Exploring Relationships between Tweet Numbers and Over-The-Counter Drug Sales for Allergic Rhinitis: Retrospective Analysis (Preprint)

2021 ◽  
Author(s):  
Shoko Wakamiya ◽  
Osamu Morimoto ◽  
Katsuhiro Omichi ◽  
Hideyuki Hara ◽  
Ichiro Kawase ◽  
...  
Keyword(s):  
Social Media ◽  
Time Series ◽  
Allergic Rhinitis ◽  
Time Series Data ◽  
Series Data ◽  
Sales Volume ◽  
Social Media Data ◽  
Drug Sales ◽  
Number Of Patients ◽  
Media Data

BACKGROUND Health-related social media data are increasingly being used in disease surveillance studies. In particular, surveillance of infectious diseases such as influenza has demonstrated high correlations between the number of social media posts mentioning the disease and the number of patients who went to the hospital and were diagnosed with the disease. However, the prevalence of some diseases, such as allergic rhinitis, cannot be estimated based on the number of patients alone. Specifically, patients with allergic rhinitis self-medicate by taking over-the-counter (OTC) medications without going to the hospital. Although allergic rhinitis is not a life-threatening disease, it is a major social problem because it reduces patients’ quality of life, making it essential to understand its prevalence and the motives for self-medication behavior. OBJECTIVE To help understand the prevalence of allergic rhinitis and the motives for self-care treatment using social media data, this study investigated the relationship between the number of social media posts mentioning the main symptoms of allergic rhinitis and the sales volume of OTC rhinitis medications in Japan. METHODS We collected tweets over four years from 2017 to 2020 that included keywords corresponding to the main nasal symptoms of allergic rhinitis: “sneezing,” “runny nose,” and “stuffy nose.” We also obtained the sales volume of OTC drugs, including oral medications and nasal sprays, for the same period. We then calculated the Pearson correlation coefficient between time series data on the number of tweets per week and time series data on the sales volume of OTC drugs per week. RESULTS The results showed a much higher correlation (0.8432) between the time series data on the number of tweets mentioning “stuffy nose” and the time series data on the sales volume of nasal sprays than for the other two symptoms. There was also a high correlation (0.9317) between the seasonal components of these time series data. CONCLUSIONS We investigated the relationships between social media data and behavioral patterns, such as OTC drug sales volume. Exploring these relationships would be useful as a marketing indicator to predict sales volume using social media data. In future, in-depth investigations are required to cover other diseases and countries. We investigated the relationships between social media data and behavioral patterns, such as OTC drug sales volume. Exploring these relationships would be useful as a marketing indicator to predict sales volume using social media data. In future, in-depth investigations are required to cover other diseases and countries.

scholarly journals Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

2018 ◽  
Author(s):  
Shoko Wakamiya ◽  
Shoji Matsune ◽  
Kimihiro Okubo ◽  
Eiji Aramaki
Keyword(s):  
Social Media ◽  
Allergic Rhinitis ◽  
Seasonal Allergic Rhinitis ◽  
Japanese Cedar ◽  
Pollen Count ◽  
Causal Relationships ◽  
Social Media Data ◽  
Pollen Counts ◽  
Number Of Patients ◽  
Media Data

BACKGROUND Health-related social media data are increasingly used in disease-surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients in order to increase the credibility of disease surveillance based on social media data. OBJECTIVE This study aimed to clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of patients with seasonal allergic rhinitis in the real world. METHODS This analysis was conducted using datasets of pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of seasonal allergic rhinitis in Japan) and hinoki cypress (which commonly complicates seasonal allergic rhinitis) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword “kafunshō” (or seasonal allergic rhinitis) were calculated between January 1 and May 31, 2017. Daily numbers of patients with seasonal allergic rhinitis from January 1 to May 31, 2017, were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of patients with seasonal allergic rhinitis from February to May 2017. To determine if time-variant factors affect these causal relationships, we analyzed the main seasonal allergic rhinitis phase (February to April) when Japanese cedar trees actively produce and release pollen. RESULTS Increases in pollen count were found to increase the number of tweets during the overall study period (P=.04), but not the main seasonal allergic rhinitis phase (P=.05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P=.04) and the main seasonal allergic rhinitis phase (P=.01). Increases in the number of tweets increased the patient numbers during the main seasonal allergic rhinitis phase (P=.02), but not the overall study period (P=.89). Patient numbers did not affect the number of tweets in both the overall study period (P=.24) and the main seasonal allergic rhinitis phase (P=.47). CONCLUSIONS Understanding the causal relationships among pollen counts, tweet numbers, and numbers of patients with seasonal allergic rhinitis is an important step to increasing the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis.

scholarly journals Timing matters: crisis severity and occupancy rate forecasts in social unrest periods

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Richard T.R. Qiu ◽  
Anyu Liu ◽  
Jason L. Stienmetz ◽  
Yang Yu
Keyword(s):  
Social Media ◽  
Time Series ◽  
Hospitality Industry ◽  
Demand Forecasting ◽  
Time Series Model ◽  
Social Media Data ◽  
Forecasting Accuracy ◽  
Social Unrest ◽  
Content Type ◽  
Media Data

