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In traditional public good experiments participants receive an endowment from the experimenter that can be invested in a public good or kept in a private account. In this paper we present an experimental environment where participants can invest time during five days to contribute to a public good. Participants can make contributions to a linear public good by logging into a web application and performing virtual actions. We compared four treatments, with different group sizes and information of (relative) performance of other groups. We find that information feedback about performance of other groups has a small positive effect if we control for various attributes of the groups. Moreover, we find a significant effect of the contributions of others in the group in the previous day on the number of points earned in the current day. Our results confirm that people participate more when participants in their group participate more, and are influenced by information about the relative performance of other groups. [ABSTRACT FROM AUTHOR]

Recently, increasing numbers of users have adopted microblogging services as their main information source. However, most of them find themselves drowning in the millions of posts produced by other users every day. To cope with this, identifying a set of the most influential people is paramount. Moreover, finding a set of related influential users to expand the coverage of one particular topic is required in real world scenarios. Most of the existing algorithms in this area focus on topology-related methods such as PageRank. These methods mine link structures to find the expected influential rank of users. However, because they ignore the interaction data, these methods turn out to be less effective in social networks. In reality, a variety of topics exist within the information diffusing through the network. Because they have different interests, users play different roles in the diffusion of information related to different topics. As a result, distinguishing influential leaders according to different topics is also worthy of research. In this paper, we propose a multi-topic influence diffusion model (MTID) based on traces acquired from historic information. We decompose the influential scores of users into two parts: the direct influence determined by information propagation along the link structure and indirect influence that extends beyond the restrictions of direct follower relationships. To model the network from a multi-topical viewpoint, we introduce topic pools, each of which represents a particular topic information source. Then, we extract the topic distributions from the traces of tweets, determining the influence propagation probability and content generation probability. In the network, we adopt multiple ground nodes representing topic pools to connect every user through bidirectional links. Based on this multi-topical view of the network, we further introduce the topic-dependent rank (TD-Rank) algorithm to identify the multi-topic influential users. Our algorithm not only effectively overcomes the shortages of PageRank but also effectively produces a measure of topic-related rank. Extensive experiments on a Weibo dataset show that our model is both effective and robust. [ABSTRACT FROM AUTHOR]

Privacy-preserving profile matching, a challenging task in mobile social networks, is getting more attention in recent years. In this paper, we propose a novel scheme that is based on ciphertext-policy attribute-based encryption to tackle this problem. In our scheme, a user can submit a preference-profile and search for users with matching-profile in decentralized mobile social networks. In this process, no participant’s profile and the submitted preference-profile is exposed. Meanwhile, a secure communication channel can be established between the pair of successfully matched users. In contrast to existing related schemes which are mainly based on the secure multi-party computation, our scheme can provide verifiability (both the initiator and any unmatched user cannot cheat each other to pretend to be matched), and requires few interactions among users. We provide thorough security analysis and performance evaluation on our scheme, and show its advantages in terms of security, efficiency and usability over state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Privacy-aware intersection set computation (PISC) can be modeled as secure multi-party computation. The basic idea is to compute the intersection of input sets without leaking privacy. Furthermore, PISC should be sufficiently flexible to recommend approximate intersection items. In this paper, we reveal two previously unpublished attacks against PISC, which can be used to reveal and link one input set to another input set, resulting in privacy leakage. We coin these as Set Linkage Attack and Set Reveal Attack. We then present a lightweight and flexible PISC scheme (LiPISC) and prove its security (including against Set Linkage Attack and Set Reveal Attack). [ABSTRACT FROM AUTHOR]

A network is considered observable if its current state can be determined in finite time from knowledge of the observed states. The observability transitions in networks based on random or degree-correlated sensor placement have recently been studied. However, these placement strategies are predominantly based on local information regarding the network. In this paper, to understand the phase transition process of network observability, we analyze the network observability transition for a betweenness-based sensor placement strategy, in which sensors are placed on nodes according to their betweenness. Using numerical simulations, we compute the size of the network’s largest observable component (LOC) and compare the observability transitions for different sensor placements. We find that betweenness-based sensor placement can generate a larger LOC in the observability transition than the random or degree-based placement strategy in both model and real networks. This finding may help to understand the relationship between network observability and the topological properties of the network. [ABSTRACT FROM AUTHOR]

In recent years social and news media have increasingly been used to explain patterns in disease activity and progression. Social media data, principally from the Twitter network, has been shown to correlate well with official disease case counts. This fact has been exploited to provide advance warning of outbreak detection, forecasting of disease levels and the ability to predict the likelihood of individuals developing symptoms. In this paper we introduce DEFENDER, a software system that integrates data from social and news media and incorporates algorithms for outbreak detection, situational awareness and forecasting. As part of this system we have developed a technique for creating a location network for any country or region based purely on Twitter data. We also present a disease nowcasting (forecasting the current but still unknown level) approach which leverages counts from multiple symptoms, which was found to improve the nowcasting accuracy by 37 percent over a model that used only previous case data. Finally we attempt to forecast future levels of symptom activity based on observed user movement on Twitter, finding a moderate gain of 5 percent over a time series forecasting model. [ABSTRACT FROM AUTHOR]

When disaster events capture global attention users of Twitter form transient interest communities that disseminate information and other messages online. This paper examines content related to Typhoon Haiyan (locally known as Yolanda) as it hit the Philippines and triggered international humanitarian response and media attention. It reveals how Twitter conversations about disasters evolve over time, showing an issue attention cycle on a social media platform. The paper examines different functions of Twitter and the information hubs that drive and sustain conversation about the event. Content analysis shows that the majority of tweets contain information about the typhoon or its damage, and disaster relief activities. There are differences in types of content between the most retweeted messages and posts that are original tweets. Original tweets are more likely to come from ordinary users, who are more likely to tweet emotions, messages of support, and political content compared with official sources and key information hubs that include news organizations, aid organization, and celebrities. Original tweets reveal use of the site beyond information to relief coordination and response. [ABSTRACT FROM AUTHOR]

