Off and Online Journalism and Corruption

This book provides a new theoretical framework of determinants that interact together in five hierarchical levels to restrain or produce corruption. The theory suggests a multilevel analysis that tests hypotheses regarding the relations of journalism and corruption within each level and across levels in international comparative research designs. Corruption as the abuse of power for private gain is built into the journalistic, economic, political, and cultural structures of any society and is affected by its interaction within the international system. The important questions of how differences in corruption across countries can be explained or what makes it more or less in a particular society and how press freedom and social media contribute to the fight against corruption are still unanswered. This book represents a significant contribution on the way to answer these critical questions. It discusses a variety of journalism-corruption experiences that provide a wealth of results and analyses. The cases it examines extend from Cuba to Algeria, India, Saudi Arabia, Sub-Saharan African, Gulf Cooperation Countries, Arab World, and Japan. The primary contribution of this book is both theoretical and empirical. Its details as well as the general theoretical frameworks make it a useful book for scholars, academics, undergraduate and graduate students, journalists, and policy makers

The expansion of isms, 1820-1917 : Data-driven analysis of political language in digitized newspaper collections

Words with the suffix -ism are reductionist terms that help us navigate complex social issues by using a simple one-word label for them. On the one hand, they are often associated with political ideologies, but on the other they are present in many other domains of language, especially culture, science, and religion.This has not always been the case. This paper studies isms in a historical record of digitized newspapers published from 1820 to 1917 in Finland to find out how the language of isms developed historically.We use diachronic word embeddings and affinity propagation clustering to trace how new isms entered the lexicon and how they relate to one another over time. We are able to show how they became more common and entered more and more domains. Still, the uses of isms as traditions for political action and thinking stand out in our analysisPeer reviewe

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA

Twitter and elections: are tweets, predictive, reactive, or a form of buzz?

The popular microblogging social media platform Twitter has been prominently covered in the press for its perceived role in activism, disaster recovery, and elections amongst other things. In the case of elections, Twitter has been used actively by candidates and voters alike in a diverse range of elections around the world including the 2010 UK elections, the 2012 US presidential elections, and the 2013 Italian elections. However, Twitter has often been found to be a poor predictor of electoral success. This article investigates what role tweets play during elections and whether they are more reactive than predictive. Using the specific case of the 2012 US Republican presidential primary elections, this article explores how candidate's Twitter presence affects electoral outcomes and whether the sentiment and frequency of candidate-related tweets is related to campaign success and offline success at the ballot box. This study finds that tweets were more reactive rather than predictive. Additionally, sentiment analysis revealed that tweets were generally neutral towards candidates. An interesting finding of our study is how candidates used Twitter to generate ‘buzz’, political capital that did not translate to success at the ballot box. We specifically explore how Huntsman's daughters used YouTube videos and tweets that were perceived as a ‘backstage’ look into the campaign and ultimately generated high levels of buzz. Though tweets do not seem to be reflective or predictive of an election campaign offline, they are being used for social media campaigns which can and do get covered by traditional media

Religious World-Denying and Trajectories of Activism in the Field of Strongly-Religious Corporative Actors

This comparative case study links together the scholarly discourses on religiously-motivated world-rejecting and religious activism. It provides empirical evidence for differentiation between four ideal-typed patterns of religious activism as they relate to different trajectories and spheres of religious world-denying in strongly religious movements: Pattern I (world conquerors) targets inner-worldly sphere of activism in the particular state, using the full scope of political tools to promote its religiously-fueled political agenda. Pattern II (world transformers) creatively combines inner-worldly and other-worldly spheres of activism, applying political strategies in the most advantageous political situations and primarily focusing on strategic missionary activism in “the world.” Pattern III (world creators) utilizes different forms of civic engagement to re-create “the righteous world” on the congregational level, but also participates in missionary activities. Finally, pattern IV (world renouncers) renounces any inner-worldly forms of political or civic engagement as “sinful activity.

Current landscape and influence of big data on finance

Big data is one of the most recent business and technical issues in the age of technology. Hundreds of millions of events occur every day. The financial field is deeply involved in the calculation of big data events. As a result, hundreds of millions of financial transactions occur in the financial world each day. Therefore, financial practitioners and analysts consider it an emerging issue of the data management and analytics of different financial products and services. Also, big data has significant impacts on financial products and services. Therefore, identifying the financial issues where big data has a significant influence is also an important issue to explore with the influences. Based on these concepts, the objective of this paper was to show the current landscape of finance dealing with big data, and also to show how big data influences different financial sectors, more specifically, its impact on financial markets, financial institutions, and the relationship with internet finance, financial management, internet credit service companies, fraud detection, risk analysis, financial application management, and so on. The connection between big data and financial-related components will be revealed in an exploratory literature review of secondary data sources. Since big data in the financial field is an extremely new concept, future research directions will be pointed out at the end of this study

Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences

Elections are among the most critical events in a national calendar. During elections, candidates increasingly use social media platforms to engage voters. Using the 2016 US presidential election as a case study, we looked at the use of Twitter by political campaigns and examined how the drivers of voter behaviour were reflected in Twitter. Social media analytics have been used to derive insights related to theoretical frameworks within political science. Using social media analytics, we investigated whether the nature of social media discussions have an impact on voting behaviour during an election, through acculturation of ideologies and polarization of voter preferences. Our findings indicate that discussions on Twitter could have polarized users significantly. Reasons behind such polarization were explored using Newman and Sheth's model of voter's choice behaviour. Geographical analysis of tweets, users, and campaigns suggests acculturation of ideologies among voting groups. Finally, network analysis among voters indicates that polarization may have occurred due to differences between the respective online campaigns. This study thus provides important and highly relevant insights into voter behaviour for the future management and governance of successful political campaigns.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog