracy theories. Beyond the examination of Russian disinformation in this case study, the paper introduces an innovative methodology for constructing topic networks from social media data. This approach expands upon traditional topic modeling by incorporating the network properties of social media data to establish directed networks that characterize the interplay between conversation topics. Through this methodology, we gain the ability to observe topical evolutions, providing fresh insights into the disinformation campaign and its efficacy in shaping discussions around the Russian invasion on social media.

Among popular social media platforms, Reddit stands out for its decentralized approach to moderation and community management. Due to this and its community-based network structure, Reddit provides a unique environment for studying the diffusion of knowledge and beliefs over social media. While assortativity, polarization, and user behavior have been examined within empirical contexts, having the ability to model the impacts of different moderation policies and rules across communities could provide useful insights for limiting the spread of misinformation. In this work, we introduce an agent-based model of Reddit interactions and moderating actions. By simulating interactions at the user level and specifying user-specific attributes, our model allows practitioners to conduct experiments with various types of actors and moderators and study their potential impact on Reddit-facilitated discussions and information diffusion. Additionally, subreddit-specific attributes enable communities to have different standards and thresholds for user conduct. To validate this model, we rely on an empirical dataset of over 100K posts and 800K comments across three U.S. political events in addition to user surveys and studies.

Online social connections occur within a specific conversational context. Prior work in network analysis of social media data attempts to contextualize data through filtering. We propose a method of contextualizing online conversational connections automatically and illustrate this method with Twitter data. Specifically, we detail a graph neural network model capable of representing tweets in a vector space based on their text, hashtags, URLs, and neighboring tweets. Once tweets are represented, clusters of tweets uncover conversational contexts. We apply our method to a dataset with 4.5 million tweets discussing the 2020 US election. We find that even filtered data contains many different conversational contexts, with users engaging in multiple conversations. While users engage in multiple conversations, the overlap between any two pairs of conversations tends to be only 30-40%, giving very different networks for different conversations. Even accounting for this variation, we show that the relative social status of users varies considerably across contexts, with tau=0.472 on average. Our findings imply that standard network analysis on social media data can be unreliable in the face of multiple conversational contexts.

Despite rapid development, current bot detection models still face challenges in dealing with incomplete data and cross-platform applications. In this paper, we propose BotBuster, a social bot detector built with the concept of a mixture of experts approach. Each expert is trained to analyze a portion of account information, e.g. username, and are combined to estimate the probability that the account is a bot. Experiments on 10 Twitter datasets show that BotBuster outperforms popular bot-detection baselines (avg F1=73.54 vs avg F1=45.12). This is accompanied with F1=60.04 on a Reddit dataset and F1=60.92 on an external evaluation set. Further analysis shows that only 36 posts is required for a stable bot classification. Investigation shows that bot post features have changed across the years and can be difficult to differentiate from human features, making bot detection a difficult and ongoing problem.

Target-stance detection on large-scale datasets is a core component of many of the most common stance detection applications. However, despite progress in recent years, stance detection research primarily occurs at the document-level on small-scale data. We propose a highly efficient Twitter Stance Propagation Algorithm (TSPA) for detecting user-level stance on Twitter that leverages the social networks of Twitter users and runs in near-linear time. We find TSPA achieves SoTA accuracy against BERT, homogenous Graph Attention Networks (GAT), and heterogenous GAT baselines. Additionally, TSPA's wall-clock time was 10x faster than our best baseline on a GPU and over 100x faster than our best baseline on a CPU.

This case study investigates a recent Russian disinformation narrative about U.S. biolabs and the development of biological weapons in Ukraine. This disinformation campaign was officially initiated by the Russian government, including the Russian Ministry of Defense, and was disseminated by official state-funded Russian media. In their announcement, Russian officials condemned pharmaceutical companies such as Pfizer and Moderna, as well as the U.S. military, for developing biological weapons in Ukraine. Four datasets with tweets were collected in English and Russian to analyze narratives and communities involved in the Ukrainian biolabs conversations on Twitter.This study aims to investigate how this disinformation theme was propagated in two languages on Twitter. Twitter social media data analysis, network influence metrics, coordination analysis, Leiden clustering, and qualitative data analysis were used in this study. The results have demonstrated that this particular disinformation campaign targeted both English and Russian-speaking audiences with coordinated inauthentic amplification to spread this disinformation to support Russia’s invasion of Ukraine. However, we found that this narrative was also naturally propagated by anti-vax and conservative communities among English-speaking users on Twitter.

This paper investigates how hate speech varies in systematic ways according to the identities it targets.Across multiple hate speech datasets annotated for targeted identities, we find that classifiers trained on hate speech targeting specific identity groups struggle to generalize to other targeted identities.This provides empirical evidence for differences in hate speech by target identity; we then investigate which patterns structure this variation.We find that the targeted demographic category (e.g.gender/sexuality or race/ethnicity) appears to have a greater effect on the language of hate speech than does the relative social power of the targeted identity group.We also find that words associated with hate speech targeting specific identities often relate to stereotypes, histories of oppression, current social movements, and other social contexts specific to identities.These experiments suggest the importance of considering targeted identity, as well as the social contexts associated with these identities, in automated hate speech classification.Warning: This paper contains offensive and hateful terms and concepts.We have chosen to reproduce these terms for clarity in aiding efforts against hate speech.

