scholarly journals Vaping-related news events and their relationship with sentiment in the online vaping environment: A computational interrupted time series analysis with large-scale public data

2021 ◽  
Author(s):  
Navin Kumar ◽  
Kamila Janmohamed
Keyword(s):  
Health Outcomes ◽  
Large Scale ◽  
News Coverage ◽  
Interrupted Time Series Analysis ◽  
Online Data ◽  
Significant Positive Association ◽  
Public Data ◽  
Online Environments ◽  
News Events ◽  
Sentiment Score

Abstract Background: Vaping-related news coverage may have furthered misconceptions around the relative harms of vapes. Also, some positive opinions around vaping may be derived from misinformation, perhaps creating inimical health outcomes. Thus, we need to study how vaping-related news events (e.g. 2019 vaping illness epidemic, COVID-19) are associated with sentiment in the online vaping environment, to better understand how to promote vaping as a potential harm reduction technique for those who smoke and are unable to quit, and to minimize vape-centric misinformation that could lead to reduced health outcomes. Methods: We obtained vaping-related online data through web-scraping several online environments from August 1 2019 - April 21 2020. Sentiment analysis was performed to understand changes in sentiment in the online vaping environment in relation to vaping-related events, such as the Trump administration's planned ban on flavored vaping products, and when COVID-19 was first reported to the WHO. Results: For all online environments, we observed a statistically significant negative association of 15% (Estimate: -0.16; 95% CI: -0.29, -0.03; P: 0.01) between sentiment score and the Trump administration's move towards a ban on flavored vaping products, and a statistically significant positive association of 7% between sentiment score (Estimate: 0.07; 95% CI: 0.01, 0.14; P: 0.02) and when COVID-19 was first reported to the WHO (December 31 2019). Conclusions: News events may be related to sentiment in the online vaping environment, depending on the event. Depending on the nature of the event, we suggest that public health messaging may improve health outcomes.

scholarly journals News Events and Their Relationship With US Vape Sales: An Interrupted Time Series Analysis

2021 ◽  
Author(s):  
Kamila Janmohamed ◽  
Shinpei Nakamura Sakai ◽  
Abdul-Nasah Soale ◽  
Laura Forastiere ◽  
Navin Kumar
Keyword(s):  
Time Series ◽  
Risk Perceptions ◽  
News Coverage ◽  
Policy Instruments ◽  
Positive Association ◽  
Interrupted Time Series ◽  
The United States ◽  
Interrupted Time Series Analysis ◽  
Mortality Burden ◽  
News Events

Abstract Objective News coverage around vaping-related events may have furthered misconceptions regarding the relative harms of vapes. Such information may influence the decisions of individuals who smoke, around switching to vaping, potentially affecting the overall tobacco mortality burden. Thus, it is prudent to study how news events (e.g., 2019 vaping illness epidemic) are associated with vape sales in the United States, to possibly reduce the tobacco mortality burden. Methods We used weekly retail sales data for e-cigarettes (30 December 2018 - 28 December 2019) from the US retail scanner data compiled by the Nielsen Company. We used an interrupted time series design with segmented regression analysis to determine immediate and longer-term impacts of individual news events (e.g. Trump administration's planned ban on flavored vaping products) on vape sales, controlling for pre-existing trends.Results Unexpectedly, we noted a statistically significant positive relationship between vape sales and the CDC announcing an investigation into vaping-related illnesses (Change: 6.59%, Estimate: 0.064; 95% CI: 0.036, 0.092; P<0.001). We also observed a similar positive association between vape sales and the CDC's announcement on the link between Vitamin E acetate and EVALI (Change: 2.93%, Estimate: 0.029; 95% CI: 0.003, 0.055; P<0.05). There was a steep decline in sales after these events.ConclusionsNews events are associated with US vape sales. Findings have implications for the management of risk perceptions around vaping to improve health outcomes of tobacco users. Information-based policy instruments can be applied to balance the effects of news events that may influence vape sales.

scholarly journals metaXplor: an interactive viral and microbial metagenomic data manager

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Guilhem Sempéré ◽  
Adrien Pétel ◽  
Magsen Abbé ◽  
Pierre Lefeuvre ◽  
Philippe Roumagnac ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Metagenomic Data ◽  
Online Data ◽  
Data Repositories ◽  
Ongoing Research ◽  
Efficient Management ◽  
Public Data ◽  
Reference Databases ◽  
Interactive Data ◽  
User Friendly

