The Cost of Sharing Information in a Social World

With the increasing prevalence of large scale online social networks, the field has evolved from studying small scale networks and interactions to massive ones that encompass huge fractions of the world’s population. While many methods focus on techniques at scale applied to a single domain, methods that apply techniques across multiple domains are becoming increasingly important. These methods rely on understanding the complex relationships in the data. In the context of social networks, the big data available allows us to better model and analyze the flow of information within the network. The first part of this thesis discusses methods to more effectively learn and predict in a social network by leveraging information across multiple domains and types of data. We document a method to identify users from their access to content in a network and their click behavior. Even on a macro level, click behavior is often hard to obtain. We describe a technique to predict click behavior using other public information about the social network. Communication within a network inevitably has some bias that can be attributed to individual preferences and quality as well as the underlying structure of the network. The second part of the thesis characterizes the structural bias in a network by modeling the underlying information flow as a commodity of trade

Emerging Scholars Program—A PLTL-CS Program That Increases Recruitment and Retention of Women in the Major

The Emerging Scholars Program (ESP) in Computer Science is a Peer Led Team Learning (PLTL) approach to bringing undergraduates new to the discipline together with peer mentors to work on computational problems, and to expose them to the broad array of disciplines within computer science. This program demonstrates that computer science is necessarily a collaborative activity that focuses more on problem solving and algorithmic thinking than on programming. In spring 2012 the computer science department at an urban research university university completed the 9th iteration of ESP, with 104 women and 36 men completing the program. Our evaluation data indicates that ESP increased enrollment in the computer science major. 47% of students who took ESP along with the introduction to computer programming course at the university study site during this study majored in computer science. In addition, survey results indicated that a large majority of students intended to take another computer science course, were enthusiastic about the program, and found the workshop topics exciting and engaging. Participants reported that they learned more about computer science in ESP, and would recommend ESP to others

Social Clicks: What and Who Gets Read on Twitter?

International audienceOnline news domains increasingly rely on social media to drive traffic to their websites. Yet we know surprisingly little about how a social media conversation mentioning an online article actually generates clicks. Sharing behaviors, in contrast, have been fully or partially available and scrutinized over the years. While this has led to multiple assumptions on the diffusion of information, each assumption was designed or validated while ignoring actual clicks. We present a large scale, unbiased study of social clicks - that is also the first data of its kind - gathering a month of web visits to online resources that are located in 5 leading news domains and that are mentioned in the third largest social media by web referral (Twitter). Our dataset amounts to 2.8 million shares, together responsible for 75 billion potential views on this social media, and 9.6 million actual clicks to 59,088 unique resources. We design a reproducible methodology and carefully correct its biases. As we prove, properties of clicks impact multiple aspects of information diffusion, all previously unknown. (i) Secondary resources, that are not promoted through headlines and are responsible for the long tail of content popularity, generate more clicks both in absolute and relative terms. (ii) Social media attention is actually long-lived, in contrast with temporal evolution estimated from shares or receptions. (iii) The actual influence of an intermediary or a resource is poorly predicted by their share count, but we show how that prediction can be made more precise

Measuring Click and Share Dynamics on Social Media: A Reproducible and Validated Approach

Online social conversations have increasingly become the means to find and view information online. In contrast to traditional web surfing models, clicks from social media result from a series of endorsements subject to user memory, past behavior and intermittently divided attention. Understanding click dynamics allows us to leverage those facets to improve relevance, forecast traffic and better manage influence in information dissemination. Unfortunately, data on clicks – even in aggregate – remain proprietary and inaccessible to researchers and scientists in many disciplines. In our work, we aim to allow the study of clicks through a proxy, allowing analysts to fully study click dynamics on Twitter. We focus on the scope of a news content publisher with a large readership and a broad domain of topics. We validate one such proxy, clicks-per-follower (CPF), based on publicly accessible data. We develop a model to compute CPI from public data. We use this method to examine how sharing affects consumption on Twitter: our findings suggest that mass retweeting of a URL does not necessarily translate into a substantial increase in clicks

Metaproteomics of aquatic microbial communities in a deep and stratified estuary

Here we harnessed the power of metaproteomics to assess the metabolic diversity and function of stratified aquatic microbial communities in the deep and expansive Lower St. Lawrence Estuary, located in eastern Canada. Vertical profiling of the microbial communities through the stratified water column revealed differences in metabolic lifestyles and in carbon and nitrogen processing pathways. In productive surface waters, we identified heterotrophic populations involved in the processing of high and low molecular weight organic matter from both terrestrial (e.g. cellulose and xylose) and marine (e.g. organic compatible osmolytes) sources. In the less productive deep waters, chemosynthetic production coupled to nitrification by MG-I Thaumarchaeota and Nitrospina appeared to be a dominant metabolic strategy. Similar to other studies of the coastal ocean, we identified methanol oxidation proteins originating from the common OM43 marine clade. However, we also identified a novel lineage of methanol-oxidizers specifically in the particle-rich bottom (i.e. nepheloid) layer. Membrane transport proteins assigned to the uncultivated MG-II Euryarchaeota were also specifically detected in the nepheloid layer. In total, these results revealed strong vertical structure of microbial taxa and metabolic activities, as well as the presence of specific "nepheloid" taxa that may contribute significantly to coastal ocean nutrient cycling.Peer reviewed: YesNRC publication: Ye

Socio-Structural and Neighborhood Predictors of Incident Criminal Justice Involvement in a Population-Based Cohort of Young Black MSM and Transgender Women.

Black men who have sex with men (MSM) and transgender women are disproportionately affected by criminal justice involvement (CJI) and HIV. This study recruited 618 young Black MSM and transgender women in Chicago, IL, using respondent-driven sampling between 2013 and 2014. Random effects logistic regression evaluated predictors of incident CJI over 18 months of follow-up. Controlling for respondent age, gender and sexual identity, spirituality (aOR 0.56, 95% CI 0.33-0.96), and presence of a mother figure (aOR 0.41, 95% CI 0.19-0.89) were protective against CJI. Economic hardship (financial or residential instability vs. neither aOR 2.23, 95% CI 1.10-4.51), two or more past episodes of CJI vs. none (aOR 2.66, 95% CI 1.40-5.66), and substance use (marijuana use vs. none aOR 2.79, 95% CI 1.23-6.34; other drug use vs. none aOR 4.49, 95% CI 1.66-12.16) were associated with CJI during follow-up. Research to identify and leverage resilience factors that can buffer the effects of socioeconomic marginalization may increase the effectiveness of interventions to address the socio-structural factors that increase the risk for CJI among Black MSM and transgender women. Given the intersection of incarceration, HIV and other STIs, and socio-structural stressors, criminal justice settings are important venues for interventions to reduce health inequities in these populations