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Introduction: Endometriosis has a significant cost of illness burden in Europe, UK and the USA, with the majority of costs coming from reductions in productivity. However, information is scarce on if there is a differing impact between endometriosis and other causes of chronic pelvic pain, and if there are modifiable factors, such as pain severity, that may be significant contributors to the overall burden. Methods: An online survey was hosted by SurveyMonkey and the link was active between February to April 2017. Women aged 18–45, currently living in Australia, who had either a confirmed diagnosis of endometriosis via laparoscopy or chronic pelvic pain without a diagnosis of endometriosis were included. The retrospective component of the WERF EndoCost tool was used to determine direct healthcare costs, direct non-healthcare costs (carers) and indirect costs due to productivity loss. Estimates were extrapolated to the Australian population using published prevalence estimates. Results: 407 valid responses were received. The cost of illness burden was significant in women with chronic pelvic pain (Int $16,970 to $ 20,898 per woman per year) irrespective of whether they had a diagnosis of endometriosis. The majority of costs (75–84%) were due to productivity loss. Both absolute and relative productivity costs in Australia were higher than previous estimates based on data from Europe, UK and USA. Pain scores showed the strongest relationship to productivity costs, a 12.5-fold increase in costs between minimal to severe pain. The total economic burden per year in Australia in the reproductive aged population (at 10% prevalence) was 6.50 billion Int $. Conclusion: Similar to studies in European, British and American populations, productivity costs are the greatest contributor to overall costs. Given pain is the most significant contributor, priority should be given to improving pain control in women with pelvic pain [ABSTRACT FROM AUTHOR]

Background: Many countries have initiated school-based human papillomavirus (HPV) vaccination programs. The real-life effectiveness of HPV vaccines has become increasingly evident, especially among girls vaccinated before HPV exposure in countries with high vaccine uptake. In 2009, Norway initiated a school-based HPV vaccination program for 12-year-old girls using the quadrivalent HPV vaccine (Gardasil®), which targets HPV6, 11, 16, and 18. Here, we aim to assess type-specific vaginal and oral HPV prevalence in vaccinated compared with unvaccinated girls in the first birth cohort eligible for school-based vaccination (born in 1997). Methods: This observational, cross-sectional study measured the HPV prevalence ratio (PR) between vaccinated and unvaccinated girls in Norway. Facebook advertisement was used to recruit participants and disseminate information about the study. Participants self-sampled vaginal and oral specimens using an Evalyn® Brush and a FLOQSwab™, respectively. Sexual behavior was ascertained through a short questionnaire. Results: Among the 312 participants, 239 (76.6%) had received at least one dose of HPV vaccine prior to sexual debut. 39.1% of vaginal samples were positive for any HPV type, with similar prevalence among vaccinated and unvaccinated girls (38.5% vs 41.1%, PR: 0.93, 95% confidence interval [CI]: 0.62–1.41). For vaccine-targeted types there was some evidence of lower prevalence in the vaccinated (0.4%) compared to the unvaccinated (6.8%) group (PR: 0.06, 95%CI: 0.01–0.52). This difference remained after adjusting for sexual behavior (PR: 0.04, 95%CI: 0.00–0.42). Only four oral samples were positive for any HPV type, and all of these participants had received at least one dose of HPV vaccine at least 1 year before oral sexual debut. Conclusion: There is evidence of a lower prevalence of vaccine-targeted HPV types in the vagina of vaccinated girls from the first birth cohort eligible for school-based HPV vaccination in Norway; this was not the case when considering all HPV types or types not included in the quadrivalent HPV vaccine. [ABSTRACT FROM AUTHOR]

People with schizophrenia report positive emotion during social interactions in ecological momentary assessment (EMA) studies; however, few of these studies examine the qualities of social interactions (e.g., intimacy) that may affect emotion experience. In the current EMA study, people with (n = 20) and without schizophrenia (n = 15) answered questions about the quality of their social interactions, including their emotion experiences. We also explored the relationship between EMA-reported social experiences and trait loneliness, negative symptoms, and social functioning. People with and without schizophrenia did not differ in EMA-reported proportion of time spent with others, extent of involvement during social interactions, intimacy of interactions, or average number of social interactions. Both people with and without schizophrenia reported more positive than negative emotion during social experiences. However, people with schizophrenia reported more loneliness, more severe negative symptoms, and impaired social functioning compared to people without schizophrenia. Further, specific qualities of social interactions (intimacy of interaction, involvement during interaction) were related to happiness during interactions only in people without schizophrenia. These results suggest that while people with and without schizophrenia report similar rates of in-the-moment social emotion experiences, the impact of social interaction quality on emotion may differ between groups. [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]

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]

