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The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing. [ABSTRACT FROM AUTHOR]

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a “dying” paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing “offspring”. Practically, this might be used to trace the successive published milestones of a research field. [ABSTRACT FROM AUTHOR]

An editorial is presented which discusses the collaborative writing with multiple authors which has additional challenges including varied levels of engagement of coauthors, provision of fair credit through authorship or acknowledgements and acceptance of diversity of work styles. Also discussed are the 10 simple rules for collaboratively writing a multi-authored paper which include to build a writing team wisely; to create a data management plan and to jointly decide on authorship guidelines.

The safety of highly aggregated tourist crowds is a challenging and important issue. This paper not only provided a comprehensive analysis of the accidents of highly aggregated tourist crowds but also determined the occurrence mechanism and coping paths. Based on the analysis of multiple cases, we found that the variable status of highly aggregated tourist crowds was the result of the interaction of three main elements: multisource pressure, state mutations and management responses. A series of factors interact and result in accidents, and the lack of a management response or a low-quality management response is the root cause of such accidents. A high-quality management response is a basic safety precaution for highly aggregated tourist crowds. Therefore, forming a virtuous circle of multisource pressure, state mutations and management responses is an effective path for coping with accidents. [ABSTRACT FROM AUTHOR]

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society. [ABSTRACT FROM AUTHOR]

By utilizing cognitive mapping and leveraging georeferenced text data, this paper aims to suggest a new visualization method that combines the advantages of both conventional and state-of-the-art research techniques to depict the collective identity of place in a single image. The study addressed two research questions: (1) Can crowd-sourced text data be utilized in representing place identity? (2) Can collective place identity be expressed in the form of a cognitive map? By confirming that text data gathered from social media effectively demonstrate people’s behaviors and perceptions related to places, we propose a novel method to create a visual representation of urban identity–a “crowd-sourced cognitive map”. In particular, to improve the conventional cognitive mapping method to depict the collective identity of a city, we draw cognitive maps of Bundang and Ilsan developed in the 1990s, as well as Songdo and Dongtan developed in the 2000s, just outside of the administrative boundaries of Seoul in Korea, through a computational method based on crowd-sourced opinions collected from social media. We open the possibility for the use of social media text data to capture the identity of cities and suggest a graphical image through which people without prior information could also easily apprehend the overall image of a city. The work in this paper is expected to provide a methodological technique for appropriate decision-making and the evaluation of urban identity to shape a more unique and imageable city. [ABSTRACT FROM AUTHOR]

Social networks influence health-related behavior, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behavior, it is less understood how the structure or topology of the network, in itself, impacts an individual's health behavior and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual's health and wellness. We develop a method called the Network-Driven health predictor (NetCARE) that leverages features representative of social network structure. Using a large longitudinal data set of students enrolled in the NetHealth study at the University of Notre Dame, we show that the NetCARE method improves the overall prediction performance over the baseline models—that use demographics and physical attributes—by 38%, 65%, 55%, and 54% for the wellness states—stress, happiness, positive attitude, and self-assessed health—considered in this paper. [ABSTRACT FROM AUTHOR]

Preprints have arrived. In increasing numbers, researchers across the life sciences are embracing the once-niche practice, shaking off decades of reluctance and posting hundreds of papers per week to preprint servers, sharing their findings with the community before embarking on the weary march through peer review. However, there are limited methods for individuals sifting through this avalanche of research to identify the preprints that are most relevant to their interests. Here, we describe , a website that indexes all preprints posted to , the largest preprint server in the life sciences, and allows users to filter and sort papers based on download metrics and Twitter activity over a variety of categories and time periods. In this work, we hope to make it easier for readers to find relevant research on bioRxiv and to improve the visibility of preprints currently being read and discussed online. [ABSTRACT FROM AUTHOR]

