scholarly journals Changes in public response associated with various COVID-19 restrictions in Ontario, Canada: an observational study using social media time series data (Preprint)

10.2196/28716 ◽  
2021 ◽  
Author(s):  
Antony Chum ◽  
Andrew Nielsen ◽  
Zachary Bellows ◽  
Eddie Farrell ◽  
Pierre-Nicolas Durette ◽  
...  
Keyword(s):  
Social Media ◽  
Time Series ◽  
Observational Study ◽  
Time Series Data ◽  
Series Data ◽  
Public Response

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.

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 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.

scholarly journals Measuring the Impact of Financial News and Social Media on Stock Market Modeling Using Time Series Mining Techniques

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 181 ◽  
Author(s):  
Foteini Kollintza-Kyriakoulia ◽  
Manolis Maragoudakis ◽  
Anastasia Krithara
Keyword(s):  
Social Media ◽  
Time Series ◽  
Time Series Data ◽  
Technical Analysis ◽  
Series Data ◽  
Textual Information ◽  
Public Opinions ◽  
Time Periods ◽  
The Impact ◽  
Matching Techniques

In this work, we study the task of predicting the closing price of the following day of a stock, based on technical analysis, news articles and public opinions. The intuition of this study lies in the fact that technical analysis contains information about the event, but not the cause of the change, while data like news articles and public opinions may be interpreted as a cause. The paper uses time series analysis techniques such as Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) to study the existence of a relation between price data and textual information, either from news or social media. Pattern matching techniques from time series data are also incorporated, in order to experimentally validate potential correlations of price and textual information within given time periods. The ultimate goal is to create a forecasting model that exploits the previously discovered patterns in order to augment the forecasting accuracy. Results obtained from the experimental phase are promising. The performance of the classifier shows clear signs of improvement and robustness within the time periods where patterns between stock price and the textual information have been identified, compared to the periods where patterns did not exist.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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