To explore the motivation and behavior of Facebook users when clicking the "Like" button, we analyzed the behaviors of 743 university student Facebook users using motivational theory and the theory of reasoned action. The main study findings were as follows: (a) hedonic motivation, utilitarian motivation, compliance motivation, conformity motivation, and affiliation motivation all had a positive impact on attitudes toward "Like"-clicking behaviors; (b) subjective norms and attitudes toward "Like"-clicking behaviors all had a positive impact on behavioral intention, and (c) behavioral intention had a positive impact on actual behaviors. These findings provide a valuable basis for constructing an explanatory model for "Like"-clicking behaviors of Facebook community platform users, as well as making significant practical contributions to enhance social and commercial benefits for businesses and individuals.

Facing the COVID-19 pandemic, Taiwan demonstrated resilience at the initial stage of epidemic prevention, and effectively slowed down its spread. This study aims to document public epidemic awareness of COVID-19 in Taiwan through collecting social media- and Internet-based data, and provide valuable experience of Taiwan’s response to COVID-19, involving citizens, news media, and the government, to aid the public in overcoming COVID-19, or infectious diseases that may emerge in the future. The volume of Google searches related to COVID-19 and face masks was regarded as an indicator of public epidemic awareness in the study. A time-series analysis was used to explore the relationships among public epidemic awareness and other COVID-19 relevant variables, which were collected based on big data analysis. Additionally, the content analysis was adopted to analyze the transmission of different types of fear information related to COVID-19 and their effects on the public. Our results indicate that public epidemic awareness was significantly correlated with the number of confirmed cases in Taiwan and the number of news reports on COVID-19 (correlation coefficient: .33–.56). Additionally, the findings from the content analysis suggested that the fear of the loss of control best explains why panic behavior occurs among the public. When confronting the highly infectious COVID-19, public epidemic awareness is vital. While fear is an inevitable result when an emerging infectious disease occurs, the government can convert resistance into assistance by understanding why fear arises and which fear factors cause excessive public panic. Moreover, in the digitalization era, online and social media activities could reflect public epidemic awareness that can e harnessed for epidemic control.

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BackgroundFacing the COVID-19 epidemic, Taiwan has demonstrated resilience at the initial stage of epidemic prevention and effectively slowed down its spread. This study aims to capture public epidemic awareness toward the COVID-19 through collecting social media- and Internet-based data and elaborate on how the public epidemic awareness rose and played a role in the epidemic prevention in Taiwan during the initial course of COVID-19 spread.MethodsUsing the Google search query volume of COVID-19 and face mask as indicators of public epidemic awareness, we collected the volume of news reports and the mentions on social media about COVID-19 and face masks between December 31, 2019, and February 29, 2020, through big data analysis and sorted the daily total confirmed cases of COVID-19 worldwide and in Taiwan as well as critical mask-related measures implemented by the Taiwanese government to plot the trends in this information and conduct correlation analysis. Additionally, the content analysis was adopted to analyze the transmission of different types of fear information of COVID-19 between December 31, 2019, and March 29, 2020, and their effects on the public.ResultsThe Google search query volume of COVID-19 and face mask was significantly correlated with the number of confirmed cases in Taiwan, the number of news reports on COVID-19 (correlation coefficient: .74–.90). Since the first confirmed cases of COVID-19, public epidemic awareness has increased rapidly, prompting the government to formulate relevant emergency measures. Additionally, the findings from content analysis suggested that the fear of the loss of control best explains why panic behavior occurs in public.ConclusionsConfronting the highly infectious COVID-19, public epidemic awareness is vital. While fear is an inevitable product when an emerging infectious disease occurs, the government can convert resistance into assistance by understanding why fear arises and which fear factors cause excessive panic in public. Moreover, online social media promptly reflect public epidemic awareness, which can be used as a reference for epidemic prevention; this urges the government to deal with the crisis in the form of public opinion.

This study is devoted to gain insight into a timely, accurate, and relevant combining forecast by considering social media (Facebook), opinion polls, and prediction markets. We transformed each type of raw data into the possibility of victory as a forecasting model. Besides the four single forecasts, namely Facebook fans, Facebook "people talking about this" (PTAT) statistics, opinion polls, and prediction markets, we generated three combined forecasts by associating various combinations of the four components. Then, we examined the predictive performance of each forecast on vote shares and the elected/nonelected outcome across the election period. Our findings, based on the evidence of Taiwan's 2018 county and city elections, showed that incorporating the Facebook PTAT statistic with polls and prediction markets generates the most powerful forecast. Moreover, we recognized the matter of the time horizons where the best proposed model has better accuracy gains in predictionin the "late of election," but not in "approaching election". The patterns of the trend of accuracy across time for each forecasting model also differ from one another. We also highlighted the complementarity of various types of data in the paper because each forecast makes important contributions to forecasting elections.