This paper reports the impact of a major life event–death–on the physical, psychological and social well-being of the deceased’s close friends. We utilised data from a large longitudinal survey covering a period of 14 years (2002–2015) consisting a cohort of 26,515 individuals in Australia, of whom 9,586 had experienced the death of at least one close friend. This longitudinal cohort dataset comprises responses to the SF-36 (health related quality of life measure) and allowed for analysis of the short and longer-term impacts of bereavement. In order to manage the heterogeneity of the socio-demographics of respondents who did/not experience a death event, we use a new and robust approach known as the Entropy Balancing method to construct a set of weights applied to the bereaved group and the control group (the group that did not experience death). This approach enables us to match the two groups so that the distribution of socio-demographic variables between the two groups are balanced. These variables included gender, age, marital status, ethnicity, personality traits, religion, relative socio-economic disadvantage, economic resources, and education and occupation and where they resided. The data show, for the first time, a range of negative and enduring consequences experienced by people following the death of a close friend. Significant adverse physical and psychological well-being, poorer mental health and social functioning occur up to four years following bereavement. Bereaved females experienced a sharper fall in vitality, suffered greater deterioration in mental health, impaired emotional and social functioning than the male counterparts up to four years after the death. The data show that the level of social connectedness plays an important role in bereavement outcomes. Specifically, we found that less socially active respondents experienced a longer deterioration in physical and psychological health. Finally, we found evidence that the death of a close friend lowered the respondent’s satisfaction with their health. Since death of friends is a universal phenomenon, we conclude the paper by reflecting on the need to recognise the death of a close friend as a substantial experience, and to offer support and services to address this disenfranchised grief. Recognising bereaved friends as a group experiencing adverse outcomes can be used internationally to prompt health and psychological services to assist this specific group, noting that there may be substantial longevity to the negative sequelae of the death of a friend. Facilitating bereaved people’s support networks may be a fruitful approach to minimising these negative outcomes. [ABSTRACT FROM AUTHOR]

The last several decades have witnessed a shift in the way in which news is delivered and consumed by users. With the growth and advancements in mobile technologies, the Internet, and Web 2.0 technologies users are not only consumers of news, but also producers of online content. This has resulted in a novel and highly participatory cyber-physical news awareness ecosystem that fosters digital activism, in which volunteers contribute content to online communities. While studies have examined the various components of this news awareness ecosystem, little is still known about how news media coverage (and in particular digital media) impacts digital activism. In order to address this challenge and develop a greater understanding of it, this paper focuses on a specific form of digital activism, that of the production of digital geographical content through crowdsourcing efforts. Using refugee camps from around the world as a case study, we examine the relationship between news coverage (via Google news), search trends (via Google trends) and user edit contribution patterns in OpenStreetMap, a prominent geospatial data crowdsourcing platform. In addition, we compare and contrast these patterns with user edit patterns in Wikipedia, a well-known non-geospatial crowdsourcing platform. Using Google news and Google trends to derive a measure of thematic public awareness, our findings indicate that digital activism bursts tend to take place during periods of sustained build-up of public awareness deficit or surplus. These findings are in line with two prominent mass communication theories: agenda setting and corrective action, and suggest the emergence of a novel stimulus-awareness-activism framework in today’s participatory digital age. Moreover, these findings further complement existing research examining the motivational factors that drive users to contribute to online collaborative communities. This paper brings us one step closer to understanding the underlying mechanisms that drive digital activism in particular in the geospatial domain. [ABSTRACT FROM AUTHOR]

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970–2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship. [ABSTRACT FROM AUTHOR]

Objective: There is a need for structure to achieve functional patient engagement within mental healthcare organizations, and for clarification on how to proceed on a strategic level. The aim of this paper is to shed light on the implementation of a strategic organizational structure for patient engagement in mental health by examining why and how to implement a structure, the organizational and environmental factors that facilitate or limit the process, and the perceived consequences of the implementation. Method: This paper evaluates the implementation of a strategic committee for patient engagement in a mental healthcare organization in Montreal (Quebec, Canada). The research was designed as a qualitative single case study using a deductive approach by means of a conceptual framework. Data sources consisted in ten semi-structured interviews, three focus groups, and organizational documents. Results: The strategic committee for patient engagement was implemented as a means to formalize patient partner participation, following the introduction of a vision of full citizenship. Important aspects of its implementation included its composition and role, the elaboration of a framework for patient partner participation, and finally, ongoing application and evaluation of the framework. Several facilitating factors were identified, including executive management support, leadership, and a vision behind the participation. Limiting factors mainly consisted of resistance towards patient participation and the existence of stigma. Consequences included increased and improved patient engagement, as well as reduced stigma within the organization. Conclusion: This study shows that the implementation of a strategic organizational structure for patient engagement is comprehensive. It further shows the importance of a vision and an articulate leadership involving several actors. Further research is needed regarding the impact of this type of strategic structure on a clinical level. [ABSTRACT FROM AUTHOR]