Purpose The impact of demand fluctuation during crisis events is crucial to the dynamic pricing and revenue management tactics of the hospitality industry. The purpose of this paper is to improve the accuracy of hotel demand forecast during periods of crisis or volatility, taking the 2019 social unrest in Hong Kong as an example. Design/methodology/approach Crisis severity, approximated by social media data, is combined with traditional time-series models, including SARIMA, ETS and STL models. Models with and without the crisis severity intervention are evaluated to determine under which conditions a crisis severity measurement improves hotel demand forecasting accuracy. Findings Crisis severity is found to be an effective tool to improve the forecasting accuracy of hotel demand during crisis. When the market is volatile, the model with the severity measurement is more effective to reduce the forecasting error. When the time of the crisis lasts long enough for the time series model to capture the change, the performance of traditional time series model is much improved. The finding of this research is that the incorporating social media data does not universally improve the forecast accuracy. Hotels should select forecasting models accordingly during crises. Originality/value The originalities of the study are as follows. First, this is the first study to forecast hotel demand during a crisis which has valuable implications for the hospitality industry. Second, this is also the first attempt to introduce a crisis severity measurement, approximated by social media coverage, into the hotel demand forecasting practice thereby extending the application of big data in the hospitality literature.

scholarly journals Tweets, tents, and events: The interplay between street protests and social media

2017 ◽  
Author(s):  
Marco T. Bastos ◽  
Dan Mercea ◽  
Arthur Charpentier
Keyword(s):  
Social Media ◽  
Time Series ◽  
Granger Causality ◽  
Time Series Data ◽  
Series Data ◽  
Political Unrest ◽  
Protest Activity

Recent protests have fuelled deliberations about the extent to which social media ignites popular uprisings. In this paper we use time-series data of Twitter, Facebook, and onsite protests to assess the Granger-causality between social media streams and onsite developments at the Indignados, Occupy, and Brazilian Vinegar protests. After applying a Gaussianization procedure to the data, we found that contentious communication on Twitter and Facebook forecasted onsite protest during the Indignados and Occupy protests, with bidirectional Granger-causality between online and onsite protest in the Occupy series. Conversely, the Vinegar demonstrations presented Granger-causality between Facebook and Twitter communication, and separately between protestors and injuries/arrests onsite. We conclude that the effective forecasting of protest activity likely varies across different instances of political unrest.

scholarly journals Deep Mixed Effect Model Using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

2020 ◽  
Vol 34 (04) ◽  
pp. 3649-3657
Author(s):  
Ingyo Chung ◽  
Saehoon Kim ◽  
Juho Lee ◽  
Kwang Joon Kim ◽  
Sung Ju Hwang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gaussian Processes ◽  
Time Series Data ◽  
Series Data ◽  
Patient Specific ◽  
Reliable Prediction ◽  
Mixed Effect ◽  
Treatment And Prevention ◽  
Number Of Patients

We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN).

scholarly journals Extending HydroShare to enable hydrologic time series data as social media

2015 ◽  
Vol 18 (2) ◽  
pp. 198-209 ◽  
Author(s):  
Jeffrey M. Sadler ◽  
Daniel P. Ames ◽  
Shaun J. Livingston
Keyword(s):  
Social Media ◽  
Time Series ◽  
Web Services ◽  
Data Management ◽  
Data Storage ◽  
Time Series Data ◽  
Series Data ◽  
Design Development ◽  
Scientific Discussion ◽  
Hydrologic Time Series

The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) hydrologic information system (HIS) is a widely used service oriented system for time series data management. While this system is intended to empower the hydrologic sciences community with better data storage and distribution, it lacks support for the kind of ‘Web 2.0’ collaboration and social-networking capabilities being used in other fields. This paper presents the design, development, and testing of a software extension of CUAHSI's newest product, HydroShare. The extension integrates the existing CUAHSI HIS into HydroShare's social hydrology architecture. With this extension, HydroShare provides integrated HIS time series with efficient archiving, discovery, and retrieval of the data, extensive creator and science metadata, scientific discussion and collaboration around the data and other basic social media features. HydroShare provides functionality for online social interaction and collaboration while the existing HIS provides the distributed data management and web services framework. The extension is expected to enable scientists to access and share both national- and laboratory-scale hydrologic time series datasets in a standards-based web services architecture combined with social media functionality developed specifically for the hydrologic sciences.

Global Patterns of Digital Repression

2021 ◽  
pp. 62-95
Author(s):  
Steven Feldstein
Keyword(s):  
Social Media ◽  
Time Series ◽  
Quantitative Data ◽  
Time Series Data ◽  
Regime Type ◽  
Series Data ◽  
Latent Construct ◽  
Global Patterns ◽  
The Relationship ◽  
Composite Indexes

This chapter presents quantitative data to explain the main arguments of the book. Specifically, it provides pooled, cross-national, time-series data to describe global patterns of digital repression, and it uses that data to develop and validate two composite indexes: a latent construct of digital repression and a latent construct of digital repression capacity. It discusses overall findings from the digital repression index—the relationship between regime type and digital repression, highest- and lowest-performing countries, as well as outliers. It also compares digital repression enactment to capacity, and investigates differences between autocracies and democracies. Finally, it analyzes individual components of digital repression—social media surveillance, online censorship, social manipulation and disinformation, Internet shutdowns, and arrests of online users for political content—and provide explanations for authoritarian and democratic use.

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