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The aim of this study is to reveal a robust correlation between the amount of attention international journalism devotes to scientific papers and the amount of attention scientific journals devote to the respective topics. Using a Mainstream-Media-Score (MSM) ≥ 100 (which we regard as an indicator for news media attention) from the altmetrics provider Altmetric, we link 983 research articles with 185,166 thematically similar articles from the PubMed database (which we use to operationalize attention from scientific journals). The method we use is to test whether there is a concomitant increase in scientific attention after a research article has received popular media coverage. To do so, we compare the quotient of the number of thematically similar articles published in scientific journals during the period before and after the publication of an MSM ≥ 100 article. Our main result shows that in 59 percent of cases, more thematically similar articles were published in scientific journals after a scientific paper received noteworthy news media coverage than before (p < 0.01). In this context, we neither found significant differences between various types of scientific journal (p = 0.3) nor between scientific papers that were originally published in renowned opinion-leading journals or in less renowned, non-opinion-leading journals (p = 0.1). Our findings indicate a robust correlation between the choice of topics in the mass media and in research. However, our study cannot clarify whether this correlation occurs because researchers and/or scientific journals are oriented towards public relevance (publicity effect) or whether the correlation is due to the parallelism of relevance attributions in quality journalism and research (earmark hypothesis). We infer that topics of social relevance are (more) likely to be picked up by popular media as well as by scientific journals. Altogether, our study contributes new empirical findings to the relationship between topic selection in journalism and in research. [ABSTRACT FROM AUTHOR]

Social media plays an important role in how journalists gather and report news. To understand journalists' professional environment, we examine the networks of journalists on Twitter who cover politics for U.S. newspapers in conjunction with a sample of journalists who completed a survey. By combining both their network data and survey responses, we examine the distribution of journalists' ideology (n = 264) and journalistic values (n = 247); and using the network data, we examine the directional relationships between journalists working at large and small papers (n = 4,661). We find that journalists tend to form connections with those who share similar journalistic values. However, we find little evidence that journalists build professional relationships based on similarity in political ideology. Lastly, journalists at larger media outlets are more likely to be central in journalists' Twitter networks, providing evidence that journalists look to other journalists at larger outlets for direction in news coverage. Our evidence provides unique insights into how social media illuminates journalists' professional environment and how that environment may shape news coverage., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As researchers use computational methods to study complex social behaviors at scale, the validity of this computational social science depends on the integrity of the data. On July 2, 2015, Jason Baumgartner published a dataset advertised to include “every publicly available Reddit comment” which was quickly shared on Bittorrent and the Internet Archive. This data quickly became the basis of many academic papers on topics including machine learning, social behavior, politics, breaking news, and hate speech. We have discovered substantial gaps and limitations in this dataset which may contribute to bias in the findings of that research. In this paper, we document the dataset, substantial missing observations in the dataset, and the risks to research validity from those gaps. In summary, we identify strong risks to research that considers user histories or network analysis, moderate risks to research that compares counts of participation, and lesser risk to machine learning research that avoids making representative claims about behavior and participation on Reddit. [ABSTRACT FROM AUTHOR]

Mass media campaigns are frequently used to address public health issues. Considering the considerable cost, there has been little analysis of why campaigns sometimes fail. This study used a sequential mixed methods approach to explore the mechanisms that can lead to failure and to identify what can be done to avoid or overcome common mistakes in campaign planning, implementation, and evaluation. We conducted interviews and a survey with 28 public health social marketing and mass media campaign experts over three rounds of research and analysed the data thematically, generating themes inductively. We identified four systemic factors that drive success: long-term strategic thinking and commitment, understanding the campaign context, doing and learning from evaluation, and fostering strong relationships. The factors did not operate in isolation, rather good (or poor) execution in one area was likely to influence performance in another. The experts also emphasised that a change of political context could drastically affect one or more of the identified factors. Our analysis showed that campaign failures are not simply individuals making mistakes. Systemic issues throughout the planning, execution, and evaluation phases need to be addressed if campaign outcomes are to improve. [ABSTRACT FROM AUTHOR]

This qualitative study explores how and why journalists use preprints-unreviewed research papers-in their reporting. Through thematic analysis of interviews conducted with 19 health and science journalists in the second year of the COVID-19 pandemic, it applies a theoretical framework that conceptualizes COVID-19 preprint research as a form of post-normal science, characterized by high scientific uncertainty and societal relevance, urgent need for political decision-making, and value-related policy considerations. Findings suggest that journalists approach the decision to cover preprints as a careful calculation, in which the potential public benefits and the ease of access preprints provided were weighed against risks of spreading misinformation. Journalists described viewing unreviewed studies with extra skepticism and relied on diverse strategies to find, vet, and report on them. Some of these strategies represent standard science journalism, while others, such as labeling unreviewed studies as preprints, mark a departure from the norm. However, journalists also reported barriers to covering preprints, as many felt they lacked the expertise or the time required to fully understand or vet the research. The findings suggest that coverage of preprints is likely to continue post-pandemic, with important implications for scientists, journalists, and the publics who read their work., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