Objective: To conduct a time-cost analysis of formatting in scientific publishing. Design: International, cross-sectional study (one-time survey). Setting: Internet-based self-report survey, live between September 2018 and January 2019. Participants: Anyone working in research, science, or academia and who submitted at least one peer-reviewed manuscript for consideration for publication in 2017. Completed surveys were available for 372 participants from 41 countries (60% of respondents were from Canada). Main outcome measure: Time (hours) and cost (wage per hour x time) associated with formatting a research paper for publication in a peer-reviewed academic journal. Results: The median annual income category was US$61,000–80,999, and the median number of publications formatted per year was four. Manuscripts required a median of two attempts before they were accepted for publication. The median formatting time was 14 hours per manuscript, or 52 hours per person, per year. This resulted in a median calculated cost of US$477 per manuscript or US$1,908 per person, per year. Conclusions: To our knowledge, this is the first study to analyze the cost of manuscript formatting in scientific publishing. Our results suggest that scientific formatting represents a loss of 52 hours, costing the equivalent of US$1,908 per researcher per year. These results identify the hidden and pernicious price associated with scientific publishing and provide evidence to advocate for the elimination of strict formatting guidelines, at least prior to acceptance. [ABSTRACT FROM AUTHOR]

Twitter was an integral part of Donald Trump’s communication platform during his 2016 campaign. Although its topical content has been examined by researchers and the media, we know relatively little about the style of the language used on the account or how this style changed over time. In this study, we present the first detailed description of stylistic variation on the Trump Twitter account based on a multivariate analysis of grammatical co-occurrence patterns in tweets posted between 2009 and 2018. We identify four general patterns of stylistic variation, which we interpret as representing the degree of conversational, campaigning, engaged, and advisory discourse. We then track how the use of these four styles changed over time, focusing on the period around the campaign, showing that the style of tweets shifts systematically depending on the communicative goals of Trump and his team. Based on these results, we propose a series of hypotheses about how the Trump campaign used social media during the 2016 elections. [ABSTRACT FROM AUTHOR]

Background: Technological interventions may help support and improve mental health. However young peoples’ perspectives on using different technologies to detect deteriorating mental health in those already diagnosed with a mental health condition is lacking. The study aim was to explore the perspectives of young patients on the feasibility and acceptability of using wearables, social media and technologies to detect mental health deterioration. Methods: The study was co-produced with young adults with past mental health difficulties. Semi-structured interviews were conducted with young adults with a severe mental health condition in a private room at a community mental health site. Data was triangulated by comparing codes and ideas across the two co-researchers and two researchers over two virtual meetings. Themes were finalised and presented in a thematic map. Results: Sixteen participants were interviewed (81% female). There were four main themes: dealing with mental health symptoms, signs of mental health deterioration, technology concerns and technological applications to identify worsening mental health. Wearables and mobile apps were considered acceptable and feasible to detect mental health deterioration in real-time if they could measure changes in sleep patterns, mood or activity levels as signs of deterioration. Getting help earlier was deemed essential particularly in reference to dissatisfaction with the current non-technological mental health services. However, patients identified issues to consider before implementation including practicality, safeguarding and patient preference. Conclusion: Wearables and mobile apps could be viable technological options to help detect deterioration in young people in order to intervene early and avoid delay in accessing mental health services. However, immediate action following detection is required for the patient to trust and use the intervention. [ABSTRACT FROM AUTHOR]

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]

Background: Social media has become increasingly important for communication among young people. It is also often used to communicate suicidal ideation. Aims: To investigate the link between acute suicidality and language use as well as activity on Instagram. Method: A total of 52 participants, aged on average around 16 years, who had posted pictures of non-suicidal self-injury on Instagram, and reported a lifetime history of suicidal ideation, were interviewed using Instagram messenger. Of those participants, 45.5% reported suicidal ideation on the day of the interview (acute suicidal ideation). Qualitative text analysis (software ATLAS.ti 7) was used to investigate experiences with expressions of active suicidal thoughts on Instagram. Quantitative text analysis of language use in the interviews and directly on Instagram (in picture captions) was performed using the Linguistic Inquiry and Word Count software. Language markers in the interviews and in picture captions, as well as activity on Instagram were added to regression analyses, in order to investigate predictors for current suicidal ideation. Results: Most participants (80%) had come across expressions of active suicidal thoughts on Instagram and 25% had expressed active suicidal thoughts themselves. Participants with acute suicidal ideation used significantly more negative emotion words (Cohen’s d = 0.66, 95% CI: 0.088–1.232) and words expressing overall affect (Cohen’s d = 0.57, 95% CI: 0.001–1.138) in interviews. However, activity and language use on Instagram did not predict acute suicidality. Conclusions: While participants differed with regard to their use of language in interviews, differences in activity and language use on Instagram were not associated with acute suicidality. Other mechanisms of machine learning, like identifying picture content, might be more valuable. [ABSTRACT FROM AUTHOR]

