Measuring the Impact of Online Media on Consumers, Businesses and Society (Sustainable Management, Wertschöpfung und Effizienz)

This empirical thesis analyses the impact of sentiments in online media on consumers, businesses, and society as a whole, and how knowledge of these correlations can be used in a variety of applications. The results show that the sentiment data can be employed in a variety of ways, functioning as an interesting new explanatory variable to complement and approximate survey data in areas such as tourism demand, consumer confidence, and many more. In particular, the cross-country sentiment analysis reveals compelling information on media biases, the reporting on alternative truths, and countries as a filter bubble. In addition to quantitative comparisons, the descriptive statistics reveal important information on the sentiment developments across countries. While this research is able to provide interesting findings for real-world applications for consumers, businesses, and society, the awareness of a media landscape that is heavily and increasingly dominated by negative news is particularly striking. Thus, in addition to the actual applications, above all, the thesis shows the media landscape in which everyone must act in the future. Acknowledgements Abstract Introductory Remarks Contents Abbreviations List of Figures List of Tables Part I State of the Field 1 Approaching Media Influence and its Mechanisms 1.1 Agenda Setting, Biases, and Tendentious and Fake News in the Media 1.2 The Influence of Media Reporting on Public Opinions 1.3 Media Influence on Consumers and Customers 1.4 Media Influence on Businesses 2 Connections and Relationships in Europe and Worldwide and their Impact 2.1 Social Relations Regarding the Movement of Goods, Services, and Capital and Potential Effects 2.2 Connections within the Movement of Labour and Potential Implications 2.3 Relationships Measured by Support of the EU by its Citizens 2.4 Media Influence on International Social Relationships 3 Approaching Tourism as a Driver for Research on International Relations 3.1 Current Tourism Demand Forecasting Techniques 3.2 Tourism Demand Forecasting in Online and Social Media 3.3 Machine Learning and Artificial Neural Networks in Tourism 4 Sentiment Analysis as a New Source of Information 4.1 Forecasting, Nowcasting, and Hidden Correlations Explored with Sentiment Analysis 4.2 Sentiment Analysis to Reveal and Explore Macroeconomic Phenomena 4.3 Sentiment Analysis in the Context of Business Administration 5 Technical Aspects of Sentiment Analysis of Online News Sources 5.1 Large-scale Retrieval of Big Data 5.2 Different Approaches of Computer-Based Sentiment Analysis 5.3 Insights into a Deeper Understanding of the Underlying Texts and Algorithms Part II Research on Online Media and the Impact on Consumers, Businesses and Society 6 Methodology and Research Design 6.1 Information Retrieval 6.2 Artificial Neural Network Sentiment Analysis 6.3 Sentiment Index Building 6.4 Correlation and Regression Analysis 7 Case Studies on the Online Tourism Marketplace 7.1 Study on the Impact of German-Speaking Online Media on Tourist Arrivals in Popular Tourist Destinations for Europeans 7.1.1 Methodological Aspects 7.1.2 Main Findings, Descriptive Analysis and Interpretation of the Results 7.1.3 Summary and Conclusions 7.2 Research on Artificial Neural Network-Based Emotion Classification in Online Media for Tourism Businesses 7.2.1 Methodology: Topic and Term Classification 7.2.2 Results 7.2.3 Summary and Conclusions 8 Cross-Country Perception Analysis Case Studies 8.1 Estimating Perceptions of Consumers Using AI: A Case Study of German and UK Travellers 8.1.1 Methodological Remarks 8.1.2 Main Findings and Discussions 8.1.3 Concluding Remarks 8.2 Investigating Differences in Travellers’ Perceptions of European Tourist Destinations Estimated by an LSTM Neural Network 8.2.1 Methodology 8.2.2 Interpretation of the Results 8.2.3 Summary and Conclusions of the Case Study 9 Media Bias Case Studies 9.1 Analysis of How Low Media Coverage Drives a Biased Picture in the News (and the Contrary) 9.1.1 Methodological Aspects 9.1.2 Descriptive Analysis and Interpretation of the Findings 9.1.3 Summary and Final Remarks 9.2 Investigating Biases in the Media Reporting Across Countries and their Implications for Businesses 9.2.1 Methodology 9.2.2 Main Findings 9.2.3 Summary and Conclusions of the Case Study 10 Case Studies on Media Reporting and Media Influence During the COVID-19 Pandemic 10.1 Analysis of the COVID-19 Mass Media Infodemic in Six European Countries 10.1.1 Methodology 10.1.2 Descriptive Statistics and Main Findings 10.1.3 Conclusions and Implications 10.2 Analysis of E-Commerce Popularity During the COVID-19 Pandemic 10.2.1 Methodology to Determine E-Commerce Popularity 10.2.2 Main Findings 10.2.3 Discussion and Conclusions of the Case Study 11 Development of a Model to Measure EU Friendliness and Rejection 11.1 The Context for Creation of the Index 11.2 Design and Generation of the Models 11.3 Test of the Index Benchmark Indicators 11.4 Results and Implications Final Concluding Remarks Research Contributions References / Bibliography