Weak student orientation for university students can create several problems such as skill mismatch and a high rate of unemployment. Youth often choose education paths that do not qualify them for employment, mainly because they lack reliable information about job market needs. Despite this issue, no previous research has attempted to connect university curricula to up-to-date job market needs. Youth usually rely on information from their families and friends and sometimes go to career centers to meet with career counselors. However, the resources youth go to do not have an overview of the job market needs. Students need to be provided with information about the job market needs and be aware of universities’ training that they can follow. This paper collects data from multiple sources such as job portals and universities websites to inform students about the best options for their future university training plans based on up-to-date job market needs for each occupation. In this study, we focus on the IT sector because it is the sector recruiting the most. We use machine learning algorithms to classify job ads into their corresponding occupation and then use text analysis techniques to extract requirements and skills from job ads. Furthermore, we connect jobs with universities training to build an orientation path using text analysis and the assistance of HR specialists. Our paper finds that the occupations related to programming are the most in-demand jobs for the IT sector and sometimes don’t require years of experience. We also identified emerging occupations such as data scientists who doubled the number of job openings within a year. When comparing university curricula to our job market findings, we found a small mismatch between the university offerings and the number of job ads in some occupations. Based on the results, a Dashboard will be developed to help students choose a path that will lead them in having a job.

Stance detection identifies a person’s evaluation of a subject, and is a crucial component for many downstream applications. In application, stance detection requires training a machine learning model on an annotated dataset and applying the model on another to predict stances of text snippets. This cross-dataset model generalization poses three central questions, which we investigate using stance classification models on 7 publicly available English Twitter datasets ranging from 297 to 48,284 instances. (1) Are stance classification models generalizable across datasets? We construct a single dataset model to train/test dataset-against-dataset, finding models do not generalize well (avg F1=0.33). (2) Can we improve the generalizability by aggregating datasets? We find a multi dataset model built on the aggregation of datasets has an improved performance (avg F1=0.69). (3) Given a model built on multiple datasets, how much additional data is required to fine-tune it? We find it challenging to ascertain a minimum number of data points due to the lack of pattern in performance. Investigating possible reasons for the choppy model performance we find that texts are not easily differentiable by stances, nor are annotations consistent within and across datasets. Our observations emphasize the need for an aggregated dataset as well as consistent labels for the generalizability of models.

This paper posits and tests a social cybersecurity framework to detect and characterize online trolling. Using a dataset of online trolling obtained through active learning, we empirically find that troll messages are significantly associated with more abusive language (p<.001), lower cognitive complexity (p<.01), and greater targeting of named entities (p<.05) and identities (p<.05). These effects are robust to the likelihood that these messages come from bots. We then train and evaluate TrollHunter, a theory-driven and interpretable machine learning model using the derived psycholinguistic features. TrollHunter achieves 89% accuracy and F1 score in detecting trolling messages, with an average 12.25% improvement in performance when relationally modeling conversational context. Explorations of convergent and discriminant validity reveal that our measure of trolling is more closely related to non-hateful offensive speech over hate speech, aggressive over non-aggressive speech, and that Chinese state-sponsored accounts engage in higher levels of trolling than Russian state-sponsored accounts (p<.001). Finally, we apply TrollHunter in a field study to compare the media targets of trolling activity compared to bots as a reference group. Bots dominate replies to exclusive right-leaning media outlets like Breitbart and Newsmax, while trolls disproportionately target outlets with mixed partisan trust like BBC and ABC. This bifurcation suggests that not only are trolls and bots different entities, but they also have different impacts in relation to driving polarization and disinformation in society. Echoing recent calls for interdisciplinary approaches that link computational models with social theory, we conclude with implications for platform regulation and policy-making to curtail the actions of diverse agents of disinformation.

Organizational risk and resilience as well as insider threat have been studied through the lenses of socio-psychological studies and information and computer sciences. As with all disciplines, it is an area in which practitioners, enthusiasts, and experts discuss the theory, issues, and solutions of the field in various online public forums. Such conversations, despite their public nature, can be difficult to understand and to study, even by those deeply involved in the communities themselves. Who are the key actors? How can we understand and characterize the culture around such communities, the problems they face, and the solutions favored by the experts in the field? Which narratives are being created and propagated, and by whom - and are these actors truly people, or are they autonomous agents, or "bots"?

Discussion about the interference of Russian actors in the 2016 U.S. presidential election campaign attracted enormous attention from the academic community. Numerous studies dedicated to the analysis of Internet operations, as well as activities of bots and trolls, formed a new interdisciplinary area that investigates online disinformation and computational propaganda. This study provides an analysis of a case study with Russian propaganda operations that focus on the internal political confrontation between the Russian systemic political establishment and opposition movement of Alexei Navalny. We present an analysis of how Internet trolls and sockpuppets are used to conduct information disorder activities in order to frame the discussion around the opposition movement in Russia on Twitter. We also identified attempts to manipulate the opinion of the Western audience and to spread disinformation about Western democracies by the same malicious actors. The study implements network analysis for identifying disinformation and propaganda trolls.Preliminary findings demonstrate that there is evidence of information campaigns against Alexei Navalny as one of the leaders of the Russian opposition. We observe how an internal issue is framed in the context of Russian confrontation with the West and how it is used to promote hostile narratives with the claims that Alexei Navalny is supported by the Western governments and therefore is an enemy of the Russian state. Many agents from our sample pretend to be real people, English speakers, who exhibit hostile attitudes towards Navalny and the Western democracies, promoting a lack of trust in the democratic institutions as well as spreading disinformation and conspiracy theories.

Neural networks are increasingly used for graph classification in a variety of contexts. Social media is a critical application area in this space, however the characteristics of social media graphs differ from those seen in most popular benchmark datasets. Social networks tend to be large and sparse, while benchmarks are small and dense. Classically, large and sparse networks are analyzed by studying the distribution of local properties. Inspired by this, we introduce Graph-Hist: an end-to-end architecture that extracts a graph's latent local features, bins nodes together along 1-D cross sections of the feature space, and classifies the graph based on this multi-channel histogram. We show that Graph-Hist improves state of the art performance on true social media benchmark datasets, while still performing well on other benchmarks. Finally, we demonstrate Graph-Hist's performance by conducting bot detection in social media. While sophisticated bot and cyborg accounts increasingly evade traditional detection methods, they leave artificial artifacts in their conversational graph that are detected through graph classification. We apply Graph-Hist to classify these conversational graphs. In the process, we confirm that social media graphs are different than most baselines and that Graph-Hist outperforms existing bot-detection models.