Identification of the most influential spreaders that maximize information propagation in social networks is a classic optimization problem, called the influence maximization (IM) problem. A reasonable diffusion model that can accurately simulate information propagation in social networks is the key step to efficiently solving the IM problem. Synergism of neighbor nodes plays an important role in information propagation dynamics. Some known diffusion models have considered the reinforcement mechanism in defining the activation threshold. Most of these models focus on the synergetic effects of nodes on their common neighbors, but the accumulation of synergism has been neglected in previous studies. Inspired by these facts, we first discuss the catalytic role of synergism in the spreading dynamics of social networks and then propose a novel diffusion model called the synergism-based three-step cascade model (TSSCM) based on the above analysis and the three-degree influence theory. Finally, we devise an algorithm for solving the IM problem based on the TSSCM. Experiments on five real large-scale social networks demonstrate the efficacy of our method, which achieves competitive results in terms of influence spreading compared to the four other algorithms tested. [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]

Trust is praised by many social scientists as the foundation of functioning social systems owing to its assumed connection to cooperative behavior. The existence of such a link is still subject to debate. In the present study, we first highlight important conceptual issues within this debate. Second, we examine previous evidence, highlighting several issues. Third, we present findings from an original experiment, in which we tried to identify a “real” situation that allowed us to measure both trust and cooperation. People’s expectations and behavior when they decide to share (or not) their data represents such a situation, and we make use of corresponding data. We found that there is no relationship between trust and cooperation. This non-relationship may be rationalized in different ways which, in turn, provides important lessons for the study of the trust—behavior nexus beyond the particular situation we study empirically. [ABSTRACT FROM AUTHOR]

As online content continues to grow, so does the spread of hate speech. We identify and examine challenges faced by online automatic approaches for hate speech detection in text. Among these difficulties are subtleties in language, differing definitions on what constitutes hate speech, and limitations of data availability for training and testing of these systems. Furthermore, many recent approaches suffer from an interpretability problem—that is, it can be difficult to understand why the systems make the decisions that they do. We propose a multi-view SVM approach that achieves near state-of-the-art performance, while being simpler and producing more easily interpretable decisions than neural methods. We also discuss both technical and practical challenges that remain for this task. [ABSTRACT FROM AUTHOR]

Commercially motivated junk news–i.e. money-driven, highly shareable clickbait with low journalistic production standards–constitutes a vast and largely unexplored news media ecosystem. Using publicly available Facebook data, we compared the reach of junk news on Facebook pages in the Netherlands to the reach of Dutch mainstream news on Facebook. During the period 2013–2017 the total number of user interactions with junk news significantly exceeded that with mainstream news. Over 5 Million of the 10 Million Dutch Facebook users have interacted with a junk news post at least once. Junk news Facebook pages also had a significantly stronger increase in the number of user interactions over time than mainstream news. Since the beginning of 2016 the average number of user interactions per junk news post has consistently exceeded the average number of user interactions per mainstream news post. [ABSTRACT FROM AUTHOR]

The field of paleontology, which is based principally on observations of the natural world, includes an active community that is engaged across multiple social media platforms, consisting of museums, academic researchers, amateur fossil collectors, paleontological artists, and commercial fossil dealers. As such, it represents an ideal environment for examining the people, interactions, and flow of scientific information. Using interactions involving the four most popular Twitter hashtags for paleontology, this embedded mixed methods study defined the members of this social world and investigated how they influenced and controlled the flow of information, as well as how their expression of scientific practice was related to their identity. Results provide further evidence for the diversity of people and practice involved in this domain of science and indicate that the magnitude and breadth of the public’s impact may be larger than previously projected. Certain types of messages were shown to be effective for different segments of the community, but news posts, essentially media outlet stories, were ineffective for generating any form of engagement. This study adds to our understanding of the important scientific contribution being made by members of the public as they interact with professional scientists and educators as peers in an open social media platform that supports a diverse and active community. [ABSTRACT FROM AUTHOR]

Twitter has become an important platform for geo-spatial analyses, providing high-volume spatial data on a wide variety of social processes. Understanding the relationship between population density and Twitter activity is therefore of key importance. This study reports a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power law functions with exponents greater than one. These relations are consistent with each other and hold across a range of spatial scales. This implies that population density can accurately predict Twitter activity, but importantly, it also implies that correct predictions are not given by a naive linear scaling analysis. The observed super-linearity has implications for any spatial analyses performed with Twitter data and is important for understanding the relationship between Twitter use and demographics. For example, the robustness of this relationship means that we can identify ‘anomalous’ geographic areas that deviate from the observed trend, identifying several towns with high/low usage relative to expectation; using the scaling relationship we are able to show that these anomalies are not caused by age structure, as has been previously proposed. Proper consideration of this scaling relationship will improve robustness in future geo-spatial studies using Twitter. [ABSTRACT FROM AUTHOR]