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 aims to analyze the divergent perspectives of Indian and Pakistani English print media on opening the Kartarpur corridor. It is a four-kilometer-long cordoned-off strip from the Indo-Pak international border to the Gurdwara Darbar Sahib Kartarpur located in Pakistan. The basic purpose of establishing this corridor is to give easy access to the Indian Sikh community. The initiative was taken into account in August 2018, which resulted in the appearance of a vast quantity of contemplations in the national print media of both countries, especially until the opening of the corridor in November 2019. Print media plays a key role in building knowledge and framing the general public's opinion through interpreting an issue. The data were taken from Dawn, The News International (Pakistan), The Times of India, and Hindustan Times (India) from August 2018 to March 2020 using Lexus Nexus Library. The corpus analysis was carried out by applying the lexical study of Natural Language Processing (NLP) through its Latent Dirichlet Allocation (LDA) tool to find out the general patterns or topics in the print media of both countries. It was found that Pakistani print media terms the Kartarpur corridor as a sign of regional peace, religious tourism, mediation, and diplomatic efforts. In contrast, Indian print media focuses on apprehensions related to traveling modalities, pilgrimage facilities, and tensions between the two states., Competing Interests: The authors have declared that no competing interests exist.

Men are underrepresented in family planning (FP) research, and despite the widespread promotion of FP through mass media, there is no systematic evaluation on how mass media exposure influences their FP knowledge, attitudes and behavior. Using Demographic and Health Survey (DHS) data from 31 countries in Sub-Saharan Africa (SSA), collected between 2010 and 2019, this paper examines the associations between three types of traditional mass media (radio, television and print) with FP knowledge, attitudes and method choices among reproductive age men in SSA, relative to other socio-cultural factors. Estimates to quantify the relative contribution of each type of mass media, relative to other evidence-based socio-cultural influences on FP outcomes, were derived using the Shorrocks-Shapley decomposition. Radio exposure had the largest impact on FP knowledge, attitudes and method choice, accounting for 26.1% of the variance in FP knowledge, followed by Television (21.4%) and education attainment (20.7%). Mass media exposure had relatively minimal impact on FP method choice, and between the three types of mass media, television (8%) had the largest influence on FP method choice. Print media had comparatively lesser impact on FP knowledge (8%), attitudes (6.2%) and method choice (3.2%). Findings suggest that mass media exposure has positive influences on FP knowledge, attitudes and method choice but its influence on FP knowledge, attitudes and method choice is smaller relative to other socio-cultural factors such as education, household wealth and marital status. As such, efforts to increase FP uptake in Sub-Saharan Africa should take into consideration the impact of these socio-cultural economic factors., Competing Interests: The author has declared that no competing interests exist.

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method. [ABSTRACT FROM AUTHOR]

Introduction: Violence against medical staff has been prevalent in China over the past two decades. Although Chinese authorities have released many laws and regulations to protect medical staff from violence since 2011, the legal approach alone is unlikely to resolve this complex issue. In particular, several cases of violence against medical staff in China have caused great media sensation., Method: This paper proposes an integrated model that combines the environmental stimuli theory, broken windows theory, and rational choice theory. It adopts the fuzzy set qualitative comparative analysis (fsQCA) to untangle the causal relationship between violence against medical staff, media sensation, and judicial judgment. We examined reports of medical violence on media and news websites from January 1, 2010, to January 31, 2020, and selected 50 cases with detailed information for this study., Results: The results show that each condition is not sufficient for the absence of judicial judgment, but when combined, they are conducive to the outcome. The conditions of hospital level, medical cost, and media sensation play important roles. The providers, patients, and environmental factors are indicators of inadequate or lack of judicial judgment, which corresponds to previous expectations., Conclusions: The integrated model greatly enriches the extant theories and literature, and also yields implications for preventing violence against medical staff in China. We suggest that sustainable and innovative healthcare reform should be initiated. For example, public hospitals should remain the cornerstone of national public health security. Medical staff in public hospitals must be regarded as "civil servants". Therefore, the current legal system should be improved. The media should objectively report events concerning medical staff and improve public healthcare knowledge., Competing Interests: The authors have declared that no competing interests exist.

Since the inception of the National Programme for Family Planning, messages on family planning (FP) have been promoted across India using different mass media platforms. Mass media plays an important role in disseminating important information among the masses, such as how reversible modern methods give women more reproductive choices than opting for permanent methods that limit their child-bearing capacity. Mass media can provide a continuous flow of information and motivation to deter women from discontinuing the methods they have opted for. However, very few studies have been conducted on this issue, especially using recently available data. This study particularly focuses on exposure to mass media and the use of reversible modern methods of family planning among married women in India. The data for this study was obtained from the National Family Health Survey (2015-16) on currently married women aged 15-49 years. The association of reversible modern method use with media exposure variables was examined, controlling for a set of independent variables from multiple levels-individual, district, state, and region. The findings from this study showed that television was the most important medium for disseminating information on FP among married women in India. Spatial analysis revealed that some districts in the north, parts of the northeast, and Kerala in South India lacked any television exposure. The results from the decomposition analysis showed that mass media exposure was associated with a 14% increase in the use of reversible modern methods. Results from the multilevel analyses showed that exposure to TV along with other media (AOR 1.57 95% CI 1.49-1.65) and exposure to FP messages through different media (AOR 1.22 95% CI 1.12-1.32) had a significant positive effect on the use of reversible modern methods even when various individual, district, state, and regional-level factors were controlled. The findings of this paper provide evidence supporting the use of mass media to promote and increase awareness of voluntary contraceptive use in India. An increase in mass media exposure coupled with improvement in coverage and services of the FP program can significantly increase the use of reversible modern methods in a cost-effective yet efficient manner among women in need of FP services., Competing Interests: The authors have declared that no competing interests exist.