Persuasion is at the core of norm creation, emergence of collective action, and solutions to ‘tragedy of the commons’ problems. In this paper, we show that the directionality of friendship ties affect the extent to which individuals can influence the behavior of each other. Moreover, we find that people are typically poor at perceiving the directionality of their friendship ties and that this can significantly limit their ability to engage in cooperative arrangements. This could lead to failures in establishing compatible norms, acting together, finding compromise solutions, and persuading others to act. We then suggest strategies to overcome this limitation by using two topological characteristics of the perceived friendship network. The findings of this paper have significant consequences for designing interventions that seek to harness social influence for collective action. [ABSTRACT FROM AUTHOR]

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace. [ABSTRACT FROM AUTHOR]

Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks. [ABSTRACT FROM AUTHOR]

With the rapid development of the internet, social media has become an essential tool for getting information, and attracted a large number of people join the social media platforms because of its low cost, accessibility and amazing content. It greatly enriches our life. However, its rapid development and widespread also have provided an excellent convenience for the range of fake news, people are constantly exposed to fake news and suffer from it all the time. Fake news usually uses hyperbole to catch people’s eyes with dishonest intention. More importantly, it often misleads the reader and causes people to have wrong perceptions of society. It has the potential for negative impacts on society and individuals. Therefore, it is significative research on detecting fake news. In the paper, we built a model named SMHA-CNN (Self Multi-Head Attention-based Convolutional Neural Networks) that can judge the authenticity of news with high accuracy based only on content by using convolutional neural networks and self multi-head attention mechanism. In order to prove its validity, we conducted experiments on a public dataset and achieved a precision rate of 95.5% with a recall rate of 95.6% under the 5-fold cross-validation. Our experimental result indicates that the model is more effective at detecting fake news. [ABSTRACT FROM AUTHOR]

This paper describes the process of adapting the Stanford Coreference resolution module to the Basque language, taking into account the characteristics of the language. The module has been integrated in a linguistic analysis pipeline obtaining an end-to-end coreference resolution system for the Basque language. The adaptation process explained can benefit and facilitate other languages with similar characteristics in the implementation of their coreference resolution systems. During the experimentation phase, we have demonstrated that language-specific features have a noteworthy effect on coreference resolution, obtaining a gain in CoNLL score of 7.07 with respect to the baseline system. We have also analysed the effect that preprocessing has in coreference resolution, comparing the results obtained with automatic mentions versus gold mentions. When gold mentions are provided, the results increase 11.5 points in CoNLL score in comparison with results obtained when automatic mentions are used. The contribution of each sieve is analysed concluding that morphology is essential for agglutinative languages to obtain good performance in coreference resolution. Finally, an error analysis of the coreference resolution system is presented which have revealed our system’s weak points and help to determine the improvements of the system. As a result of the error analysis, we have enriched the Basque coreference resolution adding new two sieves, obtaining an improvement of 0.24 points in CoNLL F1 when automatic mentions are used and of 0.39 points when the gold mentions are provided. [ABSTRACT FROM AUTHOR]

We investigate the effectiveness of network attack strategies when the attacker has only imperfect information about the network. While most existing network attack strategies assume complete knowledge about the network, in reality it is difficult to obtain the complete structure of a large-scale complex network. This paper considers two scenarios in which the available network information is imperfect. In one scenario, the network contains link errors (i.e., missing and false links) due to measurement errors, and in the other scenario the target network is so large that only part of the network structure is available from network sampling. Through extensive simulations, we show that particularly in a network with highly skewed degree distribution, network attack strategies are robust against link errors. Even if the network contains 30% false links and missing links, the strategies are just as effective as when the complete network is available. We also show that the attack strategies are far less effective when the network is obtained from random sampling, whereas the detrimental effects of network sampling on network attack strategies are small when using biased sampling strategies such as breadth-first search, depth-first search, and sample edge counts. Moreover, the effectiveness of network attack strategies is examined in the context of network immunization, and the implications of the results are discussed. [ABSTRACT FROM AUTHOR]

Background: Alcohol use and abuse constitute a major public health problem and identifying their determinants is a priority. Social network analysis can indicate how characteristics of social networks are related to individual health behaviors. A growing number of studies have used social network analysis to examine how social network characteristics influence adult alcohol consumption, but this literature has never been systematically reviewed and summarized. The current paper systematically reviews empirical studies that used social network analysis to assess the influence of social network characteristics on drinking behaviors in adults. Methods: A literature search of PubMed/MEDLINE, EMBASE, PsycINFO and Web of Science databases and a review of the reference lists of retrieved articles was conducted in March 2019. Two reviewers independently screened 5,510 non-duplicate records, and further screened the full text of 150 articles to determine their eligibility for inclusion. Seventeen articles were judged eligible and included. Results: Most studies were conducted among young adults (mean age<30), in university settings or follow up visits with adolescent networks moving into adulthood. The objectives and methods of the included studies were heterogeneous. All included studies reported a statistically significant association between a social network characteristic and an alcohol consumption-related outcome. Social network members drinking behaviors were associated with participants’ drinking behaviors in multiple ways. Discussion: In young adults, among whom the majority of identified studies were conducted, with whom they socialize and how they socialize appears to be associated with alcohol consumption; this was observed across methodologies and settings. We still know very little about the relationship of social networks to drinking in older age groups, and in populations most impacted by alcohol. As social networks appear to play a role in the consumption of alcohol in young adulthood, interventions that utilize social networks to help reduce harmful alcohol consumption should be considered. [ABSTRACT FROM AUTHOR]

Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate learning. This paper explores the performance of word2vec Convolutional Neural Networks (CNNs) to classify news articles and tweets into related and unrelated ones. Using two word embedding algorithms of word2vec, Continuous Bag-of-Word (CBOW) and Skip-gram, we constructed CNN with the CBOW model and CNN with the Skip-gram model. We measured the classification accuracy of CNN with CBOW, CNN with Skip-gram, and CNN without word2vec models for real news articles and tweets. The experimental results indicated that word2vec significantly improved the accuracy of the classification model. The accuracy of the CBOW model was higher and more stable when compared to that of the Skip-gram model. The CBOW model exhibited better performance on news articles, and the Skip-gram model exhibited better performance on tweets. Specifically, CNN with word2vec models was more effective on news articles when compared to that on tweets because news articles are typically more uniform when compared to tweets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for learning node embeddings for weighted undirected networks. We perform a random walk on the network to extract the latent structural information and perform node embedding learning under a similarity-based framework. Unlike previous works, we apply a different criterion to capture the proximity information between nodes in a network, and use it for improved modeling of similarities between nodes. We show that the mean commute time (MCT) between two nodes, defined as the average time a random walker takes to reach a target node and return to the source, plays a crucial role in quantifying the actual degree of proximity between two nodes of the network. We then introduce a novel definition of a similarity matrix that is based on the pair-wise mean commute time captured, which enables us to adequately represent the connection of similar nodes. We utilize pseudoinverse of the Laplacian matrix of the graph for calculating such a proximity measure, capturing rich structural information out of the graph for learning more adequate node representations of a network. The results of different experiments on three real-world networks demonstrate that our proposed method outperforms existing related efforts in classification, clustering, visualization as well as link prediction tasks. [ABSTRACT FROM AUTHOR]

Shape Up Under 5 (SUU5) was a two-year early childhood obesity prevention pilot study in Somerville, Massachusetts (2015–2017) designed to test a novel conceptual framework called Stakeholder-driven Community Diffusion. For whole-of-community interventions, this framework posits that diffusion of stakeholders’ knowledge about and engagement with childhood obesity prevention efforts through their social networks will improve the implementation of health-promoting policy and practice changes intended to reduce obesity risk. SUU5 used systems science methods (agent-based modeling, group model building, social network analysis) to design, facilitate, and evaluate the work of 16 multisector stakeholders (‘the Committee’). In this paper, we describe the design and methods of SUU5 using the conceptual framework: the approach to data collection, and methods and rationale for study inputs, activities and evaluation, which together may further our understanding of the hypothesized processes within Stakeholder-driven Community Diffusion. We also present a generalizable conceptual framework for addressing childhood obesity and similar complex public health issues through whole-of-community interventions. [ABSTRACT FROM AUTHOR]

When many events contributed to an outcome, people consistently judge some more causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air—in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people’s causal judgments are on a continuum—and relatively little is known about how these judgments vary quantitatively as the prior probabilities change. In this paper, we measure people’s causal judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories. [ABSTRACT FROM AUTHOR]

We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome—asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniques, and in particular we focus on semi-supervised classification techniques since they enable us to use more of the Twitter data collected while only doing very minimal labelling. In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches. Additionally, we highlight the use of emojis and other special features capturing the tweet’s tone to improve the classification performance. Our results show that negative emojis and those that denote laughter provide the best classification performance in conjunction with a simple word-level n-gram approach. We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets and may be advantageous in the context of a weak signal. Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions. [ABSTRACT FROM AUTHOR]

People are connected by various social networks, resulting in the interdependence of consumer choice. Therefore, it is very important and realistic to assume choice interdependence in private car ownership modeling. In this paper, we investigate the interdependence of private car ownership choice using a spatial autoregressive binary probit model estimated by the Bayesian Markov chain Monte Carlo (MCMC) method. Constructing the autoregressive matrix demographically shows that the private car ownership choice of a household is dependent on other household choices. Compared with the pure binary probit model estimated by the MCMC method, the spatial autoregressive model achieves a significant improvement both in loglikelihood value and log marginal density value, which are calculated using the importance sampling method of Newton and Raftery, from approximately -202 to approximately -63 and from -208 to -145, respectively. Moreover, the results indicated by the spatial autoregressive probit model suggest that the number of children, the ownership of an apartment or the availability of a parking lot are positively and significantly associated with the private car ownership level. To test the out-of-sample performance of the model, we estimate the model using 600 data points and test it using another 148 data points. The results indicate that the predictive power is greatly improved. Finally, we analyze the augmented parameter and discover that it is associated with the parking variable in addition to the license variable. [ABSTRACT FROM AUTHOR]

How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for this purpose. Designing an accurate and efficient CMTF method has become more crucial as the size and dimension of real-world data are growing explosively. However, existing methods for CMTF suffer from lack of accuracy, slow running time, and limited scalability. In this paper, we propose S3CMTF, a fast, accurate, and scalable CMTF method. In contrast to previous methods which do not handle large sparse tensors and are not parallelizable, S3CMTF provides parallel sparse CMTF by carefully deriving gradient update rules. S3CMTF asynchronously updates partial gradients without expensive locking. We show that our method is guaranteed to converge to a quality solution theoretically and empirically. S3CMTF further boosts the performance by carefully storing intermediate computation and reusing them. We theoretically and empirically show that S3CMTF is the fastest, outperforming existing methods. Experimental results show that S3CMTF is up to 930× faster than existing methods while providing the best accuracy. S3CMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply S3CMTF to Yelp rating tensor data coupled with 3 additional matrices to discover interesting patterns. [ABSTRACT FROM AUTHOR]

University dropout is a growing problem with considerable academic, social and economic consequences. Conclusions and limitations of previous studies highlight the difficulty of analyzing the phenomenon from a broad perspective and with bigger data sets. This paper proposes a new, machine-learning based method, able to examine the problem using a holistic approach. Advantages of this method include the lack of strong distribution hypothesis, the capacity for handling bigger data sets and the interpretability of the results. Results are consistent with previous research, showing the influence of personal and contextual variables and the importance of academic performance in the first year, but other factors are also highlighted with this model, such as the importance of dedication (part or full time), and the vulnerability of the students with respect to their age. Additionally, a comprehensive graphic output is included to make it easier to interpret the discovered rules. [ABSTRACT FROM AUTHOR]