Background: In 2016 the first African emergency care clinical practice guideline (CPG) was developed for national uptake in the prehospital sector in South Africa, with implementation starting in 2018. Comprehensive uptake of CPGs post development is not a given, as this requires effective and efficient dissemination and implementation strategies that take into account the perceptions, barriers and facilitators of the local end-users. This study aimed to identify prehospital end-users’ perceptions of the emergency care guidelines, including barriers and facilitators for national decision makers, to strengthen CPG uptake in South Africa. Methods: Our study employed a descriptive qualitative research design, including nine focus groups with 56 operational emergency care providers across four major provinces in South Africa. Data was analysed using thematic analysis in ATLAS.ti. Ethics approval was provided by Stellenbosch University. Results: Themes related to provider perceptions, expectations and guideline uptake emerging from the data was unofficial and unclear communication, broadening versus limiting guideline expectations, conflicted personal reactions and spreading the word. Challenges to dissemination and implementation included poor communication, changes to scope of practice, and limited capacity to upskill existing providers. Facilitators included using technology for end-user documents, local champions to support change, establishing online and modular training, and implementation by independent decision makers. Conclusion: This study provides an overview of the perceptions of operational emergency care providers and how their experiences of hearing about and engaging with the guidelines, in their industry, can contribute to the dissemination, implementation and uptake of emergency care guidelines. In order to disseminate and implement an emergency care CPG, decision makers must take into account the perceptions, barriers, and facilitators of local end-users. [ABSTRACT FROM AUTHOR]

Background: Community pharmacists are available to counsel women in early pregnancy, but no studies have assessed the feasibility of such a service. Objective: To test the feasibility of a pharmacist consultation in early pregnancy and to inform the design of a definitive trial. Setting: Six community pharmacies in Norway from Oct. to Dec. 2017. Method: We evaluated recruitment approaches and an automatic data preprocessing system (ADPS) to enroll, assign participants, and distribute questionnaires. Women (≥18 years) in early pregnancy were eligible for inclusion. Participants were assigned to a pharmacist consultation (intervention group) or standard care (control group). The intervention aimed to address each woman’s concerns and needs regarding medications and ailments in pregnancy, and was documented on a standard form. The women’s acceptability of the intervention was measured by a questionnaire. Main outcome measures: Appropriate recruitment approaches, workflow of the ADPS, and women’s acceptability of the intervention. Results: Of the 35 participants recruited, 19 were recruited through Facebook. The ADPS worked well. Treatment of nausea and vomiting (NVP) (10/11) and general information about medications (8/11) were frequently discussed during the consultations (n = 11). The women reported high satisfaction with the consultation. Having the option of telephone and follow-up consultations was important to the women. Conclusion: It is feasible to provide community pharmacist consultations in early pregnancy. In a definitive study, the consultations should focus on NVP and general medication use and further explore social media as a recruiting tool. Both in-pharmacy and telephone consultations should be offered to deliver the intervention. [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]

Earlier work by one of us examined a historical corpus of portraits and found that artists often paint the subject such that one eye is centred horizontally. If due to psychological mechanisms constraining artistic composition, this eye-centring bias should be detectable also in portraits by non-professionals. However, this finding has been questioned both on theoretical and empirical grounds. Here we tested eye-centring in a larger (N ~ = 4000) and more representative set of selfies spontaneously posted on Instagram from six world cities. In contrast with previous selfie results, the distribution of the most-centred eye position peaked almost exactly at the horizontal centre of the image and was statistically different from predictions based on realistic Monte-Carlo predictions. In addition, we observed a small but statistically reliable pseudoneglect effect as well as a preference for centring the left-eye. An eye-centring tendency appears to exist in self-portraits by non-artists. [ABSTRACT FROM AUTHOR]

Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010–2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures. [ABSTRACT FROM AUTHOR]

Two dimensions of temperament, namely, (high levels of) novelty seeking and (low levels of) harm avoidance are related to substance addictions. However, their implications for smartphone addiction remain unexplored. Medical students are heavy smartphone users. Accordingly, screening for the risk of smartphone addiction based on individual differences in temperament can facilitate the identification of the best possible prevention strategy. Therefore, the present study aimed to examine the relationship between temperament and the vulnerability to smartphone addiction among medical students in Jakarta, Indonesia. The research study adopted a cross-sectional research design and used a simple random sampling technique. The Indonesian versions of the Temperament and Character Inventory and the Smartphone Addiction Scale were used to measure the study variables. Logistic regression analysis was conducted to examine the relationships between demographic factors, patterns of smartphone use, temperament, and vulnerability to smartphone addiction. A majority of the 185 participants were found to have the following temperament profile: low levels of novelty seeking and high levels of reward dependence and harm avoidance. The average duration of daily smartphone use was 7.83 hours (SD = 4.03) and the age at first smartphone use was 7.62 years (SD = 2.60). The respondents used smartphone to communicate with other people and access social media. A high level of harm avoidance was significantly associated with the risk of smartphone addiction (Odds Ratio [OR] = 2.04, 95% Confidence Interval [CI] = 1.12, 3.70). The findings suggest that smartphone addiction is comparable to other addictive behaviors. Further, harm avoidance increases the risk of smartphone addiction. Therefore, the risk of smartphone addiction among medical students must be ascertained based on their temperament profiles. [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]