Online social media has become an important platform to organize around different socio-cultural and political topics. An extensive scholarship has discussed how people are divided into echo-chamber-like groups. However, there is a lack of work related to quantifying hostile communication or affective polarization between two competing groups. This paper proposes a systematic, network-based methodology for examining affective polarization in online conversations. Further, we apply our framework to 100 weeks of Twitter discourse about climate change. We find that deniers of climate change (Disbelievers) are more hostile towards people who believe (Believers) in the anthropogenic cause of climate change than vice versa. Moreover, Disbelievers use more words and hashtags related to natural disasters during more hostile weeks as compared to Believers. These findings bear implications for studying affective polarization in online discourse, especially concerning the subject of climate change. Lastly, we discuss our findings in the context of increasingly important climate change communication research.

The rapid growth of scientific publications brings the problem of finding appropriate citations for authors. Context-aware citation recommendation is an essential technology to overcome this obstacle when given a fragment of manuscript. In this article, we propose a novel neural network model for context-aware citation recommendation by combining stacked denoising autoencoders (SDAE) and Bi-LSTM. To obtain effective embedding for cited paper, we extend SDAE into attentive SDAE (ASDAE) by utilizing the attentive information from citation context, which essentially enhance the learning ability of original SDAE. For citation context, we devise an attentive Bi-LSTM to obtain effective embedding. Specifically, the attentive Bi-LSTM is able to extract suitable citation context and recommend citations simultaneously when given a long text, which is a issue that few papers addressed before. We also integrate personalized author information to improve the performance of recommendation. Our model is essentially a seemly integration of different types of neural network with latent variables. We derive the generative process of our model, and develop a learning algorithm based on maximum a posteriori (MAP) estimation. Experimental results on the RefSeer, ANN and DBLP datasets show that our model outperforms baseline methods.

Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve state-of-the-art performance, outperforming the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively.

Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.

We use Twitter streaming API for many purposes like monitoring brands and discovering events. Because Twitter Streaming API only allows tracking words (commonly called 'search-terms'), the data collection goal needs to be formulated in terms of search terms. Twitter limits the number of search terms that can be tracked using the API, and the number of tweets retrieved per search-term depends on the terms being tracked. Therefore it's crucial to use a small set of highly relevant terms for tracking.

Cybersecurity jobs are becoming more and more in demand for companies and governmental organizations, especially with the increasing number of cyber-threats in the world. In order to keep up with these changes, a study for the cybersecurity job market needs is required in order to help universities adapt their curricula and train more skilled graduates. However, most of prior work focuses on IT jobs and overlooks the cybersecurity field which makes the analysis of the needs of this field ambiguous. In this paper, we collect job ads from 9 Moroccan job portals from February 2017 to June 2018. After removing duplicates, we result in 409 job ads. Then, we split the data into two categories that are cybersecurity jobs and IT jobs that require cybersecurity. We use text analysis techniques to extract skills and requirements listed in these ads. More specifically, we extract the education level, programming languages, natural languages, years of experience, certifications and types of contract listed in these ads. Our paper finds that skills that are highly requested, such as risk analysis methods and certificates are not emphasized in cybersecurity-specific curriculum, while skills that are less in demand, such as a range of programming languages, are taught more regularly in universities. Additionally, more jobs require some understanding of English than of French, which was surprising given prevalence of French in Morocco. Our results can be used to reshape universities training in order to meet the demand of the job market.

Binxuan Huang, Kathleen Carley. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Binxuan Huang, Kathleen Carley. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Roll call modeling is an essential component of analyzing a political system. Current models focus on individual decision-making, and most of them do not take advantage of voting dynamics. Some political systems, such as Ukraine's Verkhovna Rada, are inherently dynamic and should be modeled as such. Therefore, a roll call model is developed from a linear second-order homogeneous differential equation. This model equation is fit to Verkhovna Rada votes from the seventh and eighth convocations. The model determines whether or not bills will reach the passing threshold with 77% and 85% accuracy for the seventh and eighth convocations, respectively. It is shown that the dynamic legislative model is slightly less accurate than a neural network, but it is significantly more interpretable. This interpretability is vitally important, as it is what makes models meaningful beyond their predictive power. It is found that bills sponsored by the president show quantitatively different behavior than ordinary bills and the ordinary bills are largely decided in the first two votes. Furthermore, our models have intuitive theoretical implications, some of which are back by prior work. The models suggest that MPs are less willing to change their vote on bills as iterations increase and they are more sensitive to change the public opinion if the bill is sponsored by the president. While the majority of bills are modeled well, about 25% of votes have greater than 10% error. Investigation of these votes indicates that some votes may be impossible to predict without a more complex model which incorporates contextual information. Finally, the information from a bill's first two votes is also leveraged through a vote switching network. This directed network gives insight into who sends the most powerful signals and who follows them. An ensemble of centrality members is then used to identify the legislator's most influential members.

Automated social media bots have existed almost as long as the social media platforms they inhabit. Although efforts have long existed to detect and characterize these autonomous agents, these efforts have redoubled in the recent months following sophisticated deployment of bots by state and non-state actors. This research will study the differences between human and bot social communication networks by conducting an account snow ball data collection, and then evaluate features derived from this communication network in several bot detection machine learning models.

The offshore sector creates a large number of job opportunities in Morocco. Analyzing job ads related to that sector can help universities adapt their curricula in order to produce more employable graduates. Unfortunately, analyzing these ads is challenging because they are mostly non-structured. Most prior work, however, focuses on analyzing structured and semi-structured job ads through keyword search and regular expressions. In this work, we collect and analyze job ads related to the offshore sector in Morocco over the period February-August 2017. We use a variety of machine learning and text mining techniques to process these ads. We examine the natural languages, programming languages, education level, and years of experience needed. We also examine contract type and salary when available. Our results reveal that French is the most needed language in offshore jobs, but that other foreign languages (English and Spanish) are also needed. We also find that the most needed offshore IT jobs are development and web design jobs. The most needed programming languages for these jobs are Java, SQL, JavaScript and PHP.