The SARS-CoV-2 pandemic has emerged as one of the most dramatic health crises of recent decades. This paper treats mainstream news about the current pandemic as a valuable entry point for analyzing the relationship between science and politics in the public sphere, where the outbreak must be both understood and confronted through appropriate public-health policy decisions. In doing so, the paper aims to examine which actors, institutions, and experts dominate the SARS-CoV-2 media narratives, with particular attention to the roles of political, medical, and scientific actors and institutions within the pandemic crisis. The study relies on a large dataset consisting of all SARS-CoV-2 articles published by eight major Italian national newspapers between January 1, 2020 and June 15, 2020. These articles underwent a quantitative analysis based on a topic modeling technique. The topic modeling outputs were further analyzed by innovatively combining ad-hoc metrics and a classifier based on the stacking ensemble method (combining regularized logistic regression and linear stochastic gradient descent) for quantifying scientific salience. This enabled the identification of relevant topics and the analysis of the roles that different actors and institutions engaged in making sense of the pandemic. The results show how the health emergency has been addressed primarily in terms of political regulation and concerns and only marginally as a scientific matter. Hence, science has been overwhelmed by politics, which, in media narratives, exerts a moral as well as regulatory authority. Media narratives exclude neither scientific issues nor scientific experts; rather, they configure them as a subsidiary body of knowledge and expertise to be mobilized as an ancillary, impersonal institution useful for legitimizing the expansion of political jurisdiction over the governance of the emergency., Competing Interests: The authors have declared that no competing interests exist.

The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network’s tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment. [ABSTRACT FROM AUTHOR]

Background: Building or maintaining institutional trust is of central importance in democratic societies since negative experiences (potentially leading to mistrust) with government or other institutions may have a much more profound effect than positive experiences (potentially maintaining trust). Healthy democracy relies on more than simply trusting the national government of the time, and is mediated through other symbols of institutional power, such as the legal system, banks, the media and religious organisations. This paper focuses on institutional trust–the level and predictors of trust in some of the major institutions in society, namely politics, the media, banks, the legal system and religious organisations. We present analyses from a consolidated dataset containing data from six countries in the Asia Pacific region–Australia, Hong Kong, Japan, South Korea, Taiwan and Thailand. Methods: Cross-sectional surveys were undertaken in each country in 2009–10, with an overall sample of 6331. Analyses of differences in overall levels of institutional trust between countries were undertaken using Chi square analyses. Multivariate binomial logistic regression analysis was undertaken to identify socio-demographic predictors of trust in each country. Results: Religious institutions, banks and the judicial system had the highest overall trust across all countries (70%, 70% and 67% respectively), followed by newspapers and TV (59% and 58%) and then political leaders (43%). The range of levels of higher trust between countries differed from 43% for banks (range 49% in Australia to 92% in Thailand) to 59% for newspapers (28% in Australia to 87% in Japan). Across all countries, except for Australia, trust in political leaders had the lowest scores, particularly in Japan and South Korea (25% in both countries). In Thailand, people expressed the most trust in religious organisations (94%), banks (92%) and in their judicial/legal system (89%). In Hong Kong, people expressed the highest level of trust in their judicial/legal system (89%), followed by religious organisations (75%) and banks (77%). Australian respondents reported the least amount of trust in TV/media (24%) and press/newspapers (28%). South Korea put the least trust in their political leaders (25%), their legal system (43%) and religious organisations (45%). The key predictors of lower trust in institutions across all countries were males, people under 44 years and people unsatisfied with the health and standard of living. Conclusion: We interpreted our data using Fukuyama’s theory of ‘high/low trust’ societies. The levels of institutional trust in each society did not conform to our hypothesis, with Thailand exhibiting the highest trust (predicted to be medium level), Hong Kong and Japan exhibiting medium trust (predicted to be low and high respectively) and Australia and South Korea exhibiting low trust (predicted to be high and medium respectively). Taiwan was the only country where the actual and predicted trust was the same, namely low trust. Given the fact that these predictors crossed national boundaries and institutional types, further research and policy should focus specifically on improving trust within these groups in order that they can be empowered to play a more central role in democratic vitality. [ABSTRACT FROM AUTHOR]