Robust inventories are vital for improving assessment of and response to deadly and costly landslide hazards. However, collecting landslide events in inventories is difficult at the global scale due to inconsistencies in or the absence of landslide reporting. Citizen science is a valuable opportunity for addressing some of these challenges. The new Cooperative Open Online Landslide Repository (COOLR) supplements data in a NASA-developed Global Landslide Catalog (GLC) with citizen science reports to build a more robust, publicly available global inventory. This manuscript introduces the COOLR project and its methods, evaluates the initial citizen science results from the first 13 months, and discusses future improvements to increase the global engagement with the project. The COOLR project () contains Landslide Reporter, the first global citizen science project for landslides, and Landslide Viewer, a portal to visualize data from COOLR and other satellite and model products. From March 2018 to April 2019, 49 citizen scientists contributed 162 new landslide events to COOLR. These events spanned 37 countries in five continents. The initial results demonstrated that both expert and novice participants are contributing via Landslide Reporter. Citizen scientists are filling in data gaps through news sources in 11 different languages, in-person observations, and new landslide events occurring hundreds and thousands of kilometers away from any existing GLC data. The data is of sufficient accuracy to use in NASA susceptibility and hazard models. COOLR continues to expand as an open platform of landslide inventories with new data from citizen scientists, NASA scientists, and other landslide groups. Future work on the COOLR project will seek to increase participation and functionality of the platform as well as move towards collective post-disaster mapping. [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]

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients’ consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients’ Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions. [ABSTRACT FROM AUTHOR]

Social Networking Sites (SNS) such as Facebook and Instagram have relocated a large portion of people's social lives online, but can be intrusive and create social disturbances. Many people therefore consider taking an "SNS vacation." We investigated the effects of a one-week vacation from both Facebook and Instagram on subjective well-being, and whether this would vary for passive or active SNS users. Usage amount was measured objectively, using RescueTime software, to circumvent issues of self-report. Usage style was identified at pre-test, and SNS users with a more active or more passive usage style were assigned in equal numbers to the conditions of one-week SNS vacation (n = 40) or no SNS vacation (n = 38). Subjective well-being (life satisfaction, positive affect, and negative affect) was measured before and after the vacation period. At pre-test, more active SNS use was found to correlate positively with life satisfaction and positive affect, whereas more passive SNS use correlated positively with life satisfaction, but not positive affect. Surprisingly, at post-test the SNS vacation resulted in lower positive affect for active users and had no significant effects for passive users. This result is contrary to popular expectation, and indicates that SNS usage can be beneficial for active users. We suggest that SNS users should be educated in the benefits of an active usage style and that future research should consider the possibility of SNS addiction among more active users. [ABSTRACT FROM AUTHOR]

Adolescence is a developmental phase in which feelings of loneliness often increase. It is also a time period during which computer-mediated communication (CMC) is frequently used by youth to communicate with their peers. Strong family relationships protect youth from experiencing a wide range of adversities and mental health problems, including loneliness, and yet use of CMC to contact peers may leave adolescents feeling disconnected and lonely while also limiting the amount of time they spend with their family. This study examines the association between CMC and feelings of loneliness among Canadian youth, with family communication explored as an effect modifier. The study base was the Canadian 2013–2014 Health Behaviour in School-aged Children study used in a cross-sectional analysis (N = 30117; grades 6–10). Random-effects multilevel Poisson regression methods were used to quantify risks for adolescent loneliness among daily vs. non-daily users of verbal CMC (e.g., Skype, phone calls), text/instant messaging and social media CMC with friends. Effect modification was tested via the inclusion of modelled interaction terms. Family communication quality moderated the relationship between daily CMC use and loneliness among Canadian youth. Among youth experiencing high relative quality of family communication, daily use of verbal and social media CMC to contact friends was positively associated with reports of loneliness, compared to non-daily users. Findings suggest that family communication must remain central in societal discussions of youth loneliness, mental health and use of CMC. [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]

This work examines Twitter discussion surrounding the 2015 outbreak of Zika, a virus that is most often mild but has been associated with serious birth defects and neurological syndromes. We introduce and analyze a collection of 3.9 million tweets mentioning Zika geolocated to North and South America, where the virus is most prevalent. Using a multilingual topic model, we automatically identify and extract the key topics of discussion across the dataset in English, Spanish, and Portuguese. We examine the variation in Twitter activity across time and location, finding that rises in activity tend to follow to major events, and geographic rates of Zika-related discussion are moderately correlated with Zika incidence (ρ = .398). [ABSTRACT FROM AUTHOR]