‘Social media metrics’ are bursting into science studies as emerging new measures of impact related to scholarly activities. However, their meaning and scope as scholarly metrics is still far from being grasped. This research seeks to shift focus from the consideration of social media metrics around science as mere indicators confined to the analysis of the use and visibility of publications on social media to their consideration as metrics of interaction and circulation of scientific knowledge across different communities of attention, and particularly as metrics that can also be used to characterize these communities. Although recent research efforts have proposed tentative typologies of social media users, no study has empirically examined the full range of Twitter user’s behavior within Twitter and disclosed the latent dimensions in which activity on Twitter around science can be classified. To do so, we draw on the overall activity of social media users on Twitter interacting with research objects collected from the Altmetic.com database. Data from over 1.3 million unique users, accounting for over 14 million tweets to scientific publications, is analyzed. Based on an exploratory and confirmatory factor analysis, four latent dimensions are identified: ‘Science Engagement’, ‘Social Media Capital’, ‘Social Media Activity’ and ‘Science Focus’. Evidence on the predominant type of users by each of the four dimensions is provided by means of VOSviewer term maps of Twitter profile descriptions. This research breaks new ground for the systematic analysis and characterization of social media users’ activity around science. [ABSTRACT FROM AUTHOR]

To what extent can online social networks predict who is at risk of an infection? Many infections are transmitted through physical encounter between humans, but collecting detailed information about it can be expensive, might invade privacy, or might not even be possible. In this paper, we ask whether online social networks help predict and contain epidemic risk. Using a dataset from a popular online review service which includes over 100 thousand users and spans 4 years of activity, we build a time-varying network that is a proxy of physical encounter between its users (the encounter network) and a static network based on their reported online friendship (the friendship With computer simulations, we compare stochastic infection processes on the two networks, considering infections on the encounter network as the benchmark. First, we show that the friendship network is useful to identify the individuals at risk of infection, despite providing lower accuracy than the ideal case in which the encounters are known. This limited prediction accuracy is not only due to the static nature of the friendship network because a static version of the encounter network provides more accurate prediction of risk than the friendship network. Then, we show that periodical monitoring of the infection spreading on the encounter network allows to correct the infection predicted by a process spreading on the friendly staff ndship network, and achieves high prediction accuracy. Finally, we show that the friendship network contains valuable information to effectively contain epidemic outbreaks even when a limited budget is available for immunization. In particular, a strategy that immunizes random friends of random individuals achieves the same performance as knowing individuals’ encounters at a small additional cost, even if the infection spreads on the encounter network. [ABSTRACT FROM AUTHOR]

Given the centrality of regions in social movements, politics and public administration, here we aim to quantitatively study regional identity, cross-region communication and sentiment. This paper presents a new methodology to study social interaction within and between social-geographic regions, and then applies the methodology to a case study of England and Wales. We use a social network, built from geo-located Twitter data, to identify contiguous geographical regions with a shared social identity and then investigate patterns of communication within and between them. In contrast to other approaches (e.g. using phone call data records or online friendship networks), use of Twitter data provides message contents as well as social connections. This allows us to investigate not only the volume of communication between locations, but also the sentiment and vocabulary used in the messages. For example, our case study shows: a significant dialect difference between England and Wales; that regions tend to be more positive about themselves than about others, with the South being more ‘self-regarding’ than the North; and that people talk politics much more between regions than within. This study demonstrates how social media can be used to quantify regional identity and inter-region communications and sentiment, exposing these previously hard-to-observe geographic concepts to analysis. [ABSTRACT FROM AUTHOR]

The use of virtual laboratories is growing as companies and educational institutions try to expand their reach, cut costs, increase student understanding, and provide more accessible hands on training for future scientists. Many new higher education initiatives outsource lab activities so students now perform them online in a virtual environment rather than in a classroom setting, thereby saving time and money while increasing accessibility. In this paper we explored whether the learning and motivational outcomes of interacting with a desktop virtual reality (VR) science lab simulation on the internet at home are equivalent to interacting with the same simulation in class with teacher supervision. A sample of 112 (76 female) university biology students participated in a between-subjects experimental design, in which participants learned at home or in class from the same virtual laboratory simulation on the topic of microbiology. The home and classroom groups did not differ significantly on post-test learning outcome scores, or on self-report measures of intrinsic motivation or self-efficacy. Furthermore, these conclusions remained after accounting for prior knowledge or goal orientation. In conclusion, the results indicate that virtual simulations are learning activities that students can engage in just as effectively outside of the classroom environment. [ABSTRACT FROM AUTHOR]

Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP—Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving—Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest. [ABSTRACT FROM AUTHOR]

This paper presents the first empirical investigation that compares Euler and linear diagrams when they are used to represent set cardinality. A common approach is to use area-proportional Euler diagrams but linear diagrams can exploit length-proportional straight-lines for the same purpose. Another common approach is to use numerical annotations. We first conducted two empirical studies, one on Euler diagrams and the other on linear diagrams. These suggest that area-proportional Euler diagrams with numerical annotations and length-proportional linear diagrams without numerical annotations support significantly better task performance. We then conducted a third study to investigate which of these two notations should be used in practice. This suggests that area-proportional Euler diagrams with numerical annotations most effectively supports task performance and so should be used to visualize set cardinalities. However, these studies focused on data that can be visualized reasonably accurately using circles and the results should be taken as valid within that context. Future work needs to determine whether the results generalize both to when circles cannot be used and for other ways of encoding cardinality information. [ABSTRACT FROM AUTHOR]