We introduce a novel parameterized convolutional neural network for aspect level sentiment classification.Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN).Experiments demonstrate that our parameterized filters and parameterized gates effectively capture the aspectspecific features, and our CNN-based models achieve excellent results on SemEval 2014 datasets.

A mobility event occurs when a passenger moves out or takes off from a particular location. Mobility event prediction is of utmost importance in the field of intelligent transportation systems. It has a huge potential in solving important problems such as minimizing passenger waiting time and maximizing the utilization of the transportation resources by planning vehicle routes and dispatching transportation resources. Recently, numerous mobility pattern mining methods have been proposed to predict the transportation supply and demand in different locations. Those methods first reveal the event patterns of each Place of Interests (POI) independently and then employ a separate region function as a post-processing step. This separate process, that disregards the intrinsic spatial and temporal pattern correlations between POI, is sub-optimal and complex, resulting in a poor generalization in different scenarios. In this work, we propose a Spatial-Temporal mobility Event Prediction framework based on Deep neural network (StepDeep) for simultaneously taking into account all correlated spatial and temporal mobility patterns. StepDeep not only simplifies the prediction process but also enhances the prediction accuracy. Our StepDeep proposes a novel problem formulation towards an end-to-end mobility prediction framework, that is, switching mobility events over time in an area into an event video and then posing the mobility prediction problem as a video prediction task. Such a novel formulation can naturally encode spatial and temporal dependencies for each POI. StepDeep thus predicts the spatial-temporal events by incorporating the new time sensitive convolution filters, spatial sensitive convolution filters, and spatial-temporal sensitive convolution filters into a single network. We conduct experimental evaluations on a real-world 547-day New York City taxi trajectory dataset, which show that StepDeep provides higher prediction accuracy than five existing baselines. Moreover, StepDeep is generalizable and can be applied to numerous spatial-temporal event prediction scenarios.

Social Cyber-Security is an emerging scientific and engineering area. It is an applied field focused on the science to characterize, understand, and forecast cyber-mediated changes in human behavior and activity, and the related social, cultural, economic and political outcomes. From the engineering side it is concerned with building the cyber-infrastructure needed for society to persist in its essential character in a cyber-mediated information environment under changing conditions, in the presence of actual or imminent social cyber-threats. Research in social cyber-security has demonstrated that social media and personalized data assistants are critical technologies that affect the ways humans navigate this cyber-mediated information space, and the way they interact and engage in discussions. This impact is complex and is related to the human socio-cognitive response, engineered features of the technologies, and the policy/legal environment in which humans and technology interact. The changing nature of interaction in the cyber-mediated information environment are creating new scientific and policy challenges that those in social cyber-security must address. This talk will describe the nature, promise of, and challenges in social cyber-security. The role of network science and AI, as to of the core methodologies, in addressing these challenges is addressed. Illustrative examples will be drawn from areas such as disaster response, state stability, and the distribution of false information.

Network analytics are widely used in many fields. Increasingly though, the networks of interest are high dimensional. Rather than just focusing on the traditional who is interaction with whom networks, modern network analysis examines the who, what, where, when and why ecology of relations. For example, studies of organizations might look at the social network within the organization as well as the task assignment and knowledge network. Studies using Twitter frequently look at the mentions network at the same time as the hashtag network. Making network science actionable typically involves answering questions about where the network is concentrated or how the network varies over time. This tutorial will cover the basics of Network analytics and demonstrate the use of ORA to support social media analytics using high dimensional network analytics and visualization for social media networks, semantic networks, geo-spatial networks, and dynamic networks. It will cover the basics of visualizing and analyzing time varying networks and spatially positioned networks. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. ORA is a powerful network analysis and visualization tool (Carley, 2017). ORA supports the assessment of standard social network data, organizational network data, high-dimensional network data, meta-network data, geo-spatial network data, and dynamic network data.

Predicting countries that will seek bioweapons (BW) enables the international community to act early in order to prevent these countries from acquiring such weapons. Unfortunately, the literature on countries' BW programs mainly consists of case studies that focus on one or a few countries. Although case studies are valuable, they are typically not predictive. Moreover, case studies require substantial effort and expertise, and are thus unfeasible for all countries. In this paper, we develop a computational methodology that predicts countries that will seek BW. Our methodology consists of a sociocultural model and indicators that computationally capture expert opinions about why and how countries acquire BW. Our methodology systematically examines all countries in the world and can be used by non-BW experts based on publicly available data. We validate our methodology by examining the methodology's ability to predict historical BW proliferators.

The study of information flow typically does not distinguish the choices of tie strength on which the information flows. All receivers of the information are assumed to have the same potential to pass on the information. Modifying the SEIZ (susceptible, exposed, infected, skeptic) model, we discover that people choose to retweet strong or weak ties based on the topic. We made two modifications in the model. In the first modification (Model I), we assume that the contact rates of agents in different compartment and the probability of an agent transitioning from one compartment to another are different for strong ties and weak ties. In the second modification (Model II), we assume that only the probability of transitioning is different for strong ties and weak ties. We discover that people do not discriminate strong ties and weak ties when retweeting controversial topic, perhaps because this topic can both be personal and breaking news. On the other hand, people discriminate strong ties and weak ties when retweeting non-controversial topic. They prefer to retweet strong ties when the topic is donation, and kids, and weak ties when the topic is news on hurricane and music. Meanwhile, SEIZ model and its modifications are found to be inadequate to model tweets on event promotion.

Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection. While tweet text is the most commonly used feature in location estimation, most of the prior works suffer from either the noise or the sparsity of textual features. In this paper, we aim to tackle these two problems. We use topic modeling as a building block to characterize the geographic topic variation and lexical variation so that "one-hot" encoding vectors will no longer be directly used. We also incorporate other features which can be extracted through the Twitter streaming API to overcome the noise problem. Experimental results show that our RATE algorithm outperforms several benchmark methods, both in the precision of region classification and the mean distance error of latitude and longitude regression.

Social identities carry widely agreed upon meanings, called stereotypes, that have important effects on social processes. In the present work, we develop a method to extract the stereotypes of Twitter users. Our method is grounded in two distinct strands of theory, one that represents stereotypes as identities' affective meanings and the other that represents stereotypes as semantic relationships between identities. After validating our approach via a prediction task, we apply the model to a dataset of 45 thousand Twitter users who actively tweeted about the Michael Brown and Eric Garner tragedies. Our work provides unique insights into the stereotypes of these users, as well as providing a way of quantifying stereotypes that blends existing sociological and psychological theory in a novel, parsimonious way.

Due to the rapid increase in the number of users owning location-based devices, there is a considerable amount of geo-tagged data available on social media websites, such as Twitter and Facebook. This geo-tagged data can be useful in a variety of ways to extract location-specific information, as well as to comprehend the variation of information across different geographical regions. A lot of techniques have been proposed for extracting location-based information from social media, but none of these techniques aim to utilize an important characteristic of this data, which is the presence of aspects and their opinions, expressed by the users on these platforms. In this paper, we propose Geographic Aspect Opinion model (GASPOP), a probabilistic model that jointly discovers the variation of aspect and opinion, that correspond to different topics across various geographical regions from geo-tagged social media data. It incorporates the syntactic features of text in the generative process to differentiate aspect and opinion words from general background words. The user-based modeling of topics, also enables it to determine the interest distribution of various users. Furthermore, our model can be used to predict the location of different tweets based on their text. We evaluated our model on Twitter data, and our experimental results show that GASPOP can jointly discover latent aspect and opinion words for different topics across latent geographical regions. Moreover, a quantitative analysis of GASPOP using widely used evaluation metrics shows that it outperforms the state-of-the-art methods.

Through the development of a computational sociocultural model, we demonstrate the potential utility of a rapid-modeling approach for exploring the dynamics of nuclear deterrence. We review the past literature, showing how our model is influenced by both the domestic politics and constructivist paradigm models of nuclear deterrence. Our model development process incorporates expert elicitation into a model featuring cognitively limited, perception error-prone, belief holding, and knowledge diffusing actors. Results from both an intellective and specific scenario were generated through running the model across various strategies and courses of action (CoAs). Third-party experts validated the model's shortand long-term results and contribution to stakeholders exploring alternative CoAs. Work remains in validating this paper against empirical data sets and in automating the model construction process.

The presence of bots has been felt in many aspects of social media. Twitter, one example of social media, has especially felt the impact, with bots accounting for a large portion of its users. These bots have been used for malicious tasks such as spreading false information about political candidates and inflating the perceived popularity of celebrities. Furthermore, these bots can change the results of common analyses performed on social media. It is important that researchers and practitioners have tools in their arsenal to remove them. Approaches exist to remove bots, however they focus on precision to evaluate their model at the cost of recall. This means that while these approaches are almost always correct in the bots they delete, they ultimately delete very few, thus many bots remain. We propose a model which increases the recall in detecting bots, allowing a researcher to delete more bots. We evaluate our model on two real-world social media datasets and show that our detection algorithm removes more bots from a dataset than current approaches.

There is a general belief that cyber attacks on the United States are increasing, as indicated by the 30% increase in cyber-attack related news from 2014 to 2015. During the same time frame, however, the USA government enacted several policy measures meant to reduce or mitigate cyber-attacks. These recent developments have lead us to ask two questions, a) Have cyber-attacks on the United States actually increased, as perceived in the news media? b) What is the impact of changes in US cyber-policy on this trend in cyber-attacks? Our initial investigation reveals that in contrast to the perception, there has been a drop in the number of DDoS cyber-attacks on the USA. In this research, we compare the perceptions and the reality of cyber attacks by considering recent trends in cyber-related news and cyber-attacks (specifically DDoS type attacks). The analysis makes three important contributions: a) Using GDelt data, we show that from 2014 to 2015 the amount of news referring to cyber events have increased, but the sentiment expressed in such news has become more negative. b) Using DDoS-attacks data shared by Arbor Network, we show that from 2014 to 2016, there has been a marked decrease in the number of cyber-attacks on the USA. c) Using a time series intervention analysis, we show that the decline in cyber-attacks appears to be related to the changes in US cyber policy. In particular, the US President's authorization to prosecute malicious cyber actors significantly decreased the DDoS-attacks on the US.

Cyber-attacks aimed at breaking into networks and bringing websites down appear to have become an every-day phenomenon, but there is less clarity on where the attacks come from and who are the top targets. We used DDoS attacks data shared by Arbor Networks, from June 2013 to Mar 2016, to understand the cyber-attacks network. We take a high-level view of attacks mostly considering aggregate country-to-country attacks, using which we summarize the major players and trends in DDoS cyber-attacks.

Cyber-attacks are cheap, easy to conduct and often pose little risk in terms of attribution, but their impact could be lasting. The low attribution is because tracing cyber-attacks is primitive in the current network architecture. Moreover, even when attribution is known, the absence of enforcement provisions in international law makes cyber attacks tough to litigate, and hence attribution is hardly a deterrent. Rather than attributing attacks, we can re-look at cyber-attacks as societal events associated with social, political, economic and cultural (SPEC) motivations. Because it is possible to observe SPEC motives on the internet, social media data could be valuable in understanding cyber attacks. In this research, we use sentiment in Twitter posts to observe country-to-country perceptions, and Arbor Networks data to build ground truth of country-to-country DDoS cyber-attacks. Using this dataset, this research makes three important contributions: a) We evaluate the impact of heightened sentiments towards a country on the trend of cyber-attacks received by the country. We find that, for some countries, the probability of attacks increases by up to 27% while experiencing negative sentiments from other nations. b) Using cyber-attacks trend and sentiments trend, we build a decision tree model to find attacks that could be related to extreme sentiments. c) To verify our model, we describe three examples in which cyber-attacks follow increased tension between nations, as perceived in social media.