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

The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive voting event can be used to build an accurate map of real-world political parties and electoral ranks for Italian elections in 2018. We provide evidence that information flow and collective attention are often driven by a special class of highly influential users, that we name “augmented humans”, who exploit thousands of automated agents, also known as bots, for enhancing their online influence. We show that augmented humans generate deep information cascades, to the same extent of news media and other broadcasters, while they uniformly infiltrate across the full range of identified groups. Digital augmentation represents the cyber-physical counterpart of the human desire to acquire power within social systems. [ABSTRACT FROM AUTHOR]

How do echo chambers operate? Why does social propagation of information become trapped within the boundaries of social groups? Previous studies of these questions have identified informational and structural factors which hinder information exchange across group boundaries; these factors constitute “chambers” in which information flows are confined and transformed into “echoes.” However, empirical evidence has indicated that these factors may not sufficiently explain the mechanism of echo chambers. Hence, the present study investigated whether the insular flow of information emerges and endures without the chambers. A randomized controlled experiment was conducted in which participants, who were classified into two political groups, exchanged randomly selected articles with the same number of ingroup and outgroup neighbors. The experiment manipulated the directionality of incoming information flow by varying the number of articles sent from ingroup neighbors across two conditions. Analyses revealed that the ingroup-slanted inflow induced ingroup-slanted outflow, suppressing transmission toward neighbors in a different social group. The biased inflow also promoted positive reactions to information exchanges and reduced negative evaluations on the exchanged information. Furthermore, the ingroup-slanted inflow increased false perceptions of ingroup majority, which is known to encourage information dissemination by a social group. The present study suggests two self-reinforcing mechanisms of ingroup-biased flows that generate echoes even without the chambers. These mechanisms may enable a small group of strategic actors to exacerbate polarization within a large population by manipulating directions of information flow. [ABSTRACT FROM AUTHOR]

Originally, online public engagement with science tended to be one directional—from experts to the general population via news media. Such an arrangement allowed for little to no direct interaction between the public and scientists. However, the emergence of social media has opened the door to meaningful engagement between scientists and the general public. The current study examines scientists’ perspectives on the interactions between laypeople and scientists by asking questions and sharing information on social media platforms, specifically, through Ask Me Anything (AMA) sessions on Reddit’s “Science” subreddit (r/science). By analyzing the content of six different r/science AMAs and surveying scientists who participated as r/science AMA hosts, our research attempts to gain a richer understanding of direct communication between scientists and lay audiences online. We had three main questions: (1) who are the participant scientists hosting r/science AMAs, (2) what are their experiences like as hosts, and (3) what type of discussions do they have on this platform? Survey results suggested that these scientists recognize the promising interactive nature of Reddit and are interested in continuing to use this platform as a tool for public engagement. Survey respondents generally had positive experiences as AMA hosts, but further research is needed to examine negative experiences. Overall, this study has significant implications for how scientists can engage public audiences online and more effectively communicate scientific findings to the general populace. [ABSTRACT FROM AUTHOR]

In an online Qualtrics panel survey experiment (N = 1620), we found that scientists posting self-portraits (“selfies”) to Instagram from the science lab/field were perceived as significantly warmer and more trustworthy, and no less competent, than scientists posting photos of only their work. Participants who viewed scientist selfies, especially posts containing the face of a female scientist, perceived scientists as significantly warmer than did participants who saw science-only images or control images. Participants who viewed selfies also perceived less symbolic threat from scientists. Most encouragingly, participants viewing selfies, either of male or female scientists, did not perceive scientists as any less competent than did participants viewing science-only or control images. Subjects who viewed female scientist selfies also perceived science as less exclusively male. Our findings suggest that self-portraiture by STEM professionals on social media can mitigate negative attitudes toward scientists. [ABSTRACT FROM AUTHOR]

Interest in public engagement with science activities has grown in recent decades, especially engagement through social media and among graduate students. Research on scientists’ views of engagement, particularly two-way engagement and engagement through social media, is sparse, particularly research examining graduate students’ views. We compare graduate students and faculty in biological and physical sciences at a land-grant, research-intensive university in their views on engagement. We find that both groups overwhelmingly believe that public input in decision-making around science issues is important, and hold largely pro-engagement attitudes. Graduate students, however, have somewhat more optimistic views of engagement through social media and on the appropriateness of discussing science controversy on social media. We discuss implications for graduate education and future engagement. [ABSTRACT FROM AUTHOR]