Background: The news media is a key source for health and medical information, and relies to a large degree on material from press releases (PR). Medical universities are key players in the dissemination of PRs. This study aims to 1) explore the relation between the quality of press releases (PRs) from medical universities and their corresponding news stories (NSs) and 2) to identify the likelihood that specific scientific and interest-raising measures appear or are omitted in PRs and NSs., Methods and Findings: In this retrospective study using quantitative content analysis, PRs (n = 507) from 21 medical universities in Germany, the Netherlands, Sweden, the USA and the UK were retrieved. Of all PRs, 33% had media coverage, resulting in 496 NSs. With two codebooks, 18 scientific (e.g. reporting the study design of the study correctly) and 7 interest-raising measures (e.g. words like 'ground-breaking') were evaluated in the PRs and NSs. For all measures the percentage of presence in NSs and PRs was calculated, together with a Mean PR Influence Factor. Quality of PRs and NSs was defined as a score, based on 12 of the 18 scientific measures. Mean (SD) NS quality score was 6.5 (1.7) which was significantly lower than the PR score of 8.0 (1.5). The two quality scores were significantly correlated. Quality measures that were frequently omitted included reporting important study limitations (present in 21% of PRs, 21% of NSs), funding (59% of PRs, 7% of NSs) and conflicts of interest (16% of PRs, 3% of NSs). We did not evaluate the quality of the scientific papers (SPs), and can therefore not determine if the quality of PRs and NSs is associated with the quality of SPs., Conclusions: This large study of medical university press releases and corresponding news stories showed that important measures of a scientific study such as funding and study limitations were omitted to a very large extent. The lay public and health personnel as well as policy makers, politicians and other decision makers may be misled by incomplete and partly inaccurate representations of scientific studies which could negatively affect important health-related behaviours and decisions., Competing Interests: JK is a paid employee of Biostatistics AB and AL is a paid employee of the National Swedish Broadcasting Corporations. JK also received fees for statistical consultancy, unrelated to this manuscript. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Transmedia storytelling involves telling a story using multiple distinct media. The remit of stories that fall under this broad definition is vast, consequently causing theorists to examine different phenomena using tools that are not suitable for all forms of transmedia storytelling. The lack of critical tools means we are unable to describe, compare and analyse different experiences using common language. In this paper, we present our model that can be used to identify the fundamental structural features of a variety of transmedia storytelling forms. We illustrate its usage with twenty case studies and discuss how three groups of patterns emerge which can be identifiable in all transmedia stories. These patterns can be used to extend transmedia language and help form taxonomies, by identifying common patterns and their usages amongst various forms of transmedia stories. [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]

In political campaigns, perceived candidate credibility influences the persuasiveness of messages. In campaigns aiming to influence people's beliefs, micro-targeted campaigns (MTCs) that target specific voters using their psychological profile have become increasingly prevalent. It remains open how effective MTCs are, notably in comparison to population-targeted campaign strategies. Using an agent-based model, the paper applies recent insights from cognitive models of persuasion, extending them to the societal level in a novel framework for exploring political campaigning. The paper provides an initial treatment of the complex dynamics of population level political campaigning in a psychologically informed manner. Model simulations show that MTCs can take advantage of the psychology of the electorate by targeting voters favourable disposed towards the candidate. Relative to broad campaigning, MTCs allow for efficient and adaptive management of complex campaigns. Findings show that disliked MTC candidates can beat liked population-targeting candidates, pointing to societal questions concerning campaign regulations.

Many scientific researchers aspire to engage policy in their writing, but translating scientific research and findings into policy discussion often requires an understanding of the institutional complexities of legal and policy processes and actors. To examine how researchers have undertaken that challenge, we developed a set of metrics and applied them to articles published in one of the principal academic publication venues for science and policy—Science magazine’s Policy Forum. We reviewed each Policy Forum article published over a five-year period (2011–15), 220 in all. For each article, we assessed the level of policy content based on presence of a stated policy proposal or position and identification of the relevant policy actors and actions, and recorded attributes such as field of science, field of policy, number of references to legal and policy sources, number of authors from law and policy institutions, and number of citations. We find that a handful of science fields dominate publication frequency, but that all fields have produced publications with high policy engagement. Of the attributes, number of references to law and policy sources is correlated positively with level of engagement, whereas number of law and policy authors was fairly constant across all depths of engagement. Surprisingly, level of policy engagement was negatively correlated with the number of citations an article subsequently received. We offer possible explanations for these results and thoughts for authors, editors, and research institutions interested in facilitating robust engagement of policy in scientific writing. [ABSTRACT FROM AUTHOR]

Interest in modeling contemporary crime trends, a task that has historically been considered valuable to the public, researchers, and policymakers, is resurging. Advancements in criminology have made it clear that understanding crime trends necessarily involves understanding trends in how likely individuals are to report crimes to the police, as well as how likely the police are to accurately record those crimes. In this paper, we use dynamic linear models to simultaneously model the time series for several crime types in order to gain insight into trends in crime and crime reporting. We analyze crime data from Chicago spanning 2007 through 2016 and show how correlations in the way crime trends evolve may contain information about drivers of crime and crime reporting. We provide evidence of substantial differences in the relationships between the trends of crimes of different types depending on whether crimes are violent or nonviolent and whether or not crimes are tracked in the FBI’s Uniform Crime Report. [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]

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]

This paper investigates the relationship between candidates’ online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan’s 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates’ Facebook posts will be positively related to their election results, I calculated each candidate’s Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate’s Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election. [ABSTRACT FROM AUTHOR]