Background: Digital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country’s political landscape from Twitter data. Method: The proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets. We validate our methodology on a case study on the 2017 French presidential election (60 million Twitter exchanges between more than 2.4 million users) via two independent methods: the comparison between our automated political categorization and a human categorization based on the evaluation of a sample of 5000 profiles descriptions; the correspondence between the reconfigurations detected in the reconstructed political landscape and key political events reported in the media. This latter validation demonstrated the ability of our approach to accurately reflect the reconfigurations at play in the off-line political scene. Results: We built on this reconstruction to give insights into the opinion dynamics and the reconfigurations of political communities at play during a presidential election. First, we propose a quantitative description and analysis of the political engagement of members of political communities. Second, we analyze the impact of political communities on information diffusion and in particular on their role in the fake news phenomena. We measure a differential echo chamber effect on the different types of political news (fake news, debunks, standard news) caused by the community structure and emphasize the importance of addressing the meso-structures of political networks in understanding the fake news phenomena. Conclusions: Giving access to an intermediate level, between sociological surveys in the field and large statistical studies (such as those conducted by national or international organizations) we demonstrate that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces. We conclude this paper with a comment on the political and ethical implications of the use of social networks data in politics. We stress the importance of developing social macroscopes that will enable citizens to better understand how they collectively make society and propose as example the “Politoscope”, a macroscope that delivers some of our results in an interactive way. [ABSTRACT FROM AUTHOR]

Multi-stakeholder platforms (MSPs) have been playing an increasing role in interventions aiming to generate and scale innovations in agricultural systems. However, the contribution of MSPs in achieving innovations and scaling has been varied, and many factors have been reported to be important for their performance. This paper aims to provide evidence on the contribution of MSPs to innovation and scaling by focusing on three developing country cases in Burundi, Democratic Republic of Congo, and Rwanda. Through social network analysis and logistic models, the paper studies the changes in the characteristics of multi-stakeholder innovation networks targeted by MSPs and identifies factors that play significant roles in triggering these changes. The results demonstrate that MSPs do not necessarily expand and decentralize innovation networks but can lead to contraction and centralization in the initial years of implementation. They show that some of the intended next users of interventions with MSPs–local-level actors–left the innovation networks, whereas the lead organization controlling resource allocation in the MSPs substantially increased its centrality. They also indicate that not all the factors of change in innovation networks are country specific. Initial conditions of innovation networks and funding provided by the MSPs are common factors explaining changes in innovation networks across countries and across different network functions. The study argues that investigating multi-stakeholder innovation network characteristics targeted by the MSP using a network approach in early implementation can contribute to better performance in generating and scaling innovations, and that funding can be an effective implementation tool in developing country contexts. [ABSTRACT FROM AUTHOR]

Public transport (PT) is a key element in most major cities around the world. With the development of smartphones, available journey planning information is becoming an integral part of the PT system. Each traveler has specific preferences when undertaking a trip, and these preferences can also be reflected on the smartphone. This paper considers transit assignment in urban public-transport networks in which the passengers receive smartphone-based information containing elements that might influence the travel decisions in relation to line loads, as well as passenger benefits, and the paper discusses the transition from the current widespread choosing approach to a personalized decision-making approach based on smartphone information. The approach associated with smartphone guidance that considers passengers’ preference on travel time, waiting time and transfer is proposed in the process of obtaining his/her preferred route from the potential travel routes generated by the Deep First Search (DFS) method. Two other approaches, based on the scenarios reflecting reality, include passengers with access to no real time information, and passengers that only have access to the arrival time at the platform are used as comparisons. For illustration, the same network proposed by Spiess and Florian is utilized on the experiments in an agent-based model. Two experiments are conducted respectively according to whether each passenger’s choosing method is consistent. As expected, the results in the first experiment showed that the travel for consistent passengers with smartphone guidance was clearly shorter and that it can reduce travel time exceeding 15% and weighted cost exceeding 20%, and the average saved time approximated 3.88 minutes per passenger. The second experiment presented that travel cost, as well as cost savings, gradually decreased by employing smartphone guidance, and the maximum cost savings accounted for 14.2% of the total weighted cost. [ABSTRACT FROM AUTHOR]