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

Crowdfunding is gradually becoming a modern marketing pattern. By noting that the success of crowdfunding depends on network externalities, our research aims to utilize these network externalities to provide an applicable referral mechanism in a crowdfunding-based marketing pattern. In the context of network externalities, measuring the value of leading customers is chosen as the key to coping with the research problem, as the leading customers are considered to be critical in forming a referral network. Accordingly, two sequential-move game models (i.e., the basic model and the extended model) were established to measure the value of leading customers, and a matrix transformation process was adopted to solve the model by transforming a complicated multisequence game into a simple simultaneous-move game. Based on the defined value of leading customers, a network-based referral mechanism was proposed that explores exactly how many awards should be allocated along the customer sequence to encourage the leading customers to provide a successful recommendation, and this demonstrates two general rules of awarding the referrals in our model setting. Moreover, the proposed solution approach helps to deepen the understanding of the effect of the leading position, which is meaningful for designing a greater number of referral approaches. [ABSTRACT FROM AUTHOR]

Background: In the UK, approximately 4,200 men who have sex with men (MSM) are living with HIV but remain undiagnosed. Maximising the number of high-risk people testing for HIV is key to ensuring prompt treatment and preventing onward infection. This study assessed how different HIV test characteristics affect the choice of testing option, including remote testing (HIV self-testing or HIV self-sampling), in the UK, a country with universal access to healthcare.Methods and Findings: Between 3 April and 11 May 2017, a cross-sectional online-questionnaire-based discrete choice experiment (DCE) was conducted in which respondents who expressed an interest in online material used by MSM were asked to imagine that they were at risk of HIV infection and to choose between different hypothetical HIV testing options, including the option not to test. A variety of different testing options with different defining characteristics were described so that the independent preference for each characteristic could be valued. The characteristics included where each test is taken, the sampling method, how the test is obtained, whether infections other than HIV are tested for, test accuracy, the cost of the test, the infection window period, and how long it takes to receive the test result. Participants were recruited and completed the instrument online, in order to include those not currently engaged with healthcare services. The main analysis was conducted using a latent class model (LCM), with results displayed as odds ratios (ORs) and probabilities. The ORs indicate the strength of preference for one characteristic relative to another (base) characteristic. In total, 620 respondents answered the DCE questions. Most respondents reported that they were white (93%) and were either gay or bisexual (99%). The LCM showed that there were 2 classes within the respondent sample that appeared to have different preferences for the testing options. The first group, which was likely to contain 86% of respondents, had a strong preference for face-to-face tests by healthcare professionals (HCPs) compared to remote testing (OR 6.4; 95% CI 5.6, 7.4) and viewed not testing as less preferable than remote testing (OR 0.10; 95% CI 0.09, 0.11). In the second group, which was likely to include 14% of participants, not testing was viewed as less desirable than remote testing (OR 0.56; 95% CI 0.53, 0.59) as were tests by HCPs compared to remote testing (OR 0.23; 95% CI 0.15, 0.36). In both classes, free remote tests instead of each test costing £30 was the test characteristic with the largest impact on the choice of testing option. Participants in the second group were more likely to have never previously tested and to be non-white than participants in the first group. The main study limitations were that the sample was recruited solely via social media, the study advert was viewed only by people expressing an interest in online material used by MSM, and the choices in the experiment were hypothetical rather than observed in the real world.Conclusions: Our results suggest that preferences in the context we examined are broadly dichotomous. One group, containing the majority of MSM, appears comfortable testing for HIV but prefers face-to-face testing by HCPs rather than remote testing. The other group is much smaller, but contains MSM who are more likely to be at high infection risk. For these people, the availability of remote testing has the potential to significantly increase net testing rates, particularly if provided for free. [ABSTRACT FROM AUTHOR]

Objective: To explore pregnant women’s preferences for birth setting in England. Design: Labelled discrete choice experiment (DCE). Setting: Online survey. Sample: Pregnant women recruited through social media and an online panel. Methods: We developed a DCE to assess women’s preferences for four hypothetical birth settings based on seven attributes: reputation, continuity of care, distance from home, time to see a doctor, partner able to stay overnight, chance of straightforward birth and safety for baby. We used a mixed logit model, with setting modelled as an alternative-specific constant, and conducted a scenario analysis to evaluate the impact of changes in attribute levels on uptake of birth settings. Main outcome measures: Women’s preferences for birth setting. Results: 257 pregnant women completed the DCE. All birth setting attributes, except ‘time to see doctor’, were significant in women’s choice (p<0.05). There was significant heterogeneity in preferences for some attributes. Changes to levels for ‘safety for the baby’ and ‘partner able to stay overnight’ were associated with larger changes from baseline uptake of birth setting. If the preferences identified were translated into the real-world context up to a third of those who reported planning birth in an obstetric unit might choose a midwifery unit assuming universal access to all settings, and knowledge of the differences between settings. Conclusions: We found that ‘safety for the baby’, ‘chance of a straightforward birth’ and ‘can the woman’s partner stay overnight following birth’ were particularly important in women’s preferences for hypothetical birth setting. If all birth settings were available to women and they were aware of the differences between them, it is likely that more low risk women who currently plan birth in OUs might choose a midwifery unit. [ABSTRACT FROM AUTHOR]