Neuroscience research on sex difference is currently a controversial field, frequently accused of purveying a ‘neurosexism’ that functions to naturalise gender inequalities. However, there has been little empirical investigation of how information about neurobiological sex difference is interpreted within wider society. This paper presents a case study that tracks the journey of one high-profile study of neurobiological sex differences from its scientific publication through various layers of the public domain. A content analysis was performed to ascertain how the study was represented in five domains of communication: the original scientific article, a press release, the traditional news media, online reader comments and blog entries. Analysis suggested that scientific research on sex difference offers an opportunity to rehearse abiding cultural understandings of gender. In both scientific and popular contexts, traditional gender stereotypes were projected onto the novel scientific information, which was harnessed to demonstrate the factual truth and normative legitimacy of these beliefs. Though strains of misogyny were evident within the readers’ comments, most discussion of the study took pains to portray the sexes’ unique abilities as equal and ‘complementary’. However, this content often resembled a form of benevolent sexism, in which praise of women’s social-emotional skills compensated for their relegation from more esteemed trait-domains, such as rationality and productivity. The paper suggests that embedding these stereotype patterns in neuroscience may intensify their rhetorical potency by lending them the epistemic authority of science. It argues that the neuroscience of sex difference does not merely reflect, but can actively shape the gender norms of contemporary society. [ABSTRACT FROM AUTHOR]

Background: Exaggerated or simplistic news is often blamed for adversely influencing public health. However, recent findings suggested many exaggerations were already present in university press releases, which scientists approve. Surprisingly, these exaggerations were not associated with more news coverage. Here we test whether these two controversial results also arise in press releases from prominent science and medical journals. We then investigate the influence of mitigating caveats in press releases, to test assumptions that caveats harm news interest or are ignored., Methods and Findings: Using quantitative content analysis, we analyzed press releases (N = 534) on biomedical and health-related science issued by leading peer-reviewed journals. We similarly analysed the associated peer-reviewed papers (N = 534) and news stories (N = 582). Main outcome measures were advice to readers and causal statements drawn from correlational research. Exaggerations in press releases predicted exaggerations in news (odds ratios 2.4 and 10.9, 95% CIs 1.3 to 4.5 and 3.9 to 30.1) but were not associated with increased news coverage, consistent with previous findings. Combining datasets from universities and journals (996 press releases, 1250 news), we found that when caveats appeared in press releases there was no reduction in journalistic uptake, but there was a clear increase in caveats in news (odds ratios 9.6 and 9.5 for caveats for advice and causal claims, CIs 4.1 to 24.3 and 6.0 to 15.2). The main study limitation is its retrospective correlational nature., Conclusions: For health and science news directly inspired by press releases, the main source of both exaggerations and caveats appears to be the press release itself. However we find no evidence that exaggerations increase, or caveats decrease, the likelihood of news coverage. These findings should be encouraging for press officers and scientists who wish to minimise exaggeration and include caveats in their press releases., Competing Interests: The authors have declared that no competing interests exist.

Interactions between countries originate from diverse aspects such as geographic proximity, trade, socio-cultural habits, language, religions, etc. Geopolitics studies the influence of a country’s geographic space on its political power and its relationships with other countries. This work reveals the potential of Wikipedia mining for geopolitical study. Actually, Wikipedia offers solid knowledge and strong correlations among countries by linking web pages together for different types of information (e.g. economical, historical, political, and many others). The major finding of this paper is to show that meaningful results on the influence of country ties can be extracted from the hyperlinked structure of Wikipedia. We leverage a novel stochastic matrix representation of Markov chains of complex directed networks called the reduced Google matrix theory. For a selected small size set of nodes, the reduced Google matrix concentrates direct and indirect links of the million-node sized Wikipedia network into a small Perron-Frobenius matrix keeping the PageRank probabilities of the global Wikipedia network. We perform a novel sensitivity analysis that leverages this reduced Google matrix to characterize the influence of relationships between countries from the global network. We apply this analysis to two chosen sets of countries (i.e. the set of 27 European Union countries and a set of 40 top worldwide countries). We show that with our sensitivity analysis we can exhibit easily very meaningful information on geopolitics from five different Wikipedia editions (English, Arabic, Russian, French and German). [ABSTRACT FROM AUTHOR]

Deep learning techniques, e.g., Convolutional Neural Networks (CNNs), have been explosively applied to the research in the fields of information retrieval and natural language processing. However, few research efforts have addressed semantic indexing with deep learning. The use of semantic indexing in the biomedical literature has been limited for several reasons. For instance, MEDLINE citations contain a large number of semantic labels from automatically annotated MeSH terms, and for a great deal of the literature, only the information of the title and the abstract is readily available. In this paper, we propose a Boltzmann Convolutional neural network framework (B-CNN) for biomedicine semantic indexing. In our hybrid learning framework, the CNN can adaptively deal with features of documents that have sequence relationships, and can capture context information accordingly; the Deep Boltzmann Machine (DBM) merges global (the entity in each document) and local information through its training with undirected connections. Additionally, we have designed a hierarchical coarse to fine style indexing structure for learning and classifying documents, and a novel feature extension approach with word sequence embedding and Wikipedia categorization. Comparative experiments were conducted for semantic indexing of biomedical abstract documents; these experiments verified the encouraged performance of our B-CNN model. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies. [ABSTRACT FROM AUTHOR]

The existence of a shared classification system is essential to knowledge production, transfer, and sharing. Studies of knowledge classification, however, rarely consider the fact that knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. This neglect is problematic whenever information about categorical membership is itself used to evaluate the quality of the items that the category contains. The main objective of this paper is to show that the effects of category membership depend on the position that a category occupies in the hierarchical knowledge classification system of Wikipedia—an open knowledge production and sharing platform taking the form of a freely accessible on-line encyclopedia. Using data on all English-language Wikipedia articles, we examine how the position that a category occupies in the classification hierarchy affects the attention that articles in that category attract from Wikipedia editors, and their evaluation of quality of the Wikipedia articles. Specifically, we show that Wikipedia articles assigned to coarse-grained categories (i. e., categories that occupy higher positions in the hierarchical knowledge classification system) garner more attention from Wikipedia editors (i. e., attract a higher volume of text editing activity), but receive lower evaluations (i. e., they are considered to be of lower quality). The negative relation between attention and quality implied by this result is consistent with current theories of social categorization, but it also goes beyond available results by showing that the effects of categorization on evaluation depend on the position that a category occupies in a hierarchical knowledge classification system. [ABSTRACT FROM AUTHOR]