The Moral Foundations Dictionary (MFD) is a useful tool for applying the conceptual framework developed in Moral Foundations Theory and quantifying the moral meanings implicated in the linguistic information people convey. However, the applicability of the MFD is limited because it is available only in English. Translated versions of the MFD are therefore needed to study morality across various cultures, including non-Western cultures. The contribution of this paper is two-fold. We developed the first Japanese version of the MFD (referred to as the J-MFD) using a semi-automated method—this serves as a reference when translating the MFD into other languages. We next tested the validity of the J-MFD by analyzing open-ended written texts about the situations that Japanese participants thought followed and violated the five moral foundations. We found that the J-MFD correctly categorized the Japanese participants’ descriptions into the corresponding moral foundations, and that the Moral Foundations Questionnaire (MFQ) scores correlated with the frequency of situations, of total words, and of J-MFD words in the participants’ descriptions for the Harm and Fairness foundations. The J-MFD can be used to study morality unique to the Japanese and also multicultural comparisons in moral behavior. [ABSTRACT FROM AUTHOR]

The Google Books Ngram Viewer (Google Ngram) is a search engine that charts word frequencies from a large corpus of books and thereby allows for the examination of cultural change as it is reflected in books. While the tool's massive corpus of data (about 8 million books or 6% of all books ever published) has been used in various scientific studies, concerns about the accuracy of results have simultaneously emerged. This paper reviews the literature and serves as a guideline for improving Google Ngram studies by suggesting five methodological procedures suited to increase the reliability of results. In particular, we recommend the use of (I) different language corpora, (II) cross-checks on different corpora from the same language, (III) word inflections, (IV) synonyms, and (V) a standardization procedure that accounts for both the influx of data and unequal weights of word frequencies. Further, we outline how to combine these procedures and address the risk of potential biases arising from censorship and propaganda. As an example of the proposed procedures, we examine the cross-cultural expression of religion via religious terms for the years 1900 to 2000. Special emphasis is placed on the situation during World War II. In line with the strand of literature that emphasizes the decline of collectivistic values, our results suggest an overall decrease of religion's importance. However, religion re-gains importance during times of crisis such as World War II. By comparing the results obtained through the different methods, we illustrate that applying and particularly combining our suggested procedures increase the reliability of results and prevents authors from deriving wrong assumptions. [ABSTRACT FROM AUTHOR]

Given a real-world graph, how can we measure relevance scores for ranking and link prediction? Random walk with restart (RWR) provides an excellent measure for this and has been applied to various applications such as friend recommendation, community detection, anomaly detection, etc. However, RWR suffers from two problems: 1) using the same restart probability for all the nodes limits the expressiveness of random walk, and 2) the restart probability needs to be manually chosen for each application without theoretical justification. We have two main contributions in this paper. First, we propose R W E R (RWER), a random walk based measure which improves the expressiveness of random walks by using a distinct restart probability for each node. The improved expressiveness leads to superior accuracy for ranking and link prediction. Second, we propose SR (pervised start for RWER), an algorithm for learning the restart probabilities of RWER from a given graph. SR eliminates the need to heuristically and manually select the restart parameter for RWER. Extensive experiments show that our proposed method provides the best performance for ranking and link prediction tasks. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a scalable machine learning approach accompanied by open-source software for identifying violent and peaceful forms of political protest participation using social media data. While violent political protests are statistically rare events, they often shape public perceptions of political and social movements. This is, in part, due to the extensive and disproportionate media coverage which violent protest participation receives relative to peaceful protest participation. In the past, when a small number of media conglomerates served as the primary information source for learning about political and social movements, viewership and advertiser demands encouraged news organizations to focus on violent forms of political protest participation. Consequently, much of our knowledge about political protest participation is derived from data collected about violent protests, while less is known about peaceful forms of protest. Since the early 2000s, the digital revolution shifted attention away from traditional news sources toward social media as a primary source of information about current events. This, along with developments in machine learning which allow us to collect and analyze data relevant to political participation, present us with unique opportunities to expand our knowledge of peaceful and violent forms of political protest participation through social media data. [ABSTRACT FROM AUTHOR]

This paper examines the link between reliance on Facebook for news, political knowledge, and political engagement in the Philippines. We tested five hypotheses using data gathered from an online survey of 978 Filipinos conducted from February 1 to March 31, 2016. Findings support the hypothesis that those who rely less on social media as a news source exhibit higher levels of perceived knowledge about politics than those who rely more on it for news. Controlling for traditional news use, following political officials or institutions on social media is associated with higher levels of political interest and engagement, those with more politically active friends on Facebook have higher levels of exposure to political content online, and there is a positive correlation between Facebook being a source of information about politics and discussing politics more often with others. However, the hypothesis that those with more friends on their network who are politically active, will have greater political knowledge and more political engagement than those who have few politically active friends on their Facebook network is not supported. [ABSTRACT FROM AUTHOR]

Temperament and Psychological Types can be defined as innate psychological characteristics associated with how we relate with the world, and often influence our study and career choices. Furthermore, understanding these features help us manage conflicts, develop leadership, improve teaching and many other skills. Assigning temperament and psychological types is usually made by filling specific questionnaires. However, it is possible to identify temperamental characteristics from a linguistic and behavioral analysis of social media data from a user. Thus, machine-learning algorithms can be used to learn from a user’s social media data and infer his/her behavioral type. This paper initially provides a brief historical review of theories on temperament and then brings a survey of research aimed at predicting temperament and psychological types from social media data. It follows with the proposal of a framework to predict temperament and psychological types from a linguistic and behavioral analysis of Twitter data. The proposed framework infers temperament types following the David Keirsey’s model, and psychological types based on the MBTI model. Various data modelling and classifiers are used. The results showed that Random Forests with the LIWC technique can predict with 96.46% of accuracy the Artisan temperament, 92.19% the Guardian temperament, 78.68% the Idealist, and 83.82% the Rational temperament. The MBTI results also showed that Random Forests achieved a better performance with an accuracy of 82.05% for the E/I pair, 88.38% for the S/N pair, 80.57% for the T/F pair, and 78.26% for the J/P pair. [ABSTRACT FROM AUTHOR]

