scholarly journals Assessing Epidemic Diseases and Public Opinion through Popular Search Behavior Using Non-English Language Google Trends (Preprint)

10.2196/12226 ◽  
2018 ◽  
Author(s):  
Yu-Wei Chang ◽  
Wei-Lun Chiang ◽  
Wen-Hung Wang ◽  
Chun-Yu Lin ◽  
Ling-Chien Hung ◽  
...  
Keyword(s):  
Public Opinion ◽  
English Language ◽  
Search Behavior ◽  
Google Trends ◽  
Epidemic Diseases

scholarly journals Assessing Epidemic Diseases and Public Opinion through Popular Search Behavior Using Non-English Language Google Trends (Preprint)

2018 ◽  
Author(s):  
Yu-Wei Chang ◽  
Wei-Lun Chiang ◽  
Wen-Hung Wang ◽  
Chun-Yu Lin ◽  
Ling-Chien Hung ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Surveillance System ◽  
English Language ◽  
Google Trends ◽  
Excellent Correlation ◽  
Epidemic Disease ◽  
Web Social ◽  
Epidemic Diseases ◽  
The Web

BACKGROUND Web social media has identified to utilize as an epidemic outbreaks surveillance tool. However, the correlation between non-English language queries search data and epidemic diseases remains unclear. OBJECTIVE This study aimed to confirm the suitable non-English language keywords research relative intensities that were sensitive and specific to estimate the level of epidemic disease and the public opinion in non-English language country. Moreover, our approach indicated that a surveillance system based on Internet activity can be served an essential tool for detecting emerging diseases with distinct symptoms (e.g. zika virus fever in Brazil, 2015), and estimating the local epidemic diseases (e.g. enterovirus infectious disease in Taiwan, 2012). Otherwise, we further evaluated whether the social media reflected social uneasiness and fear during epidemic outbreaks and natural catastrophes. Our specific aim is to develop a suitable surveillance system for monitoring epidemic outbreak and observing related public opinion in the non-English language countries. METHODS The present study was based on freely available weekly epidemic incidence data from Taiwan Center for Disease Control, and the web search query data obtained from Google Trends between October 4, 2015, and April 2, 2016. To validate whether the non-English query keywords were the excellent surveillance tools, we estimated the correlation between the web query data and epidemic incidence in Taiwan. RESULTS Based on our approach, the total of 8 influenza-related queries was introduced to the analysis. The keywords, “感冒(common cold), 發燒(fever), and 咳嗽(cough)”, revealed good to excellent correlation between the Google Trends query data and influenza incidence (r= 0.89, P< 0.001; r= 0.77, p< 0.001; r= 0.79, p< 0.001, respectively). Moreover, those also displayed a high correlation with the influenza-like illness emergency and outpatient visits. We further found the query ”腸病毒 (enteroviruses)” in Google Trends, which showed excellent correlation with enterovirus infected patients in the emergency department (r= 0.91, p< 0.001). CONCLUSIONS This result suggested that Google Trends can serve as a good surveillance tool for epidemic outbreaks even in non-English language countries. Due to online search activity indicated people’s concerns for epidemic diseases even when they do not visit hospitals, it prompted us to develop the effectiveness of epidemic monitoring in web social media, which reflected the infectious trend more timeliness than traditional reporting system. In addition, the web queries data in suitable non-English search terms can provide more advantage information for medical education, healthcare, and disease prevention.

#TurkeyIsNotAChicken

2019 ◽  
Vol 12 (2) ◽  
pp. 166-184
Author(s):  
Bilge Yesil
Keyword(s):  
Public Opinion ◽  
Discourse Analysis ◽  
English Language ◽  
Security Measures ◽  
Political Event ◽  
Western Discourse ◽  
State Actors ◽  
Coup Attempt

Abstract In this article, I analyze pro-AKP actors’ grassroots communications in the immediate aftermath of the 2016 coup attempt. I explore the Twitter participation of non-state actors in this momentous political event, with the specific aim of shaping western audiences’ understanding of the failed coup and countering western criticism of post-coup security measures. I do not evaluate pro-AKP netizens’ Twitter communications in terms of their effectiveness in influencing western public opinion; instead I focus on the underlying anti-hegemonic and Occidentalist ideological positions. Through a discourse analysis of English-language Twitter posts, I argue that the engagement of non-state actors on behalf of the AKP government was not simply informed by nationalist mobilization, but rather by an Occidentalist exigency to invert the hegemonic western discourse about Turkey and Turks.