The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf’s law and Heaps’ law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf’s law and Heaps’ law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks. [ABSTRACT FROM AUTHOR]

This paper demonstrates a framework of processes for identifying potential witnesses of events from evidence they post to social media. The research defines original evidence models for micro-blog content sources, the relative uncertainty of different evidence types, and models for testing evidence by combination. Methods to filter and extract evidence using automated and semi-automated means are demonstrated using a Twitter case study event. Further, an implementation to test extracted evidence using Dempster Shafer Theory of Evidence are presented. The results indicate that the inclusion of evidence from micro-blog text and linked image content can increase the number of micro-bloggers identified at events, in comparison to the number of micro-bloggers identified from geotags alone. Additionally, the number of micro-bloggers that can be tested for evidence corroboration or conflict, is increased by incorporating evidence identified in their posting history. [ABSTRACT FROM AUTHOR]

Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading. [ABSTRACT FROM AUTHOR]

A broadcast encryption scheme with personalized messages (BEPM) is a scheme in which a broadcaster transmits not only encrypted broadcast messages to a subset of recipients but also encrypted personalized messages to each user individually. Several broadcast encryption (BE) schemes allow a broadcaster encrypts a message for a subset S of recipients with public keys and any user in S can decrypt the message with his/her private key. However, these BE schemes can not provide an efficient way to transmit encrypted personalized messages to each user individually. In this paper, we propose a broadcast encryption scheme with a transmission of personalized messages. Besides, the scheme is based on multilinear maps ensure constant ciphertext size and private key size of each user and the scheme can achieve statically security. More realistically, the scheme can be applied to the Conditional Access System (CAS) of pay television (pay-TV) efficiently and safely.

Over the history of mankind, textual records change. Sometimes due to mistakes during transcription, sometimes on purpose, as a way to rewrite facts and reinterpret history. There are several classical cases, such as the logarithmic tables, and the transmission of antique and medieval scholarship. Today, text documents are largely edited and redistributed on the Web. Articles on news portals and collaborative platforms (such as Wikipedia), source code, posts on social networks, and even scientific publications or literary works are some examples in which textual content can be subject to changes in an evolutionary process. In this scenario, given a set of near-duplicate documents, it is worthwhile to find which one is the original and the history of changes that created the whole set. Such functionality would have immediate applications on news tracking services, detection of plagiarism, textual criticism, and copyright enforcement, for instance. However, this is not an easy task, as textual features pointing to the documents’ evolutionary direction may not be evident and are often dataset dependent. Moreover, side information, such as time stamps, are neither always available nor reliable. In this paper, we propose a framework for reliably reconstructing text phylogeny trees, and seamlessly exploring new approaches on a wide range of scenarios of text reusage. We employ and evaluate distinct combinations of dissimilarity measures and reconstruction strategies within the proposed framework, and evaluate each approach with extensive experiments, including a set of artificial near-duplicate documents with known phylogeny, and from documents collected from Wikipedia, whose modifications were made by Internet users. We also present results from qualitative experiments in two different applications: text plagiarism and reconstruction of evolutionary trees for manuscripts (stemmatology). [ABSTRACT FROM AUTHOR]

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds. [ABSTRACT FROM AUTHOR]

Media, and particularly TV media, have a great impact on the general public. In recent years, spatial patterns of information and the relevance of intangible geographies have become increasingly important. Gatekeeping plays a critical role in the selection of information that is transformed into media. Therefore, gatekeeping, through national media, also co-forms the generation of mental maps. In this paper, correspondence analysis (a statistical method) combined with cloud lines (a new visual analytics technique) is used to analyze how individual major regional events in one of the post-communist countries, the Czech Republic, penetrate into the media on a national scale. Although national news should minimize distortions about regions, this assumption has not been verified by our research. Impressions presented by the media of selected regions that were markedly influenced by one or several events in those regions demonstrate that gatekeepers, especially news reporters, functioned as a filter by selecting only a few specific, and in many cases, unusual events for dissemination. [ABSTRACT FROM AUTHOR]

The media have a key role in communicating advances in medicine to the general public, yet the accuracy of medical journalism is an under-researched area. This project adapted an established monitoring instrument to analyse all identified news reports (n = 312) on a single medical research paper: a meta-analysis published in the British Journal of Cancer which showed a modest link between processed meat consumption and pancreatic cancer. Our most significant finding was that three sources (the journal press release, a story on the BBC News website and a story appearing on the 'NHS Choices' website) appeared to account for the content of over 85% of the news stories which covered the meta analysis, with many of them being verbatim or moderately edited copies and most not citing their source. The quality of these 3 primary sources varied from excellent (NHS Choices, 10 of 11 criteria addressed) to weak (journal press release, 5 of 11 criteria addressed), and this variance was reflected in the accuracy of stories derived from them. Some of the methods used in the original meta-analysis, and a proposed mechanistic explanation for the findings, were challenged in a subsequent commentary also published in the British Journal of Cancer, but this discourse was poorly reflected in the media coverage of the story.