This paper demonstrates how Geographic Information Systems (GIS) can be utilized to study the effects of spatial phenomena. Since experimental designs such as Randomized Controlled Trials are generally not feasible for spatial problems, researchers need to rely on quasi-experimental approaches using observational data. We provide a regression-based framework of the key procedures for GIS-based empirical research design using georeferenced point data for both spatial events of interest and subjects exposed to the events. We illustrate its utility and implementation through a case study on the impacts of the Cambodian genocide under the Pol Pot regime on post-conflict education. [ABSTRACT FROM AUTHOR]

Earthworms are primary candidates for national soil health monitoring as they are ecosystem engineers that benefit both food production and ecosystem services associated with soil security. Supporting farmers to monitor soil health could help to achieve the policy aspiration of sustainable soils by 2030 in England; however, little is known about how to overcome participation barriers, appropriate methodologies (practical, cost-effective, usefulness) or training needs. This paper presents the results from a pilot #60minworms study which mobilised farmers to assess over >1300 ha farmland soils in spring 2018. The results interpretation framework is based on the presence of earthworms from each of the three ecological groups at each observation (20 x 20 cm x 20 cm pit) and spatially across a field (10 soil pits). Results showed that most fields have basic earthworm presence and abundance, but 42% fields may be over-worked as indicated by absence/rarity of epigeic and/or anecic earthworms. Tillage had a negative impact (p < 0.05) on earthworm populations and organic matter management did not mitigate tillage impacts. In terms of farmer participation, Twitter and Farmers Weekly magazine were highly effective channels for recruitment. Direct feedback from participants included excellent scores in trust, value and satisfaction of the protocol (e.g. 100% would do the test again) and 57% would use their worm survey results to change their soil management practices. A key training need in terms of earthworm identification skills was reported. The trade-off between data quality, participation rates and fieldwork costs suggests there is potential to streamline the protocol further to #30minworms (5 pits), incurring farmer fieldwork costs of approximately £1.48 ha-1. At national scales, £14 million pounds across 4.7 M ha-1 in fieldwork costs per survey could be saved by farmer participation. [ABSTRACT FROM AUTHOR]

Group buying (GB) is a popular business model in e-commerce. With the rise of online social media, the positive network effect of buying with others is more important than price discount for consumers to choose GB. However, the negative network effect of GB is also significant for some consumers. In this paper, we classify consumers into two segments considering both positive and negative network effects, and three possible sales strategies as well as their optimal decisions on price are presented. We find that GB strategy dominates individual buying (IB) strategy when the positive network effect is sufficiently high or the proportion of consumers with low valuation is relatively large. We also find that MIX strategy offering both IB and GB is always better than IB, while the relationship between MIX and GB is depending on actual market situations. Some other managerial insights are also discussed. [ABSTRACT FROM AUTHOR]

There is increasing concern about online communities that promote eating disorder (ED) behaviors through messages and/or images that encourage a “thin ideal” (i.e., promotion of thinness as attractive) and harmful weight loss/weight control practices. The purpose of this paper is to assess the content of body image and ED-related content on Twitter and provide a deeper understanding of EDs that may be used for future studies and online-based interventions. Tweets containing ED or body image-related keywords were collected from January 1-January 31, 2015 (N = 28,642). A random sample (n = 3000) was assessed for expressions of behaviors that align with subscales of the Eating Disorder Examination (EDE) 16.0. Demographic characteristics were inferred using a social media analytics company. The comprehensive research that we conducted indicated that 2,584 of the 3,000 tweets were ED-related; 65% expressed a preoccupation with body shape, 13% displayed issues related to food/eating/calories, and 4% expressed placing a high level of importance on body weight. Most tweets were sent by girls (90%) who were ≤19 years old (77%). Our findings stress a need to better understand if and how ED-related content on social media can be used for targeting prevention and intervention messages towards those who are in-need and could potentially benefit from these efforts. [ABSTRACT FROM AUTHOR]

Over the past several decades there have been dramatic shifts in demographic patterns pertaining to family formation, with declining and delayed marriage and childbearing, and increased cohabitation in the United States and other Western industrialized nations. These trends in family demography have been predominantly studied using large-scale datasets, which have identified total population and subgroup trends over time, including differences by age, gender, racial/ethnic, economic, educational, religious, and other characteristics. However, there is limited knowledge and understanding of how individuals across different levels of social position, as well as other important characteristics, make decisions around forming families. This lack of qualitative data on contemporary attitudes regarding family formation has hampered our ability to more completely understand the factors driving behaviors pertaining to the large-scale (ie, international) shifts in demographic trends. The Social Position and Family Formation (SPAFF) project is an in-depth interview study that used quantitative data to guide recruitment of a large sample for qualitative interview data collection on factors influencing different aspects of family formation among heterosexual females and males (18–35 years) in the context of individuals’ social position. This methodological paper describes the use of a ‘quantitatively-informed’ purposive sampling approach in a large metropolitan area to collect qualitative data (through in-depth interviews) from a large sample (n = 200), utilizing web-based tools for successful community-based recruitment and project management. [ABSTRACT FROM AUTHOR]

