Representatively Memorable: Sampling the Right Phrase Set to Get the Text Entry Experiment Right

[EN] In text entry experiments, memorability is a desired property of the phrases used as stimuli. Unfortunately, to date there is no automated method to achieve this effect. As a result, researchers have to use either manually curated Englishonly phrase sets or sampling procedures that do not guarantee phrases being memorable. In response to this need, we present a novel sampling method based on two core ideas: a multiple regression model over language-independent features, and the statistical analysis of the corpus from which phrases will be drawn. Our results show that researchers can finally use a method to successfully curate their own stimuli targeting potentially any language or domain. The source code as well as our phrase sets are publicly available.This work is supported by the 7th Framework Program of the European Commision (FP7/2007-13) under grant agreements 287576 (CASMACAT) and 600707 (tranScriptorium)Leiva, LA.; Sanchis-Trilles, G. (2014). Representatively Memorable: Sampling the Right Phrase Set to Get the Text Entry Experiment Right. ACM. 1709-1712. https://doi.org/10.1145/2556288.2557024S1709171

Cascades: A view from Audience

Cascades on online networks have been a popular subject of study in the past decade, and there is a considerable literature on phenomena such as diffusion mechanisms, virality, cascade prediction, and peer network effects. However, a basic question has received comparatively little attention: how desirable are cascades on a social media platform from the point of view of users? While versions of this question have been considered from the perspective of the producers of cascades, any answer to this question must also take into account the effect of cascades on their audience. In this work, we seek to fill this gap by providing a consumer perspective of cascade. Users on online networks play the dual role of producers and consumers. First, we perform an empirical study of the interaction of Twitter users with retweet cascades. We measure how often users observe retweets in their home timeline, and observe a phenomenon that we term the "Impressions Paradox": the share of impressions for cascades of size k decays much slower than frequency of cascades of size k. Thus, the audience for cascades can be quite large even for rare large cascades. We also measure audience engagement with retweet cascades in comparison to non-retweeted content. Our results show that cascades often rival or exceed organic content in engagement received per impression. This result is perhaps surprising in that consumers didn't opt in to see tweets from these authors. Furthermore, although cascading content is widely popular, one would expect it to eventually reach parts of the audience that may not be interested in the content. Motivated by our findings, we posit a theoretical model that focuses on the effect of cascades on the audience. Our results on this model highlight the balance between retweeting as a high-quality content selection mechanism and the role of network users in filtering irrelevant content

A Benchmark Study on Sentiment Analysis for Software Engineering Research

A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.Comment: Proceedings of 15th International Conference on Mining Software Repositories (MSR 2018

Competition and Selection Among Conventions

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the online domain, where the data offers the potential to study competition among conventions at a fine-grained level. In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis must take place in the presence of these two issues. In this work we study a setting in which we can cleanly track the competition among conventions. Our analysis is based on the spread of low-level authoring conventions in the eprint arXiv over 24 years: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of them; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time--and at the individual level about the outcome of "fights" between people over convention choices.Comment: To appear in Proceedings of WWW 2017, data at https://github.com/CornellNLP/Macro

High Levels of Education Are Associated With an Increased Risk of Latent Autoimmune Diabetes in Adults: Results from the Nord-Trøndelag Health Study

Although autoimmune diabetes in adults is a common form of diabetes, knowledge on risk factors and long term consequences of the disease is limited. The aims of this thesis were to investigate the influence of socioeconomic factors (education and occupation), sleep disturbances and psychological well-being on the risk of developing autoimmune diabetes in adults, to investigate whether genetic variation in the melatonin receptor 1B (MTNR1B) contributes to the association between poor sleep and type 2 diabetes which has been previously suggested, and finally to investigate the risk of mortality from all causes, cardiovascular disease and ischemic heart disease in adult-onset autoimmune diabetes, with consideration of the possible influence of metabolic risk factors, glycaemic control, lifestyle factors and socioeconomic position. These studies are based on data from the Norwegian HUNT Study, to date the largest population-based study where incident cases of autoimmune diabetes in adults can be separated from cases of type 2 diabetes. The HUNT Study consists of three separate surveys performed on three occasions in 1984-2008 and contains information from questionnaires, clinical examinations and blood samples. Information on mortality was obtained by linkage to the national Cause of Death Registry. Individuals who were positive for antibodies against glutamic acid decarboxylase and with onset of diabetes at ≥35 years were classified as having autoimmune diabetes in adults. The main finding of Study I was that high educational levels (university versus primary school) were associated with an increased risk of autoimmune diabetes in adults (HR 1.98, 95% CI 1.21-3.26) after adjustment for BMI, physical activity, smoking, alcohol consumption, and family history of diabetes, whereas type 2 diabetes was more common in those with low education. An increased risk of autoimmune diabetes in adults was also seen in individuals who reported having sleep disturbances and low psychological well-being (HR 1.84, 95% CI 1.10-3.09), a risk similar to that seen in type 2 diabetes (HR 1.31, 95% CI 1.13-1.50) (Study II). The results from Study III indicated that there was no influence of the MTNR1B genetic variant on the association between poor sleep and type 2 diabetes. The association remained after adjustment for genotype and was seen in non-carriers as well as in carriers of the risk allele. Mortality from all causes (HR 1.55, 95% CI 1.25-1.92), cardiovascular disease (HR 1.87, 95% CI 1.40-2.48) and ischemic heart disease (HR 2.39, 95% CI 1.57-3.64) was increased in autoimmune diabetes in adults compared to individuals without diabetes. Importantly, mortality risk was as high as in type 2 diabetes, despite a more favourable metabolic risk profile in patients with autoimmune diabetes. In these patients, excess mortality appeared to be primarily associated with poor glycaemic control. These findings suggest, for the first time, that socioeconomic and psychosocial factors contribute to the development of autoimmune diabetes in adults. The results are in line with previous data indicating that the aetiology of autoimmune diabetes is partly similar to that of type 2 diabetes but suggest, also, that there are other, currently unidentified, environmental risk factors for autoimmune diabetes that remain to be explored. Finally, the results indicate that survival in individuals with autoimmune diabetes with adult onset would be improved by a more effective treatment

Spontaneous morphing of equibiaxially pre-stretched elastic bilayers: The role of sample geometry

An elastic bilayer, consisting of an equibiaxially pre-stretched sheet bonded to a stress-free one, spontaneously morphs into curved shapes in the absence of external loads or constraints. Using experiments and numerical simulations, we explore the role of geometry for square and rectangular samples in determining the equilibrium shape of the system, for a fixed pre-stretch. We classify the observed shapes over a wide range of aspect ratios according to their curvatures and compare measured and computed values, which show good agreement. In particular, as the bilayer becomes thinner, a bifurcation of the principal curvatures occurs, which separates two scaling regimes for the energy of the system. We characterize the transition between these two regimes and show the peculiar features that distinguish square from rectangular samples. The results for our model bilayer system may help explaining morphing in more complex systems made of active materials