Cyber-attacks appear to have grown in the past few years. Many policy changes have been made to control them, however, a clear path to a safe and secure cyber world is not visible. A mere technical view to confront the cyber-attacks could be futile if motivations behind attacks are not understood. To understand the motives behind cyber-attacks, we look at cyber-attacks as societal events associated with social, economic, cultural and political factors. To find factors that encourage unsafe cyber activities, we build a network of aggregate country-to-country cyber-attacks, and compare the network with other country-to-country networks. In particular, we use variable correlation and network correlation (QAP) to examine the relevance of factors like income difference, alliance-hostility, internet penetration, and corruption. We observe that higher corruption and a large internet bandwidth favors attacks origination. We also find that countries with higher Per-capita-GDP and better Information and Communication Technologies (ICT) infrastructure are targeted more often.

Sociologists have long been interested in the ways that identities, or labels for people, are created, used and applied across various social contexts. The present work makes two contributions to the study of identity, in particular the study of identity in text. We first consider the following novel NLP task: given a set of text data (here, from Twitter), label each word in the text as being representative of a (possibly multi-word) identity. To address this task, we develop a comprehensive feature set that leverages several avenues of recent NLP work on Twitter and use these features to train a supervised classifier. Our model outperforms a surprisingly strong rule-based baseline by 33%. We then use our model for a case study, applying it to a large corpora of Twitter data from users who actively discussed the Eric Garner and Michael Brown cases. Among other findings, we observe that the identities used by individuals differ in interesting ways based on social context measures derived from census data.

Social media is rapidly becoming one of the mediums of choice for understanding the cultural pulse of a region; e.g., for identifying what the population is concerned with and what kind of help is needed in a crisis. To assess this cultural pulse it is critical to have an accurate assessment of who is saying what in social media. However, social media is also the home of malicious users engaged in disruptive, disingenuous, and potentially illegal activity. A range of users, both human and non-human, carry out such social cyber-attacks. We ask, to what extent does the presence or absence of such users influence our ability to assess the cultural pulse of a region? We conduct a series of experiments to analyze the fragility of social network assessments based on Twitter data by comparing changes in both the structural and content results when suspended users are left in and taken out. Because a Twitter account can be suspended for various reasons including spamming or spreading ideas that can lead to extremism or terrorism, we separately assess the impacts of removing apparent spam bots and apparent extremists. Experimental results demonstrate that Twitter-based network structures and content are unstable, and can be highly impacted by the removal of suspended users. Further, the results exhibit regional and temporal variation that may be related to the political situation or civil unrest. We also provides guidance on the differential impact of different types of potentially suspendable users.

Given social networks over time, how can we measure network activities across different timesteps with a limited number of metrics? We propose two classes of dynamic metrics for assessing temporal evolution patterns of agents in terms of persistency and emergence. For each class of dynamic metrics, we implement it using three different temporal aggregation models ranging from the most commonly used Average Aggregation Model to more the complex models such as the Exponential Aggregation Model. We argue that the problem of measuring temporal patterns can be formulated using Recency and Primacy effect, which is a concept used to characterize human cognitive processes. Experimental results show that the way metrics model Recency--Primacy effect is closely related to their abilities to measure temporal patterns. Furthermore, our results indicate that future network agent activities can be predicted based on history information using dynamic metrics. By conducting multiple experiments, we are also able to find an optimal length of history information that is most relevant to future activities. This optimal length is highly consistent within a dataset and can be used as an intrinsic metric to evaluate a dynamic social network.

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. More specifically, we first test the extent to which communities detected via Latent Dirichlet Allocation are homogenous across a set of four social constraints—racial homophily, income homophily, personal interest homophily and physical space. Using a bootstrapping approach, we find that 14 (of 20) communities are statistically, and all but one qualitatively, homogenous along one of these social constraints, showing the relevance of Blau’s latent space model in venue communities determined via user check-in behavior. We then consider the extent to which communities with consolidated parameters, those homogenous on more than one parameter, represent socially isolated populations. We find communities homogenous on multiple parameters, including a homosexual community and a “hipster” community, that show support for Blau’s hypothesis that consolidation breeds social isolation. We consider these results in the context of mediated communication, in particular in the context of self-representation on social media.

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.

When a crisis occurs, there is often little time to evaluate the situation and determine how best to respond. We use rapid ethnographic methods centered on the construction of geo-temporally contextualized social and knowledge networks. By utilizing a combination of Twitter and news media, the consulate attack in Libya were examined in near real time. In this work we outline a procedure to extract key insights from the event as an event unfolds using a suite of tools developed by a team of researchers from two universities.

Automation of data collection using online resources has led to significant changes in traditional practices of social network analysis. Social network analysis has been an active research field for many decades; however, most of the early work employed very small datasets. In this paper, a number of issues with traditional practices of social network analysis in the context of dynamic, large-scale social networks are pointed out. Given the continuously evolving nature of modern online social networking, we postulate that social network analysis solutions based on incremental algorithms will become more important to address high computation times for large, streaming, over-time datasets. Incremental algorithms can benefit from early pruning by updating the affected parts only when an incremental update is made in the network. This paper provides an example of this case by demonstrating the design of an incremental closeness centrality algorithm that supports efficient computation of all-pairs of shortest paths and closeness centrality in dynamic social networks that are continuously updated by addition, removal, and modification of nodes and edges. Our results obtained on various synthetic and real-life datasets provide significant speedups over the most commonly used method of computing closeness centrality, suggesting that incremental algorithm design is a fruitful research area for social network analysts.