Recently we have witnessed a number of rapid shifts toward populism in the rhetoric and policies of major political parties, as exemplified in the 2016 Brexit Referendum, 2016 US Election, and 2017 UK General Election. Our perspective here is to focus on understanding the underlying societal processes behind these recent political shifts. We use novel methods to study social dynamics behind the 2016 Presidential election. This is done by using network science methods to identify key groups associated with the US right-wing during the election. We investigate how the groups grew on Twitter, and how their associated accounts changed their following behaviour over time. We find a new external faction of Trump supporters took a strong influence over the traditional Republican Party (GOP) base during the election campaign. The new group dominated the GOP group in terms of new members and endorsement via Twitter follows. Growth of new accounts for the GOP party all but collapsed during the campaign. While the Alt-right group was growing exponentially, it has remained relatively isolated. Counter to the mainstream view, we detected an unexpectedly low number of automated ‘bot’ accounts and accounts associated with foreign intervention in the Trump-supporting group. Our work demonstrates a powerful method for tracking the evolution of societal groups and reveals complex social processes behind political changes. [ABSTRACT FROM AUTHOR]

Television (TV) programming attracts ever-growing audiences and dominates the cultural zeitgeist. Viewership and social media engagement have become standard indices of programming success. However, accurately predicting individual episode success or future show performance using traditional metrics remains a challenge. Here we examine whether TV viewership and Twitter activity can be predicted using electroencephalography (EEG) measures, which are less affected by reporting biases and which are commonly associated with different cognitive processes. 331 participants watched an hour-long episode from one of nine prime-time shows (~36 participants per episode). Three frequency-based measures were extracted: fronto-central alpha/beta asymmetry (indexing approach motivation), fronto-central alpha/theta power (indexing attention), and fronto-central theta/gamma power (indexing memory processing). All three EEG measures and the composite EEG score significantly correlated across episode segments with the two behavioral measures of TV viewership and Twitter volume. EEG measures explained more variance than either of the behavioral metrics and mediated the relationship between the two. Attentional focus was integral for both audience retention and Twitter activity, while emotional motivation was specifically linked with social engagement and program segments with high TV viewership. These findings highlight the viability of using EEG measures to predict success of TV programming and identify cognitive processes that contribute to audience engagement with television shows. [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]

There is considerable concern about the role that social media, such as Facebook and Twitter, play in promoting misperceptions during political campaigns. These technologies are widely used, and inaccurate information flowing across them has a high profile. This research uses three-wave panel surveys conducted with representative samples of Americans during both the 2012 and 2016 U.S. Presidential elections to assess whether use of social media for political information promoted endorsement of falsehoods about major party candidates or important campaign issues. Fixed effects regression helps ensure that observed effects are not due to individual differences. Results indicate that social media use had a small but significant influence on misperceptions about President Obama in the 2012 election, and that this effect was most pronounced among strong partisans. Social media had no effect on belief accuracy about the Republican candidate in that election. The 2016 survey focused on campaign issues. There is no evidence that social media use influenced belief accuracy about these topics in aggregate, but Facebook users were unique. Social media use by this group reduced issue misperceptions relative to those who only used other social media. These results demonstrate that social media can alter citizens’ willingness to endorse falsehoods during an election, but that the effects are often small. [ABSTRACT FROM AUTHOR]

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

Following the 2007-2009 financial crisis, governments around the world passed laws that marked the beginning of new period of enhanced regulation of the financial industry. These laws called for a myriad of new regulations, which in the U.S. are created through the so-called notice-and-comment process. Through examining the text documents generated through this process, we study the formation of regulations to gain insight into how new regulatory regimes are implemented following major laws like the landmark Dodd-Frank Wall Street Reform and Consumer Protection Act. Due to the variety of constituent preferences and political pressures, we find evidence that the government implements rules strategically to extend the regulatory boundary by first pursuing procedural rules that establish how economic activities will be regulated, followed by specifying who is subject to the procedural requirements. Our findings together with the unique nature of the Dodd-Frank Act translate to a number of stylized facts that should guide development of formal models of the rule-making process. [ABSTRACT FROM AUTHOR]

Collective emotion has been traditionally evaluated by questionnaire survey on a limited number of people. Recently, big data of written texts on the Internet has been available for analyzing collective emotion for very large scales. Although short-term reflection between collective emotion and real social phenomena has been widely studied, long-term dynamics of collective emotion has not been studied so far due to the lack of long persistent data sets. In this study, we extracted collective emotion over a 10-year period from 3.6 billion Japanese blog articles. Firstly, we find that collective emotion shows clear periodic cycles, i.e., weekly and seasonal behaviors, accompanied with pulses caused by natural disasters. For example, April is represented by high Tension, probably due to starting school in Japan. We also identified long-term memory in the collective emotion that is characterized by the power-law decay of the autocorrelation function over several months. [ABSTRACT FROM AUTHOR]

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

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