Previous researches support that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Moreover, literature shows that different types of graphical information can help or harm the accuracy on decision making of accountants and financial analysts. We conducted a 4×2 mixed-design experiment to examine the effects of numerical information disclosure on financial analysts’ accuracy, and investigated the role of overconfidence in decision making. Results show that compared to text, column graph enhanced accuracy on decision making, followed by line graphs. No difference was found between table and textual disclosure. Overconfidence harmed accuracy, and both genders behaved overconfidently. Additionally, the type of disclosure (text, table, line graph and column graph) did not affect the overconfidence of individuals, providing evidence that overconfidence is a personal trait. This study makes three contributions. First, it provides evidence from a larger sample size (295) of financial analysts instead of a smaller sample size of students that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Second, it uses the text as a baseline comparison to test how different ways of information disclosure (line and column graphs, and tables) can enhance understandability of information. Third, it brings an internal factor to this process: overconfidence, a personal trait that harms the decision-making process of individuals. At the end of this paper several research paths are highlighted to further study the effect of internal factors (personal traits) on financial analysts’ accuracy on decision making regarding numerical information presented in a graphical form. In addition, we offer suggestions concerning some practical implications for professional accountants, auditors, financial analysts and standard setters. [ABSTRACT FROM AUTHOR]

In a connected world where people influence each other, what can cause a globalized monoculture, and which measures help to preserve the coexistence of cultures? Previous research has shown that factors such as homophily, population size, geography, mass media, and type of social influence play important roles. In the present paper, we investigate for the first time the impact that institutions have on cultural diversity. In our first three studies, we extend existing agent-based models and explore the effects of institutional influence and agent loyalty. We find that higher institutional influence increases cultural diversity, while individuals' loyalty to their institutions has a small, preserving effect. In three further studies, we test how bottom-up and top-down processes of institutional influence impact our model. We find that bottom-up democratic practices, such as referenda, tend to produce convergence towards homogeneity, while top-down information dissemination practices, such as propaganda, further increase diversity. In our last model—an integration of bottom-up and top-down processes into a feedback loop of information—we find that when democratic processes are rare, the effects of propaganda are amplified, i.e., more diversity emerges; however, when democratic processes are common, they are able to neutralize or reverse this propaganda effect. Importantly, our models allow for control over the full spectrum of diversity, so that a manipulation of our parameters can result in preferred levels of diversity, which will be useful for the study of other factors in the future. We discuss possible mechanisms behind our results, applications, and implications for political and social sciences. [ABSTRACT FROM AUTHOR]

Place can be generally defined as a location that has been assigned meaning through human experience, and as such it is of multidisciplinary scientific interest. Up to this point place has been studied primarily within the context of social sciences as a theoretical construct. The availability of large amounts of user-generated content, e.g. in the form of social media feeds or Wikipedia contributions, allows us for the first time to computationally analyze and quantify the shared meaning of place. By aggregating references to human activities within urban spaces we can observe the emergence of unique themes that characterize different locations, thus identifying places through their discernible sociocultural signatures. In this paper we present results from a novel quantitative approach to derive such sociocultural signatures from Twitter contributions and also from corresponding Wikipedia entries. By contrasting the two we show how particular thematic characteristics of places (referred to herein as platial themes) are emerging from such crowd-contributed content, allowing us to observe the meaning that the general public, either individually or collectively, is assigning to specific locations. Our approach leverages probabilistic topic modelling, semantic association, and spatial clustering to find locations are conveying a collective sense of place. Deriving and quantifying such meaning allows us to observe how people transform a location to a place and shape its characteristics. [ABSTRACT FROM AUTHOR]

This paper uses original survey data of the Great East Japan earthquake disaster victims to examine their decision to apply for the temporary housing as well as the timing of application. We assess the effects of victims’ attachment to their locality as well as variation in victims’ information seeking behavior. We additionally consider various factors such as income, age, employment and family structure that are generally considered to affect the decision to choose temporary housing as victims’ solution for their displacement. Empirical results indicate that, ceteris paribus, as the degree of attachment increases, victims are more likely to apply for the temporary housing but attachment does not affect the timing of application. On the other hand, the victims who actively seek information and are able to collect higher quality information are less likely to apply for the temporary housing and if they do apply then they apply relatively later. [ABSTRACT FROM AUTHOR]

Twitter is an extremely high volume platform for user generated contributions regarding any topic. The wealth of content created at real-time in massive quantities calls for automated approaches to identify the topics of the contributions. Such topics can be utilized in numerous ways, such as public opinion mining, marketing, entertainment, and disaster management. Towards this end, approaches to relate single or partial posts to knowledge base items have been proposed. However, in microblogging systems like Twitter, topics emerge from the culmination of a large number of contributions. Therefore, identifying topics based on collections of posts, where individual posts contribute to some aspect of the greater topic is necessary. Models, such as Latent Dirichlet Allocation (LDA), propose algorithms for relating collections of posts to sets of keywords that represent underlying topics. In these approaches, figuring out what the specific topic(s) the keyword sets represent remains as a separate task. Another issue in topic detection is the scope, which is often limited to specific domain, such as health. This work proposes an approach for identifying domain-independent specific topics related to sets of posts. In this approach, individual posts are processed and then aggregated to identify key tokens, which are then mapped to specific topics. Wikipedia article titles are selected to represent topics, since they are up to date, user-generated, sophisticated articles that span topics of human interest. This paper describes the proposed approach, a prototype implementation, and a case study based on data gathered during the heavily contributed periods corresponding to the four US election debates in 2012. The manually evaluated results (0.96 precision) and other observations from the study are discussed in detail. [ABSTRACT FROM AUTHOR]