The increasing availability of dynamically growing digital data that can be used for extracting social networks has led to an upsurge of interest in the analysis of dynamic social networks. One key aspect of social network analysis is to understand the central nodes in a network. However, dynamic calculation of centrality values for rapidly growing networks might be unfeasibly expensive, especially if it involves recalculation from scratch for each time period. This paper proposes an incremental algorithm that effectively updates betweenness centralities of nodes in dynamic social networks while avoiding re-computations by exploiting information from earlier computations. Our performance results suggest that our incremental betweenness algorithm can achieve substantial performance speedup, on the order of thousands of times, over the state of the art, including the best-performing non-incremental betweenness algorithm and a recently proposed betweenness update algorithm.

Validating a computational model is important as it establishes that the model has met its intended purpose of representing the system under study. In this paper, we perform a validation study on Construct, a multiagent network model for the coevolution of agents and the sociocultural environments that they inhabit. In particular, we focus on validating agent interactions produced by the model against empirical communication networks collected in real-world organizations. Validation is performed using our novel calibrated grounding technique. Results show that Construct can produce valid agent interactions. The benefits and implications of the study are discussed.

We propose a system which allows users to select how and with whom they are available for contact by managing a set of personal "identities" and budding relationships. The most important of these media are public displays, which allow a low-cost barrier for finding potential friends through interaction via the display and through the attendance of displayed social events. In helping two people to both become aware of each other's existence and to pick media which suit their preferences, we believe such a system can increase the odds of two strangers forming a strong social bond.

In this work, we use foursquare check-ins to cluster users via topic modeling, a technique commonly used to classify text documents according to latent "themes". Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user check ins. Our model is agnostic of geo-spatial location, time, users' friends on social networking sites and the venue categories-we treat the existence of and intricate interactions between these factors as being latent, allowing them to emerge entirely from the data. We instantiate our model on data from New York and the San Francisco Bay Area and find evidence that the model is able to identify groups of people which are of different types (e.g. tourists), communities (e.g. users tightly clustered in space) and interests (e.g. people who enjoy athletics).

There are many factors that provide input into the software development process, such as the values, beliefs, norms, practices, skills, behaviors, knowledge and goals of stakeholders. Research has shown that successful software system development relies on alignment or congruence between these factors. How to monitor the level of congruence between these factors and how to use the congruence as an indicator or a measure to monitor a software development process is a challenge in software engineering. This paper proposes a model that uses three congruence measures to examine the levels of social-technical congruence in software development processes. Using a controlled experiment with seven student teams developing a robot project, this paper demonstrates that the proposed congruence measures provide results consistent with the assessment by the course lecturers.

The growing complexity of software has posed challenges for the design, understanding and maintenance of software. Software developers often spend a lot of effort on understanding how software components are connected. Through analysis of the data obtained from a real software application, this paper demonstrates that network analysis approaches can help understand software component interconnections and their impact on software quality. Additionally, this paper shows that there is a correlation between the number of bugs found in an application and a set of measures used in component (interconnection) networks. Furthermore, a case study showed that the software performance improved over two releases as a result of improving the component network.

The capability to build nuclear weapons is a key national security factor that has a profound influence on the balance of international relations. In addition to longstanding players, regional powers and peripheral countries have sought for ways of acquiring and/or developing them. The authors postulate that to express the capabilities, relative positions, and interrelations of the countries involved in the production of nuclear weaponization knowledge, dynamic network analysis provides valuable insight. In this article, the authors use a computational framework that combines techniques from dynamic network analysis and text mining to mine and analyze large‐scale networks that are extracted from open theoretical and experimental nuclear research publications of the last two decades. More specifically, they build interlinked, dynamic networks that model relationships of nuclear researchers based on the open literature and supplement this information with text mining to classify the nuclear weaponization capabilities of each publication—of each author, organization, city, and country. Using such a comprehensive computational framework, they are able to (a) elicit the hot topics in nuclear weaponization research, (b) assess the nuclear expertise level of each country, (c) differentiate between established and emergent players, and (d) identify the key entities at various levels such as organization, city, and country.

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.

An increasingly important class of software-based systems is platforms that permit integration of third-party components, services, and tools. Service-Oriented Architecture (SOA) is one such platform that has been successful in providing integration and distribution in the business domain, and could be effective in other domains (e.g., scientific computing, healthcare, and complex decision making). In this paper, we discuss our application of SOA to provide an integration platform for socio-cultural analysis, a domain that, through models, tries to understand, analyze and predict relationships in large complex social systems. In developing this platform, called SORASCS, we had to overcome issues we believe are generally applicable to any application of SOA within a domain that involves technically naïve users and seeks to establish a sustainable software ecosystem based on a common integration platform. We discuss these issues, the lessons learned about the kinds of problems that occur, and pathways toward a solution.

The rapid diffusion of information and opinions through social media, such as web forums and micro-blogs, is affecting the development of crisis situations, such as the Iranian presidential election, the Egyptian protest, and the ROKS Cheonan sinking. Understanding this rapid widespread diffusion, and assessing what information is spreading, what ideas are becoming common, and who is talking about what, is critical for crisis management. This paper presents a computational system for social media assessing the flow of ideas on the web and changes in who is talking about what. This system, given raw social media data, identifies the key topics, the key paths by which topics evolve, the key individuals who contribute to the topic, and the key influence relations between the contributors. We present this system implemented with the Author-Topic model, the meta-network model, and various computational techniques to find and filter the heavy contributors and influences. We demonstrate the performance of the system, by applying it to social media data surrounding the ROKS Cheonan sinking. We describe the results of assessing the initial and changing perceptions of the event using this system.

