Positivity of the English language

Over the last million years, human language has emerged and evolved as a fundamental instrument of social communication and semiotic representation. People use language in part to convey emotional information, leading to the central and contingent questions: (1) What is the emotional spectrum of natural language? and (2) Are natural languages neutrally, positively, or negatively biased? Here, we report that the human-perceived positivity of over 10,000 of the most frequently used English words exhibits a clear positive bias. More deeply, we characterize and quantify distributions of word positivity for four large and distinct corpora, demonstrating that their form is broadly invariant with respect to frequency of word use.Comment: Manuscript: 9 pages, 3 tables, 5 figures; Supplementary Information: 12 pages, 3 tables, 8 figure

Factors Predicting and Reducing Mortality in Patients with Invasive Staphylococcus aureus Disease in a Developing Country

BACKGROUND: Invasive Staphylococcus aureus infection is increasingly recognised as an important cause of serious sepsis across the developing world, with mortality rates higher than those in the developed world. The factors determining mortality in developing countries have not been identified. METHODS: A prospective, observational study of invasive S. aureus disease was conducted at a provincial hospital in northeast Thailand over a 1-year period. All-cause and S. aureus-attributable mortality rates were determined, and the relationship was assessed between death and patient characteristics, clinical presentations, antibiotic therapy and resistance, drainage of pus and carriage of genes encoding Panton-Valentine Leukocidin (PVL). PRINCIPAL FINDINGS: A total of 270 patients with invasive S. aureus infection were recruited. The range of clinical manifestations was broad and comparable to that described in developed countries. All-cause and S. aureus-attributable mortality rates were 26% and 20%, respectively. Early antibiotic therapy and drainage of pus were associated with a survival advantage (both p<0.001) on univariate analysis. Patients infected by a PVL gene-positive isolate (122/248 tested, 49%) had a strong survival advantage compared with patients infected by a PVL gene-negative isolate (all-cause mortality 11% versus 39% respectively, p<0.001). Multiple logistic regression analysis using all variables significant on univariate analysis revealed that age, underlying cardiac disease and respiratory infection were risk factors for all-cause and S. aureus-attributable mortality, while one or more abscesses as the presenting clinical feature and procedures for infectious source control were associated with survival. CONCLUSIONS: Drainage of pus and timely antibiotic therapy are key to the successful management of S. aureus infection in the developing world. Defining the presence of genes encoding PVL provides no practical bedside information and draws attention away from identifying verified clinical risk factors and those interventions that save lives

Studying Fake News via Network Analysis: Detection and Mitigation

Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost. However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information. Fake news on social media poses significant negative societal effects, and also presents unique challenges. To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news. In essence, news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types and how these networks can be used to detect and mitigation fake news on social media.Comment: Submitted as a invited book chapter in Lecture Notes in Social Networks, Springer Pres

Aggregation Bias: A Proposal to Raise Awareness Regarding Inclusion in Visual Analytics

Data is a powerful tool to make informed decisions. They can be used to design products, to segment the market, and to design policies. However, trusting so much in data can have its drawbacks. Sometimes a set of indicators can conceal the reality behind them, leading to biased decisions that could be very harmful to underrepresented individuals, for example. It is challenging to ensure unbiased decision-making processes because people have their own beliefs and characteristics and be unaware of them. However, visual tools can assist decision-making processes and raise awareness regarding potential data issues. This work describes a proposal to fight biases related to aggregated data by detecting issues during visual analysis and highlighting them, trying to avoid drawing inaccurate conclusions

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

High impact = high statistical standards? Not necessarily so.

What are the statistical practices of articles published in journals with a high impact factor? Are there differences compared with articles published in journals with a somewhat lower impact factor that have adopted editorial policies to reduce the impact of limitations of Null Hypothesis Significance Testing? To investigate these questions, the current study analyzed all articles related to psychological, neuropsychological and medical issues, published in 2011 in four journals with high impact factors: Science, Nature, The New England Journal of Medicine and The Lancet, and three journals with relatively lower impact factors: Neuropsychology, Journal of Experimental Psychology-Applied and the American Journal of Public Health. Results show that Null Hypothesis Significance Testing without any use of confidence intervals, effect size, prospective power and model estimation, is the prevalent statistical practice used in articles published in Nature, 89%, followed by articles published in Science, 42%. By contrast, in all other journals, both with high and lower impact factors, most articles report confidence intervals and/or effect size measures. We interpreted these differences as consequences of the editorial policies adopted by the journal editors, which are probably the most effective means to improve the statistical practices in journals with high or low impact factors

Lambda and Antilambda polarization from deep inelastic muon scattering

We report results of the first measurements of Lambda and Antilambda polarization produced in deep inelastic polarized muon scattering on the nucleon. The results are consistent with an expected trend towards positive polarization with increasing x_F. The polarizations of Lambda and Antilambda appear to have opposite signs. A large negative polarization for Lambda at low positive x_F is observed and is not explained by existing models.A possible interpretation is presented.Comment: 9 pages, 2 figure

Emergence of metapopulations and echo chambers in mobile agents

Multi-agent models often describe populations segregated either in the physical space, i.e. subdivided in metapopulations, or in the ecology of opinions, i.e. partitioned in echo chambers. Here we show how the interplay between homophily and social influence controls the emergence of both kinds of segregation in a simple model of mobile agents, endowed with a continuous opinion variable. In the model, physical proximity determines a progressive convergence of opinions but differing opinions result in agents moving away from each others. This feedback between mobility and social dynamics determines to the onset of a stable dynamical metapopulation scenario where physically separated groups of like-minded individuals interact with each other through the exchange of agents. The further introduction of confirmation bias in social interactions, defined as the tendency of an individual to favor opinions that match his own, leads to the emergence of echo chambers where different opinions can coexist also within the same group. We believe that the model may be of interest to researchers investigating the origin of segregation in the offline and online world