As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim. [ABSTRACT FROM AUTHOR]

This paper presents data from the Program for the International Assessment of Adult Competencies with a focus on the interrelationships among health information seeking behavior (HISB), and health status or use of preventive health measures for U.S. adults both with and without a high school diploma. Key results of ordinal and binary logistic regression analyses indicated that, after controlling for demographic factors, (1) adults with a high school diploma use more text-based health information sources while adults without a high school diploma use more oral sources, (2) using the Internet as a source of health information is more strongly related to reporting excellent/very good health status than having a high school diploma, (3) those without a high school diploma who use the Internet report the largest increase in health status over any other health information source, and (4) for those with learning disability or vision problem, a high facility in reading English is an important predictor of whether the Internet is used as a health information source. The Internet appears to play a key role in both enhancing health status and enabling use of preventive measures for those with and without a high school diploma; although, individuals without a high school diploma who use the Internet for health information derive substantial benefit in health status. [ABSTRACT FROM AUTHOR]

Graphlets are small subgraphs, usually containing up to five vertices, that can be found in a larger graph. Identification of the graphlets that a vertex in an explored graph touches can provide useful information about the local structure of the graph around that vertex. Actually finding all graphlets in a large graph can be time-consuming, however. As the graphlets grow in size, more different graphlets emerge and the time needed to find each graphlet also scales up. If it is not needed to find each instance of each graphlet, but knowing the number of graphlets touching each node of the graph suffices, the problem is less hard. Previous research shows a way to simplify counting the graphlets: instead of looking for the graphlets needed, smaller graphlets are searched, as well as the number of common neighbors of vertices. Solving a system of equations then gives the number of times a vertex is part of each graphlet of the desired size. However, until now, equations only exist to count graphlets with 4 or 5 nodes. In this paper, two new techniques are presented. The first allows to generate the equations needed in an automatic way. This eliminates the tedious work needed to do so manually each time an extra node is added to the graphlets. The technique is independent on the number of nodes in the graphlets and can thus be used to count larger graphlets than previously possible. The second technique gives all graphlets a unique ordering which is easily extended to name graphlets of any size. Both techniques were used to generate equations to count graphlets with 4, 5 and 6 vertices, which extends all previous results. Code can be found at and . [ABSTRACT FROM AUTHOR]

Online social media has recently irrupted as the last major venue for the propagation of news and cultural content, competing with traditional mass media and allowing citizens to access new sources of information. In this paper, we study collectively filtered news and popular content in Twitter, known as Trending Topics (TTs), to quantify the extent to which they show similar biases known for mass media. We use two datasets collected in 2013 and 2014, including more than 300.000 TTs from 62 countries. The existing patterns of leader-follower relationships among countries reveal systemic biases known for mass media: Countries concentrate their attention to small groups of other countries, generating a pattern of centralization in which TTs follow the gradient of wealth across countries. At the same time, we find subjective biases within language communities linked to the cultural similarity of countries, in which countries with closer cultures and shared languages tend to follow each other’s TTs. Moreover, using a novel methodology based on the Google News service, we study the influence of mass media in TTs for four countries. We find that roughly half of the TTs in Twitter overlap with news reported by mass media, and that the rest of TTs are more likely to spread internationally within Twitter. Our results confirm that online social media have the power to independently spread content beyond mass media, but at the same time social media content follows economic incentives and is subject to cultural factors and language barriers. [ABSTRACT FROM AUTHOR]

Subtle gender dynamics in the publishing process involving collaboration, peer-review, readership, citation, and media coverage disadvantage women in academia. In this study we consider whether commenting on published work is also gendered. Using all the comments published over a 16-year period in PNAS (N = 869) and Science (N = 481), we find that there is a gender gap in the authorship of comment letters: women are less likely than men to comment on published academic research. This disparity is greater than gender differences in the publication of research articles. There is also a gendered pattern in commenting: women comment writers are relatively less likely to engage with men's research. If left unaddressed, these patterns in academic commenting could impede scholarly exchange between men and women and further marginalize women within the scientific community. [ABSTRACT FROM AUTHOR]

Several uncorroborated, false, or misinterpreted conceptions have for years been widely distributed in academic publications, thus becoming scientific myths. How can such misconceptions persist and proliferate within the inimical environment of academic criticism? Examining 613 articles we demonstrate that the reception of three myth-exposing publications is skewed by an ‘affirmative citation bias’: The vast majority of articles citing the critical article will affirm the idea criticized. 468 affirmed the myth, 105 were neutral, while 40 took a negative stance. Once misconceptions proliferate wide and long enough, criticizing them not only becomes increasingly difficult, efforts may even contribute to the continued spreading of the myths. [ABSTRACT FROM AUTHOR]