The nodes and their connection relationships are the two main bodies for dynamic complex networks. In existing theoretical researches, the phenomena of stabilization and synchronization for complex dynamical networks are generally regarded as the dynamic characteristic behaviors of the nodes, which are mainly caused by coupling effect of connection relationships between nodes. However, the connection relationships between nodes are also one main body of a time-varying dynamic complex network, and thus they may evolve with time and maybe show certain characteristic phenomena. For example, the structural balance in the social networks and the synaptic facilitation in the biological neural networks. Therefore, it is important to investigate theoretically the reasons in dynamics for the occurrence. Especially, from the angle of large-scale systems, how the dynamic behaviors of nodes (such as the individuals, neurons) contribute to the connection relationships is one of worthy research directions. In this paper, according to the structural balance theory of triad proposed by F. Heider, we mainly focus on the connection relationships body, which is regarded as one of the two subsystems (another is the nodes body), and try to find the dynamic mechanism of the structural balance with the internal state behaviors of the nodes. By using the Riccati linear matrix differential equation as the dynamic model of connection relationships subsystem, it is proved under some mathematic conditions that the connection relationships subsystem is asymptotical structural balance via the effects of the coupling roles with the internal state of nodes. Finally, the simulation example is given to show the validity of the method in this paper. [ABSTRACT FROM AUTHOR]

Prior studies showed that scientists’ professional networks contribute to research productivity, but little work has examined what factors predict the formation of professional networks. This study sought to 1) examine what factors predict the formation of international ties between faculty and graduate students and 2) identify how these international ties would affect publication productivity in three East Asian countries. Face-to-face surveys and in-depth semi-structured interviews were conducted with a sample of faculty and doctoral students in life sciences at 10 research institutions in Japan, Singapore, and Taiwan. Our final sample consisted of 290 respondents (84 faculty and 206 doctoral students) and 1,435 network members. We used egocentric social network analysis to examine the structure of international ties and how they relate to research productivity. Our findings suggest that overseas graduate training can be a key factor in graduate students’ development of international ties in these countries. Those with a higher proportion of international ties in their professional networks were likely to have published more papers and written more manuscripts. For faculty, international ties did not affect the number of manuscripts written or of papers published, but did correlate with an increase in publishing in top journals. The networks we examined were identified by asking study participants with whom they discuss their research. Because the relationships may not appear in explicit co-authorship networks, these networks were not officially recorded elsewhere. This study sheds light on the relationships of these invisible support networks to researcher productivity. [ABSTRACT FROM AUTHOR]

While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance. [ABSTRACT FROM AUTHOR]

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has. [ABSTRACT FROM AUTHOR]

This study shows how liking politicians’ public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60–70%). The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems. [ABSTRACT FROM AUTHOR]

We learn language from our social environment. In general, the more sources we have, the less informative each of them is, and the less weight we should assign it. If this is the case, people who interact with fewer others should be more susceptible to the influence of each of their interlocutors. This paper tests whether indeed people who interact with fewer other people have more malleable phonological representations. Using a perceptual learning paradigm, this paper shows that individuals who regularly interact with fewer others are more likely to change their boundary between /d/ and /t/ following exposure to an atypical speaker. It further shows that the effect of number of interlocutors is not due to differences in ability to learn the speaker’s speech patterns, but specific to likelihood of generalizing the learned pattern. These results have implications for both language learning and language change, as they suggest that individuals with smaller social networks might play an important role in propagating linguistic changes. [ABSTRACT FROM AUTHOR]

Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature. The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature. In particular, the pattern of tweeting to the top ten most tweeted scientific dental articles and of tweeting by accounts is examined. The ideal that tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice. We see much presumably human tweeting almost entirely mechanical and devoid of original thought, no evidence of conversation, tweets generated by monomania, duplicate tweeting from many accounts under centralized professional management and tweets generated by bots. Some accounts exemplify the ideal, but they represent less than 10% of tweets. Therefore, any conclusions drawn from twitter data is swamped by the mechanical nature of the bulk of tweeting behavior. In light of these results, we discuss the compatibility of Twitter with the research enterprise as well as some of the financial incentives behind these patterns. [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]

Biological network figures are ubiquitous in the biology and medical literature. On the one hand, a good network figure can quickly provide information about the nature and degree of interactions between items and enable inferences about the reason for those interactions. On the other hand, good network figures are difficult to create. In this paper, we outline 10 simple rules for creating biological network figures for communication, from choosing layouts, to applying color or other channels to show attributes, to the use of layering and separation. These rules are accompanied by illustrative examples. We also provide a concise set of references and additional resources for each rule. [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]