scholarly journals Forecasting the COVID-19 epidemic integrating symptom search behavior: an infodemiology study

2021 ◽  
Author(s):  
Alessandro Rabiolo ◽  
Eugenio Alladio ◽  
Esteban Morales ◽  
Andrew I McNaught ◽  
Francesco Bandello ◽  
...  
Keyword(s):  
Time Series ◽  
Web Application ◽  
Moving Average ◽  
Time Lag ◽  
Search Behavior ◽  
Google Trends ◽  
Optimal Time ◽  
Time Range ◽  
Prediction Ability ◽  
Time Series Modelling

ABSTRACTBackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions.MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal components analysis (PCA) and time series modelling. The app facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected data of eight countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (Error Trend Seasonality, Autoregressive integrated moving average, and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root-mean-square error (RMSE) of the first principal component (PC1). Predictive ability of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only.FindingsThe degree of correlation and the best time-lag varied as a function of the selected country and topic searched; in general, the optimal time-lag was within 15 days. Overall, predictions of PC1 based on both searched termed and COVID-19 traditional metrics performed better than those not including Google searches (median [IQR]: 1.43 [0.74-2.36] vs. 1.78 [0.95-2.88], respectively), but the improvement in prediction varied as a function of the selected country and timeframe. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median [IQR]: 0.74 [0.47-1.22] vs. 2.15 [1.55-3.89], respectively).InterpretationThe inclusion of digital online searches in statistical models may improve the prediction of the COVID-19 epidemic.FundingEOSCsecretariat.eu has received funding from the European Union’s Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.

Opinion Research and Publicness: (Meinungsforschung und Öffentlichkeit)

2005 ◽  
Vol 23 (1) ◽  
pp. 116-123 ◽  
Author(s):  
Theodor W. Adorno ◽  
Andrew J. Perrin ◽  
Lars Jarkko
Keyword(s):  
Public Opinion ◽  
Mass Media ◽  
American Academy ◽  
English Language ◽  
Language Translation ◽  
Dual Role ◽  
Sociological Research ◽  
Short Introduction ◽  
Sociological Practice

We present a short introduction to, and the first English language translation of, Theodor W. Adorno's 1964 article, “Meinungsforschung und Öffentlichkeit.” In this article, Adorno situates the misunderstanding of public opinion within a dialectic of elements of publicness itself: empirical publicness' dependence on a normative ideology of publicness, and modern publicness' tendency to undermine its own principles. He also locates it in the dual role of mass media as both fora for the expression of opinion and, as he calls them, ‘organs of public opinion.’ The introduction provides a discussion of Adorno's reception in the American academy, arguing that contemporary sociological practice should be concerned with the problems Adorno raises. We suggest that Adorno's relegation to the fields of philosophy and aesthetics belies his relevance to empirical sociological research.

scholarly journals Forecasting the COVID-19 Epidemic By Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study (Preprint)

2021 ◽  
Author(s):  
Alessandro Rabiolo ◽  
Eugenio Alladio ◽  
Esteban Morales ◽  
Andrew Ian McNaught ◽  
Francesco Bandello ◽  
...  
Keyword(s):  
Time Series ◽  
Predictive Models ◽  
Web Application ◽  
Moving Average ◽  
Time Lag ◽  
Search Behavior ◽  
Principal Component ◽  
Google Trends ◽  
Time Range ◽  
Prediction Ability

BACKGROUND Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. OBJECTIVE The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics. METHODS An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. RESULTS The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively). CONCLUSIONS The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

scholarly journals Idioms as a means of creating the image of China in the English language media discourse

2021 ◽  
Vol 21 (3) ◽  
pp. 254-260
Author(s):  
Tatyana P. Filichkina ◽  
Keyword(s):  
Public Opinion ◽  
Discourse Analysis ◽  
English Language ◽  
Cognitive Factors ◽  
Media Discourse ◽  
The Media

The article deals with the application of phraseological units in describing China in the English language media discourse. The evaluation in idioms is determined by means of discourse analysis which takes into account extra-linguistic, linguistic and cognitive factors. Subjective modalities specify the evaluative potential of idioms and show the mechanism of manipulating public opinion in the media discourse.

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