We propose an agent-based model leading to a decrease or an increase of hostility between agents after a major cultural threat such as a terrorist attack. The model is inspired from the Terror Management Theory and the Social Judgement Theory. An agent has a cultural identity defined through its acceptance segments about each of three different cultural worldviews (i.e., Atheist, Muslim, Christian) of the considered society. An agent’s acceptance segment is composed from its acceptable positions toward a cultural worldview, including its most acceptable position. An agent forms an attitude about another agent depending on the similarity between their cultural identities. When a terrorist attack is perpetrated in the name of an extreme cultural identity, the negatively perceived agents from this extreme cultural identity point of view tend to decrease the width of their acceptance segments in order to differentiate themselves more from the threatening cultural identity. We generated a set of populations with cultural identities compatible with data from a survey on attitudes among a large sample representative of the population of France; we then simulated the reaction of these agents facing a terrorist attack from Muslim extremists. For most populations, the average attitude toward Muslims becomes more negative. However, for some specific populations, we noticed the opposite effect as the average attitude of the population toward Muslims becomes less negative. In these populations, the Muslim agents strongly differentiate themselves from the terrorists’ extreme cultural identity, and the other agents are aware of these changes. These reactions are due to particular properties of their cultural identities that are identified in this paper. [ABSTRACT FROM AUTHOR]

This study presents a quantitative analysis of global breast imaging research over the last eight decades. A dedicated Sino-Australian case study via a social network analysis (SNA) is included as China and Australia have a recent rapidly increasing number of research partnerships and strategic education/economic connections. Bibliographic data was extracted via Scopus and analysed for the social network parameters of degree centrality, closeness centrality, betweenness centrality and multiple cohesion measures in order to explore research collaboration networks at the organisational level. Within the last three decades there has been a tremendous increase in the publication rate within the scientific domain of breast imaging research, however, there is a significant lag in the development of this research area in China compared with Australia. Breast imaging research in China is considerably more insular, with less international collaboration and reduced variation between collaborators than Australia. The impact of national breast screening programs and novel cancer technologies upon collaboration networks is discussed alongside the ability of networks paradigm to reveal both frailties in research connections and to highlight networking strategies. [ABSTRACT FROM AUTHOR]

Our aim was to develop a widely available educational program in which students conducted authentic research that met the expectations of both the scientific and educational communities. This paper describes the development and implementation of a citizen science project based on DNA barcoding of reptile specimens obtained from the Museums Victoria frozen tissue collection. The student program was run by the Gene Technology Access Centre (GTAC) and was delivered as a “one day plus one lesson” format incorporating a one-day wet laboratory workshop followed by a single lesson at school utilising online bioinformatics tools. The project leveraged the complementary resources and expertise of the research and educational partners to generate robust scientific data that could be analysed with confidence, meet the requirements of the Victorian state education curriculum, and provide participating students with an enhanced learning experience. During two 1-week stints in 2013 and 2014, 406 students mentored by 44 postgraduate university students participated in the project. Students worked mainly in pairs to process ~200 tissue samples cut from 53 curated reptile specimens representing 17 species. A total of 27 novel Cytochrome Oxidase subunit 1 (CO1) sequences were ultimately generated for 8 south-east Australian reptile species of the families Scincidae and Agamidae. [ABSTRACT FROM AUTHOR]

Background: The burden of late presentation is well established in women presenting with advanced breast cancer in Africa. This paper aims to explore the reasons for delayed presentation in Ghanaian women with breast cancer. Method: Eleven (11) women diagnosed with advanced breast cancer were purposively sampled within three years of diagnosis at the palliative care clinic of the Komfo Anokye Teaching Hospital, Ghana. Participation was voluntary. Data was collected through in-depth interviews using a self-devised semi-structured interview guide. The interviews were conducted in “Twi” (local language), audio-tape recorded and covered the women’s journey from symptom discovery to their intention to seek help. All audio-taped interviews were transcribed based on the meaning of the respondents’ comments. The data was managed using Nvivo version 11 qualitative software. Data was analyzed concurrently with data collection applying the principles of thematic analysis. Key findings: All the women delayed presentation due to overlapping reasons. Symptom appraisal among the women occurred in two main stages: individual understanding of breast symptom and interactive understanding of the breast symptom. These stages were based on cognitive, psycho-cultural and social factors. The five main themes generated from the data were: symptom experience, knowledge of breast cancer, role of social life and network, coping with a breast symptom and lastly intent to seeking health care. A conceptual model was developed to illustrate the relationships among the key factors and concepts emanated from this study. Conclusion: Recognition and appraisal of breast cancer symptom in the eleven (11) Ghanaian women interviewed in this study was poor. For instance, a painless breast lump was considered not serious until a sensory symptom appears. This led women to experience appraisal and time point intervals. To minimize the incidence of late presentation of breast cancer cases in Ghana, adequate educational intervention should be provided for Ghanaian women and their social network, and other stakeholders. [ABSTRACT FROM AUTHOR]

Viral marketing has been one of the main marketing modes. However, theoretical study of viral marketing is still lacking. This paper focuses on the problem of developing a cost-effective dynamic discount pricing strategy for a viral marketing campaign. First, based on a novel word-of-mouth propagation model, we model the original problem as an optimal control problem. Second, we show that the optimal control problem admits an optimal control and present the optimality system for solving the optimal control problem. Next, we solve some optimal control models to get their respective optimal dynamic discount pricing strategies. Finally, we examine the effect of some factors on the maximum marketing profit. These results contribute to gaining insight into viral marketing. [ABSTRACT FROM AUTHOR]

The current marketing landscape, apart from conventional approaches, consists of campaigns designed especially for launching information diffusion processes within online networks. Associated research is focused on information propagation models, campaign initialization strategies and factors affecting campaign dynamics. In terms of algorithms and performance evaluation, the final coverage represented by the fraction of activated nodes within a target network is usually used. It is not necessarily consistent with the real marketing campaigns using various characteristics and parameters related to coverage, costs, behavioral patterns and time factors for overall evaluation. This paper presents assumptions for a decision support system for multi-criteria campaign planning and evaluation with inputs from agent-based simulations. The results, which are delivered from a simulation model based on synthetic networks in a form of decision scenarios, are verified within a real network. Last, but not least, the study proposes a multi-objective campaign evaluation framework with several campaign evaluation metrics integrated. The results showed that the recommendations generated with the use of synthetic networks applied to real networks delivered results according to the decision makers’ expectation in terms of the used evaluation criteria. Apart from practical applications, the proposed multi-objective approach creates new evaluation possibilities for theoretical studies focused on information spreading processes within complex networks. [ABSTRACT FROM AUTHOR]