Empirical studies show that discrimination by identity found in offline markets also prevails online. This paper reveal that in a competitive market, buyers that intend to discriminate exist but they are prevented from influencing the market outcome. To this end, we construct a field experiment on eBay, where half of the sellers disclose their names in their usernames while the other half do not. eBay, however, automatically discloses the seller’s names to the buyer after the auction. In the anonymous auctions, winning bidders thus learn the identity of the seller after the auction ends, and here we find buyers to discriminate against sellers with foreign-sounding names by leaving them feedback less often. However, there is no such discrimination in feedback provision when the seller name was known to the buyer before the auction. When bidders know the names of the sellers, the bidders with animus towards individuals with specific names can select out of auctions from these sellers, leaving winners that do not discriminate. One would expect that the auctions of for example sellers with foreign-sounding names would receive fewer bidders and thus lower auction prices. However, we observe no such differences: there are no statistically significant differences in the number of bids or auction prices received by sellers with foreign or domestic sounding names. [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]

This paper examined, with a behavioral paradigm, to what extent people choose to view stimuli that portray death, violence or harm. Based on briefly presented visual cues, participants made choices between highly arousing, negative images and positive or negative alternatives. The negative images displayed social scenes that involved death, violence or harm (e.g., war scene), or decontextualized, close-ups of physical harm (e.g., mutilated face) or natural threat (e.g., attacking shark). The results demonstrated that social negative images were chosen significantly more often than other negative categories. Furthermore, participants preferred social negative images over neutral images. Physical harm images and natural threat images were not preferred over neutral images, but were chosen in about thirty-five percent of the trials. These results were replicated across three different studies, including a study that presented verbal descriptions of images as pre-choice cues. Together, these results show that people deliberately subject themselves to negative images. With this, the present paper demonstrates a dynamic relationship between negative information and behavior and advances new insights into the phenomenon of morbid curiosity. [ABSTRACT FROM AUTHOR]

The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. [ABSTRACT FROM AUTHOR]

Creativity is a complex, multi-faceted concept encompassing a variety of related aspects, abilities, properties and behaviours. If we wish to study creativity scientifically, then a tractable and well-articulated model of creativity is required. Such a model would be of great value to researchers investigating the nature of creativity and in particular, those concerned with the evaluation of creative practice. This paper describes a unique approach to developing a suitable model of how creative behaviour emerges that is based on the words people use to describe the concept. Using techniques from the field of statistical natural language processing, we identify a collection of fourteen key components of creativity through an analysis of a corpus of academic papers on the topic. Words are identified which appear significantly often in connection with discussions of the concept. Using a measure of lexical similarity to help cluster these words, a number of distinct themes emerge, which collectively contribute to a comprehensive and multi-perspective model of creativity. The components provide an ontology of creativity: a set of building blocks which can be used to model creative practice in a variety of domains. The components have been employed in two case studies to evaluate the creativity of computational systems and have proven useful in articulating achievements of this work and directions for further research. [ABSTRACT FROM AUTHOR]

Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers. [ABSTRACT FROM AUTHOR]

Background: Tobacco industry interference has been identified as the greatest obstacle to the implementation of evidence-based measures to reduce tobacco use. Understanding and addressing industry interference in public health policy-making is therefore crucial. Existing conceptualisations of corporate political activity (CPA) are embedded in a business perspective and do not attend to CPA's social and public health costs; most have not drawn on the unique resource represented by internal tobacco industry documents. Building on this literature, including systematic reviews, we develop a critically informed conceptual model of tobacco industry political activity.Methods and Findings: We thematically analysed published papers included in two systematic reviews examining tobacco industry influence on taxation and marketing of tobacco; we included 45 of 46 papers in the former category and 20 of 48 papers in the latter (n = 65). We used a grounded theory approach to build taxonomies of "discursive" (argument-based) and "instrumental" (action-based) industry strategies and from these devised the Policy Dystopia Model, which shows that the industry, working through different constituencies, constructs a metanarrative to argue that proposed policies will lead to a dysfunctional future of policy failure and widely dispersed adverse social and economic consequences. Simultaneously, it uses diverse, interlocking insider and outsider instrumental strategies to disseminate this narrative and enhance its persuasiveness in order to secure its preferred policy outcomes. Limitations are that many papers were historical (some dating back to the 1970s) and focused on high-income regions.Conclusions: The model provides an evidence-based, accessible way of understanding diverse corporate political strategies. It should enable public health actors and officials to preempt these strategies and develop realistic assessments of the industry's claims. [ABSTRACT FROM AUTHOR]