Anaphora resolution (AR) identifies the entities that pronouns refer to. Coreference resolution (CR) associates the various instances of an entity with each other. Given our data, our findings suggest that deduplicating and normalizing text data by using AR and CR impacts the literal mention, frequency, identity, and existence of about 75% of the entities in texts. Results are more moderate on the relation level: 13% of the links are modified and 8% are removed. Performing social network analysis on the relations extracted from texts leads to findings contrary to the results from corpus statistics: AR and CR cause different directions in the change of network analytical measures, AR alters these measures more strongly than CR does, and each technique identifies a different set of most crucial nodes. Bringing the results from corpus statistics and social network analysis together suggests that CR is more effective in normalizing entities, while AR is a more powerful technique for splitting up generic nodes into named entities with adjusted weights. Data changes due to AR and CR are qualitatively and quantitatively meaningful: the statistical properties of entities and relations change along with their identities. Consequently, the relational data represent the underlying social structure more truthfully. Our results can support analysts in eliminating some misinterpretations of graphs distilled from texts and in selected those nodes from social networks on which reference resolution should be performed.

We present a brief report on a controlled experiment that provides valuable statistics to network-oriented defence analysts involved in threat identification. These statistics estimate the accuracy of the top-central actor findings that have been derived from relational data classically found in real-world datasets, such as those collected on distributed, covert organizations. Our experiment involved cellular social-networks with four types of data error: missing links, missing actors, extra links, and extra actors. We provide statistical results for top threat identification from the perspective of four traditional measures of network centrality: degree, betweenness, closeness and eigenvector. The results from our experiment provide a statistical estimate of the accuracy of the top-1 and top-3 actors as indicated by the observed data. Using these statistics a quantitative indication of reliability can be provided along with defence intelligence estimates of covert-organization leadership derived from relational network data. We provide lookup tables for the specific situations created for this experiment, from which other conditions may be loosely estimated. This work has highly practical implications for operational analysts and consumers of such analyses, particularly in the terrorist network and drug-trafficking domains. This work also lays the groundwork for developing more intricate estimates of reliability for other network-related, analytic tasks of analysts - from more extensive key-actor identification tasks to assessing the statistical reliability of the centrality measures in and of themselves.

Abstract 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.

The identification and management of work dependencies is a fundamental challenge in software development organizations. This paper argues that modularization, the traditional technique intended to reduce interdependencies among components of a system, has serious limitations in the context of software development. We build on the idea of congruence, proposed in our prior work, to examine the relationship between the structure of technical and work dependencies and the impact of dependencies on software development productivity. Our empirical evaluation of the congruence framework showed that when developers' coordination patterns are congruent with their coordination needs, the resolution time of modification requests was significantly reduced. Furthermore, our analysis highlights the importance of identifying the "right" set of technical dependencies that drive the coordination requirements among software developers. Call and data dependencies appear to have far less impact than logical dependencies.

OBJECTIVE: The goal of this research is to learn how the editorial staffs of bioinformatics and medical informatics journals provide support for cross-community exposure. Models such as co-citation and co-author analysis measure the relationships between researchers; but they do not capture how environments that support knowledge transfer across communities are organized. METHODS: In this paper, we propose a social network analysis model to study how editorial boards integrate researchers from disparate communities. We evaluate our model by building relational networks based on the editorial boards of approximately 40 journals that serve as research outlets in medical informatics and bioinformatics. We track the evolution of editorial relationships through a longitudinal investigation over the years 2000 through 2005. RESULTS: Our findings suggest that there are research journals that support the collocation of editorial board members from the bioinformatics and medical informatics communities. Network centrality metrics indicate that editorial board members are located in the intersection of the communities and that the number of individuals in the intersection is growing with time. CONCLUSIONS: Social network analysis methods provide insight into the relationships between the medical informatics and bioinformatics communities. The number of editorial board members facilitating the publication intersection of the communities has grown, but the intersection remains dependent on a small group of individuals and fragile.

Task dependencies drive the need to coordinate work activities. We describe a technique for using automatically generated archi-val data to compute coordination requirements, i.e., who must coordinate with whom to get the work done. Analysis of data from a large software development project revealed that coordina-tion requirements were highly volatile, and frequently extended beyond team boundaries. Congruence between coordination re-quirements and coordination activities shortened development time. Developers, particularly the most productive ones, changed their use of electronic communication media over time, achieving higher congruence. We discuss practical implications of our technique for the design of collaborative and awareness tools.

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 epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.

Countermeasures such as software patches or warnings can be effective in helping organizations avert virus infection problems. However, current strategies for disseminating such countermeasures have limited their effectiveness. We propose a new approach, called the Countermeasure Competing (CMC) strategy, and use computer simulation to formally compare its relative effectiveness with three antivirus strategies currently under consideration. CMC is based on the idea that computer viruses and countermeasures spread through two separate but interlinked complex networks-the virus-spreading network and the countermeasure-propagation network, in which a countermeasure acts as a competing species against the computer virus. Our results show that CMC is more effective than other strategies based on the empirical virus data. The proposed CMC reduces the size of virus infection significantly when the countermeasure-propagation network has properties that favor countermeasures over viruses, or when the countermeasure-propagation rate is higher than the virus-spreading rate. In addition, our work reveals that CMC can be flexibly adapted to different uncertainties in the real world, enabling it to be tuned to a greater variety of situations than other strategies.

This paper discusses the role of organizational design in affecting organizational performance. Using a computational framework, CORP, various aspects of organizational design are examined; e.g., training, communication and command structure, and resource access. Organizational performance is evaluated under both optimal operating conditions and stressed conditions (such as information errors and turnover), given both a simple and a complex task. These analyses suggest that when the organization is facing a choice task where all options are equally likely then the best performing organizational structures are simple (i.e., operational training, teams, and lack of overlap in access to information). In contrast, when the task is biased, i.e., one outcome is more likely than another, then the best performing organizational structures are more complex (i.e., training that relies on experience, hierarchies, and overlap in information access).>