Abstract Background Efficiently managing large, heterogeneous data in a structured yet flexible way is a challenge to research laboratories working with genomic data. Specifically regarding both shotgun- and metabarcoding-based metagenomics, while online reference databases and user-friendly tools exist for running various types of analyses (e.g., Qiime, Mothur, Megan, IMG/VR, Anvi'o, Qiita, MetaVir), scientists lack comprehensive software for easily building scalable, searchable, online data repositories on which they can rely during their ongoing research. Results metaXplor is a scalable, distributable, fully web-interfaced application for managing, sharing, and exploring metagenomic data. Being based on a flexible NoSQL data model, it has few constraints regarding dataset contents and thus proves useful for handling outputs from both shotgun and metabarcoding techniques. By supporting incremental data feeding and providing means to combine filters on all imported fields, it allows for exhaustive content browsing, as well as rapid narrowing to find specific records. The application also features various interactive data visualization tools, ways to query contents by BLASTing external sequences, and an integrated pipeline to enrich assignments with phylogenetic placements. The project home page provides the URL of a live instance allowing users to test the system on public data. Conclusion metaXplor allows efficient management and exploration of metagenomic data. Its availability as a set of Docker containers, making it easy to deploy on academic servers, on the cloud, or even on personal computers, will facilitate its adoption.

The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map

2021 ◽  
Vol 25 (4) ◽  
pp. 949-972
Author(s):  
Nannan Zhang ◽  
Xixi Yao ◽  
Chao Luo
Keyword(s):  
Fuzzy Set ◽  
Sequence Data ◽  
Concept Drift ◽  
Intuitionistic Fuzzy Set ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Fuzzy Cognitive Map ◽  
Online Data ◽  
Intuitionistic Fuzzy ◽  
Public Data

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.

scholarly journals Uncovering the structure and temporal dynamics of information propagation

2014 ◽  
Vol 2 (1) ◽  
pp. 26-65 ◽  
Author(s):  
MANUEL GOMEZ RODRIGUEZ ◽  
JURE LESKOVEC ◽  
DAVID BALDUZZI ◽  
BERNHARD SCHÖLKOPF
Keyword(s):  
News Media ◽  
Information Diffusion ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Inference Algorithm ◽  
Information Propagation ◽  
Transmission Rates ◽  
Diffusion Dynamics ◽  
Underlying Network ◽  
News Events

AbstractTime plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

scholarly journals Effects of a Large-Scale Unconditional Cash Transfer Program on Mental Health Outcomes of Young People in Kenya

2016 ◽  
Vol 58 (2) ◽  
pp. 223-229 ◽  
Author(s):  
Kelly Kilburn ◽  
Harsha Thirumurthy ◽  
Carolyn Tucker Halpern ◽  
Audrey Pettifor ◽  
Sudhanshu Handa
Keyword(s):  
Mental Health ◽  
Young People ◽  
Health Outcomes ◽  
Large Scale ◽  
Cash Transfer ◽  
Mental Health Outcomes ◽  
Transfer Program ◽  
Unconditional Cash Transfer

scholarly journals Spatially-explicit estimates of global wastewater production, collection, treatment and re-use

2020 ◽  
Author(s):  
Edward R. Jones ◽  
Michelle T. H. van Vliet ◽  
Manzoor Qadir ◽  
Marc F. P. Bierkens
Keyword(s):  
Large Scale ◽  
Western Europe ◽  
Treated Wastewater ◽  
Spatially Explicit ◽  
Wastewater Management ◽  
Online Data ◽  
Untreated Wastewater ◽  
Country Level ◽  
Data Driven Approach ◽  
Global Population

Abstract. Continually improving and affordable wastewater management provides opportunities for both pollution reduction and clean water supply augmentation, whilst simultaneously promoting sustainable development and supporting the transition to a circular economy. This study aims to provide the first comprehensive and consistent global outlook on the state of domestic and industrial wastewater production, collection, treatment and re-use. We use a data-driven approach, collating, cross-examining and standardising country-level wastewater data from online data resources. Where unavailable, data is estimated using multiple linear regression. Country-level wastewater data are subsequently downscaled and validated at 5 arc-minute (~ 10 km) resolution. This study estimates global wastewater production at 359.5 billion m3 yr−1, of which 63 % (225.6 billion m3 yr−1) is collected and 52 % (188.1 billion m3 yr−1) is treated. By extension, we estimate that 48 % of global wastewater production is released to the environment untreated, which is significantly lower than previous estimates of ~ 80 %. An estimated 40.7 billion m3 yr−1 of treated wastewater is intentionally re-used. Substantial differences in per capita wastewater production, collection and treatment are observed across different geographic regions and by level of economic development. For example, just over 16 % of the global population in high income countries produce 41 % of global wastewater. Treated wastewater re-use is particularly significant in the Middle East and North Africa (15 %) and Western Europe (16 %), while containing just 5.8 % and 5.7 % of the global population, respectively. Our database serves as a reference for understanding the global wastewater status and for identifying hotspots where untreated wastewater is released to the environment, which are found particularly in South and Southeast Asia. Importantly, our results also serve as a baseline for evaluating progress towards many policy goals that are both directly and indirectly connected to wastewater management (e.g. SDGs). Our spatially-explicit results available at 5 arc-minute resolution are well suited for supporting more detailed hydrological analyses such as water quality modelling and large-scale water resource assessments, and can be accessed at: https://doi.pangaea.de/10.1594/PANGAEA.918731 (Jones et al., 2020). A temporary link to this dataset for the review process can be accessed at: https://www.pangaea.de/tok/6631ef8746b59999071fa2e692fbc492c97352aa.