Traffic congestion is an important problem both on an individual and on a societal level and much research has been done to explain and prevent their emergence. There are currently many systems which provide a reasonably good picture of actual road traffic by employing either fixed measurement points on highways or so called “floating car data” i.e. by using velocity and location data from roaming, networked, GPS enabled members of traffic. Some of these systems also offer forecasting of road conditions based on such historical data. To my knowledge there is as yet no system which offers advance notice on road conditions based on a signal which is guaranteed to occur in advance of these conditions and this is the novelty of this paper. Google Search intensity for the German word (i.e. traffic jam) peaks 2 hours ahead of the number of traffic jam reports as reported by the ADAC, a well known German automobile club and the largest of its kind in Europe. This is true both in the morning (7 am to 9 am) and in the evening (4 pm to 6 pm). The main result of this paper is then that after controlling for time-of-day and day-of-week effects we can still explain a significant additional portion of the variation of the number of traffic jam reports with Google Trends and we can thus explain well over 80% of the variation of road conditions using Google search activity. A one percent increase in Google stau searches implies a .4 percent increase of traffic jams. Our paper is a proof of concept that aggregate, timely delivered behavioural data can help fine tune modern societies and prompts for more research with better, more disaggregated data in order to also achieve practical solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method. [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]

The user interaction in online social networks can not only reveal the social relationships among users in e-commerce systems, but also imply the social preferences of a target user for recommendation services. However, the current research has rarely explored the impact of social interaction on recommendation performance, especially now that recommender systems face increasing challenges and suffer from poor efficiency due to social data overload. Therefore, applied research on user interaction has become increasingly necessary in the field of social recommendation. In this paper, we develop a novel social recommendation method based on the user interaction in complex social networks, called the SRUI model, to present a basis for improving the efficiency of the recommender systems. Specifically, a weighted social interaction network is first mapped to represent the interactions among social users according to the gathered information about historical user behavior. Thereafter, the complete path set is mined by the complete path mining (CPM) algorithm to find social similar neighbors with tastes similar to those of the target user. Finally, the social similar tendencies of the users on the complete paths are obtained to predict the final ratings of items through the SRUI model. A series of experimental results based on two real public datasets show that our approach performs better than other state-of-the-art methods in terms of recommendation performance. [ABSTRACT FROM AUTHOR]

The 5Rights Youth Juries are an educational intervention to promote digital literacy by engaging participants (i.e. jurors) in a deliberative discussion around their digital rights. The main objective of these jury-styled focus groups is to encourage children and young people to identify online concerns and solutions with a view to developing recommendations for government policy-makers and industry chiefs. The methodology included a series of dramatized scenarios that encourage jurors to deliberate about their digital rights. This paper compares two formats for these scenarios: live actors and professionally recorded and edited videos of the same actors. Results failed to show any major differences between formats indicating the cost-effectiveness of the video-recorded format and the possibility for others to run the 5Rights Youth Juries with the support of an online open educational resource. [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]

The Internet provides students with a unique opportunity to connect and maintain social ties with peers from other schools, irrespective of how far they are from each other. However, little is known about the real structure of such online relationships. In this paper, we investigate the structure of interschool friendship on a popular social networking site. We use data from 36, 951 students from 590 schools of a large European city. We find that the probability of a friendship tie between students from neighboring schools is high and that it decreases with the distance between schools following the power law. We also find that students are more likely to be connected if the educational outcomes of their schools are similar. We show that this fact is not a consequence of residential segregation. While high- and low-performing schools are evenly distributed across the city, this is not the case for the digital space, where schools turn out to be segregated by educational outcomes. There is no significant correlation between the educational outcomes of a school and its geographical neighbors; however, there is a strong correlation between the educational outcomes of a school and its digital neighbors. These results challenge the common assumption that the Internet is a borderless space, and may have important implications for the understanding of educational inequality in the digital age. [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]

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]

Information about a person’s income can be useful in several business-related contexts, such as personalized advertising or salary negotiations. However, many people consider this information private and are reluctant to share it. In this paper, we show that income is predictable from the digital footprints people leave on Facebook. Applying an established machine learning method to an income-representative sample of 2,623 U.S. Americans, we found that (i) Facebook Likes and Status Updates alone predicted a person’s income with an accuracy of up to r = 0.43, and (ii) Facebook Likes and Status Updates added incremental predictive power above and beyond a range of socio-demographic variables (ΔR2 = 6–16%, with a correlation of up to r = 0.49). Our findings highlight both opportunities for businesses and legitimate privacy concerns that such prediction models pose to individuals and society when applied without individual consent. [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]