Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal information of tweets along with the textual content.

Objectives We examine the dynamics of patient sharing relations within an Italian regional community of 35 hospitals serving approximately 1,300,000 people. We test whether interorganizational relations provide individual patients access to higher quality providers of care. Methods We reconstruct the complete temporal sequence of the 3461 consecutive interhospital patient sharing events observed between each pair of hospitals in the community during 2005-2008. We distinguish between transfers occurring between and within different medical specialties. We estimate newly derived models for relational event sequences that allow us to control for the most common forms of network-like dependencies that are known to characterize collaborative relations between hospitals. We use 45 day risk-adjusted readmission rate as a proxy for hospital quality. Results After controls (e.g., geographical distance, size, and the existence of prior collaborative relations), we find that patients flow from less to more capable hospitals. We show that this result holds for patient being shared both between as well as within medical specialties. Nonetheless there are strong and persistent other organizational and relational effects driving transfers. Conclusions Decentralized patient sharing decisions taken by the 35 hospitals give rise to a system of collaborative interorganizational arrangements that allow patient to access hospitals delivering a higher quality of care. This result is relevant for health care policy because it suggests that collaborative relations between hospitals may produce desirable outcomes both for individual patients, as well as for regional health-care systems.

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Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individualshomophily -or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious -by which we merely mean predictive of future negative sentiment expression -while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent.Keywords: social media; social network; diffusion; health behavior; contagion Social networks play an important role in affecting the dynamics of health behaviors and the associated diseases [-], but identifying the main drivers of health behavior spread in social networks has been challenging. The observation that health behavior dynamics follow the patterns of social contacts -e.g. that behaviors are often clustered [, ] and positively assorted at the dyadic level [, ] -can be explained by multiple processes, the two most prominent being homophily and social influence. The homophily hypothesis posits that social contacts are a product of likemindedness, whereas the social influence hypothesis posits that likemindedness is a product of social contacts. Measuring and distinguishing between the effects of homophily and social influence can be difficult in observational studies [, , ], but is important for the development of health behavior intervention strategies. Vaccination behavior is a prime example of a health behavior shaping disease dynamics: outbreaks of vaccine preventable disease are more likely if overall vaccination rates decline [], or if vaccination refusal is clustered in local communities [, ]. The continuously evolving public concern about vaccines despite the overwhelming scientific evidence on the safety of vaccines reflect the need for an increased understanding on how such sentiments spread over time [].

Abstract-The concept of differential privacy as a rigorous definition of privacy has emerged from the cryptographic community. However, further careful evaluation is needed before we can apply these theoretical results to privacy preservation in everyday data mining and statistical analysis. In this paper we demonstrate how to integrate a differential privacy framework with the classical statistical hypothesis testing in the domain of clinical trials where personal information is sensitive. We develop concrete methodology that researchers can use. We derive rules for the sample size adjustment whereby both statistical efficiency and differential privacy can be achieved for the specific tests for binomial random variables and in contingency tables.