Agent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that these factors along with interactions of factors and the actions of individuals would lead to outbreaks of different size and severity even in two towns that appear similar on paper. Thus, it is necessary to implement a model that is able to capture these interactions and the actions of individuals. Using openly available data we create a data-driven agent-based model to simulate the spread of an airborne infectious disease in an Irish town. Agent-based models have been known to produce results that include the emergence of patterns and behaviours that are not directly programmed into the model. Our model is tested by simulating an outbreak of measles that occurred in Schull, Ireland in 2012. We simulate the same outbreak in 33 different towns and look at the correlations between the model results and the town characteristics (population, area, vaccination rates, age structure) to determine if the results of the model are affected by interactions of those town characteristics and the decisions on the agents in the model. As expected our results show that the outbreaks are not strongly correlated with any of the main characteristics of the towns and thus the model is most likely capturing such interactions and the agent-based model is successful in capturing the differences in the outbreaks. [ABSTRACT FROM AUTHOR]

A smart grid, considered the next-generation type of power grid, combines a traditional power grid with information and communication technologies to effectively facilitate power generation and ensure transmission security and reliability in real-time. Only authorized consumers should be able to access the smart grid because the information gathered by smart meters includes users’ private information. However, smart grid security is still a challenge. Motivated by this challenge, in this paper, we propose a heterogeneous signcryption (HSC) scheme for secure communication between smart meters and the utility. We demonstrate that this scheme is indistinguishable against adaptive chosen-ciphertext attacks (IND-CCA2), existentially unforgeable against adaptive chosen-message attacks (EUF-CMA) and ciphertext-anonymous against adaptive chosen ciphertext attacks (ANON-CCA2) under the computational Diffie-Hellman (CDH) problem in the random oracle model. Our scheme simultaneously achieves confidentiality, integrity, authentication, non-repudiation and ciphertext anonymity in a single logical step. It supports heterogeneous systems, allowing a meter in an identity-based cryptography (IBC) environment to transmit electrical usage data to a utility in a public key infrastructure (PKI) environment. Compared with other existing related schemes, our scheme has the lowest communication overhead and energy consumption for the smart grid. Based on these features, our scheme is highly suitable for secure power transmissions in a smart grid. [ABSTRACT FROM AUTHOR]

Propagating phenomena in networks have received significant amount of attention within various domains, ranging from contagion in epidemiology, to diffusion of innovations and social influence on behavior and communication. Often these studies attempt to model propagation processes in networks to create interventions that steer propagation dynamics towards desired or away from undesired outcomes. Traditionally, studies have used relatively simple models of the propagation mechanism. In most propagation models this mechanism is described as a monolithic process and a single parameter for the infection rate. Such a description of the propagation mechanism is a severe simplification of mechanisms described in various theoretical exchange theories and phenomena found in real world settings, and largely fails to capture the nuances present in such descriptions. Recent work has suggested that such a simplification may not be sufficient to explain observed propagation dynamics, as nuances of the mechanism of propagation can have a severe impact on its dynamics. This suggests a better understanding of the role of the propagation mechanism is desired. In this paper we put forward a novel framework and model for propagation, the RTR framework. This framework, based on communication theory, decomposes the propagation mechanism into three sub-processes; Radiation, Transmission and Reception (RTR). We show that the RTR framework provides a more detailed way for specifying and conceptually thinking about the process of propagation, aligns better with existing real world interventions, and allows for gaining new insights into effective intervention strategies. By decomposing the propagation mechanism, we show that the specifications of this mechanism can have significant impact on the effectiveness of network interventions. We show that for the same composite single-parameter specification, different decompositions in Radiation, Transmission and Reception yield very different effectiveness estimates for the same network intervention, from 30% less effective to 70% more effective. We find that the appropriate choice for intervention depends strongly on the decomposition of the propagation mechanism. Our findings highlight that a correct decomposition of the mechanism is a prerequisite for developing effective network intervention strategies, and that the use of monolithic models, which oversimplify the mechanism, can be problematic of supporting decisions related to network interventions. In contrast, by allowing more detailed specification of the propagation mechanism and enabling this mechanism to be linked to existing interventions, the RTR framework provides a valuable tool for those designing interventions and implementing interventions strategies. [ABSTRACT FROM AUTHOR]

The modelling of deceptions in game theory and decision theory has not been well studied, despite the increasing importance of this problem in social media, public discourse, and organisational management. This paper presents an improved formulation of the extant information-theoretic models of deceptions, a framework for incorporating these models of deception into game and decision theoretic models of deception, and applies these models and this framework in an agent based evolutionary simulation that models two very common deception types employed in “fake news” attacks. The simulation results for both deception types modelled show, as observed empirically in many social systems subjected to “fake news” attacks, that even a very small population of deceivers that transiently invades a much larger population of non-deceiving agents can strongly alter the equilibrium behaviour of the population in favour of agents playing an always defect strategy. The results also show that the ability of a population of deceivers to establish itself or remain present in a population is highly sensitive to the cost of the deception, as this cost reduces the fitness of deceiving agents when competing against non-deceiving agents. Diffusion behaviours observed for agents exploiting the deception producing false beliefs are very close to empirically observed behaviours in social media, when fitted to epidemiological models. We thus demonstrate, using the improved formulation of the information-theoretic models of deception, that agent based evolutionary simulations employing the Iterated Prisoner’s Dilemma can accurately capture the behaviours of a population subject to deception attacks introducing uncertainty and false perceptions, and show that information-theoretic models of deception have practical applications beyond trivial taxonomical analysis. [ABSTRACT FROM AUTHOR]

To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research. [ABSTRACT FROM AUTHOR]