scholarly journals Country-level and gridded estimates of wastewater production, collection, treatment and reuse

2021 ◽  
Vol 13 (2) ◽  
pp. 237-254
Author(s):  
Edward R. Jones ◽  
Michelle T. H. van Vliet ◽  
Manzoor Qadir ◽  
Marc F. P. Bierkens
Keyword(s):  
Large Scale ◽  
Western Europe ◽  
Wastewater Reuse ◽  
Treated Wastewater ◽  
Wastewater Management ◽  
Online Data ◽  
Untreated Wastewater ◽  
Country Level ◽  
Treated Wastewater Reuse ◽  
Global Population

Abstract. Continually improving and affordable wastewater management provides opportunities for both pollution reduction and clean water supply augmentation, while simultaneously promoting sustainable development and supporting the transition to a circular economy. This study aims to provide the first comprehensive and consistent global outlook on the state of domestic and manufacturing wastewater production, collection, treatment and reuse. We use a data-driven approach, collating, cross-examining and standardising country-level wastewater data from online data resources. Where unavailable, data are estimated using multiple linear regression. Country-level wastewater data are subsequently downscaled and validated at 5 arcmin (∼10 km) resolution. This study estimates global wastewater production at 359.4×109 m3 yr−1, of which 63 % (225.6×109 m3 yr−1) is collected and 52 % (188.1×109 m3 yr−1) is treated. By extension, we estimate that 48 % of global wastewater production is released to the environment untreated, which is substantially lower than previous estimates of ∼80 %. An estimated 40.7×109 m3 yr−1 of treated wastewater is intentionally reused. Substantial differences in per capita wastewater production, collection and treatment are observed across different geographic regions and by level of economic development. For example, just over 16 % of the global population in high-income countries produces 41 % of global wastewater. Treated-wastewater reuse is particularly substantial in the Middle East and North Africa (15 %) and western Europe (16 %), while comprising just 5.8 % and 5.7 % of the global population, respectively. Our database serves as a reference for understanding the global wastewater status and for identifying hotspots where untreated wastewater is released to the environment, which are found particularly in South and Southeast Asia. Importantly, our results also serve as a baseline for evaluating progress towards many policy goals that are both directly and indirectly connected to wastewater management. Our spatially explicit results available at 5 arcmin resolution are well suited for supporting more detailed hydrological analyses such as water quality modelling and large-scale water resource assessments and can be accessed at https://doi.org/10.1594/PANGAEA.918731 (Jones et al., 2020).

scholarly journals Helping Novices Avoid the Hazards of Data: Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources

AI Magazine ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 19-32 ◽  
Author(s):  
Sasin Janpuangtong ◽  
Dylan A. Shell
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Model Building ◽  
Data Extraction ◽  
Data Sources ◽  
Online Data ◽  
Model Generalization ◽  
Improve Model ◽  
Building Process ◽  
High Level

The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses. This article adopts the solution of using higher-level knowledge to allow this sort of domain knowledge to be used automatically, selecting relevant input attributes, and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

scholarly journals Using an Online Sample to Estimate the Size of an Offline Population

Demography ◽  
2019 ◽  
Vol 56 (6) ◽  
pp. 2377-2392 ◽  
Author(s):  
Dennis M. Feehan ◽  
Curtiss Cobb
Keyword(s):  
Social Sciences ◽  
Digital Divide ◽  
Large Scale ◽  
Empirical Test ◽  
Information Age ◽  
Modern World ◽  
Randomized Experiment ◽  
Data Sets ◽  
Online Data ◽  
Future Studies

AbstractOnline data sources offer tremendous promise to demography and other social sciences, but researchers worry that the group of people who are represented in online data sets can be different from the general population. We show that by sampling and anonymously interviewing people who are online, researchers can learn about both people who are online and people who are offline. Our approach is based on the insight that people everywhere are connected through in-person social networks, such as kin, friendship, and contact networks. We illustrate how this insight can be used to derive an estimator for tracking the digital divide in access to the Internet, an increasingly important dimension of population inequality in the modern world. We conducted a large-scale empirical test of our approach, using an online sample to estimate Internet adoption in five countries (n ≈ 15,000). Our test embedded a randomized experiment whose results can help design future studies. Our approach could be adapted to many other settings, offering one way to overcome some of the major challenges facing demographers in the information age.

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