Facebook drives behavior of passive households in stock markets

Recent studies using data on social media and stock markets have mainly focused on predicting stock returns. Instead of predicting stock price movements, we examine the relation between Facebook data and investors' decision making in stock markets with a unique data on investors' transactions on Nokia. We find that the decisions to buy versus sell are associated with Facebook data especially for passive households and also for nonprofit organizations. At the same time, it seems that more sophisticated investors---financial and insurance institutions---are behaving independently from Facebook activities.Comment: This paper is forthcoming in Finance Research Letter

Social Media Monitoring of the Campaigns for the 2013 German Bundestag Elections on Facebook and Twitter

As more and more people use social media to communicate their view and perception of elections, researchers have increasingly been collecting and analyzing data from social media platforms. Our research focuses on social media communication related to the 2013 election of the German parliament [translation: Bundestagswahl 2013]. We constructed several social media datasets using data from Facebook and Twitter. First, we identified the most relevant candidates (n=2,346) and checked whether they maintained social media accounts. The Facebook data was collected in November 2013 for the period of January 2009 to October 2013. On Facebook we identified 1,408 Facebook walls containing approximately 469,000 posts. Twitter data was collected between June and December 2013 finishing with the constitution of the government. On Twitter we identified 1,009 candidates and 76 other agents, for example, journalists. We estimated the number of relevant tweets to exceed eight million for the period from July 27 to September 27 alone. In this document we summarize past research in the literature, discuss possibilities for research with our data set, explain the data collection procedures, and provide a description of the data and a discussion of issues for archiving and dissemination of social media data

Towards a Set Theoretical Approach to Big Data Analytics

Abstract—Formal methods, models and tools for social big data analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by relational sociology. There are no other unified modeling approaches to social big data that integrate the conceptual, formal and software realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on set theory and discuss the semantics of the formal model with a real-world social data example from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth and last, based on the formal model and sentiment analysis of text, we present a method for profiling of artifacts and actors and apply this technique to the data analysis of big social data collected from Facebook page of the fast fashion company, H&M

From digital positivism and administrative big data analytics towards critical digital and social media research!

This essay argues for a paradigm shift in the study of the Internet and digital/social media. Big data analytics is the dominant paradigm. It receives large amounts of funding, is administrative and a form of digital positivism. Critical social media research is an alternative approach that combines critical social media theory, critical digital methods and critical-realist social media research ethics. Strengthening the second approach is a material question of power in academia

Identification & Role of Implicit Social Ties from Social Data

The concept of social ties was introduced by Granovetter through the seminal paper titled" Strength of weak ties". Across the past five decades, this topic has attracted much attention from both academics and practitioners. In the past two decades, the rapid increase in digitization and new modes of communication have led to collecting and analyzing data about people. One of the most popular sources for such large and granular data about people is social media platforms. The rise in the popularity of social media in the past 15 years has resulted in many research studies that have used social media data to understand a lot of different phenomena. Some of this research has focused on using social data, including social media data, on identifying different kinds of social ties online and the role these social ties play in various contexts. Over the past decade, many different approaches and models have been built to identify social ties using social media data. These methods have been built using private data and explicit social relationship data of users’ social media platforms. However, in the past few years, it has become nearly impossible to access this kind of social media data due to the changes in the business models of the social media platforms and the introduction of new privacy laws like GDPR. This thesis aims to identify the social ties from publicly available social data and study the role of the identified social ties in different contexts like business conferences and business phenomena. In order to achieve this research objective, three separate studies were conducted. The first two studies were single-case case studies, while the third was an experiment where two different sets of hypotheses were tested using empirical data. All three studies used publicly available social media data related to a specific context. The first study used a large dataset related to a game developer community on Facebook. The second study used social media data related to a business event from Twitter and Facebook. The third study used a large dataset associated with social media data about crowdfunding projects from Twitter. This study adds to the existing literature related to identifying social ties from social media data in multiple manners. The thesis illustrates a novel approach based on reciprocal interaction for filtering relevant social ties from large publicly available social media data. The thesis also contributes to the understanding of the role multiple social media platforms play in an event. Thus, showing the impact this can have on identifying social ties from publicly available social media data in case of an event. The dissertation adds to the existing literature about the role social ties have towards crowdfunding success. The thesis shows that implicit social ties, in general, positively impact crowdfunding project success. In addition, the thesis has practical implications for designers of conference recommendation systems. The dissertation also has implications for the crowdfunding project owners and the crowdfunding project campaign designers

Iniciativas científicas de arquivamento e preservação de conteúdos em mídias sociais: panorama atual

The study carries out a mapping of scientific experiments that collect and archive social media content. It presents initially a brief theoretical reference related to digital preservation and Web 2.0. Qualitative research with an exploratory approach since it sought to identify in the innovative experiences questions little investigated, such as the characteristics: technological (social media platform used, technological solution used to collect the data and format of storage of the collected data); and contextual checks to see if the metadata guarantees provenance, authenticity, and how rights management and types of data access are. The results indicate that, although the focus of these initiatives is not directly on preservation, this issue is intrinsically considered. Twitter is the primary platform for data collection used by researchers. Several are the challenges listed, it is necessary to specify how and by whom the data collected will be used and relevant should be the curatorial action in these projects, to select what should in fact be saved. Finally, the restrictions of social media platforms and the lack of transparency and understanding about their data management practices currently make it difficult to preserve this type of content.El estudio realiza un mapeo de experiencias científicas que realizan recolección y archivo de contenidos de medios sociales. Presenta inicialmente un breve referencial teórico relacionado a lapreservación digital y Web 2.0. Investigación de abordaje cualitativo y de cuño exploratorio ya que buscó identificar en las experiencias innovadoras cuestiones poco investigadas, tales como las características: tecnológicas (plataforma de mídia social utilizada, solución tecnológica utilizada para la recolección de los datos y formato de almacenamiento de los datos recolectados); y contextuales verificando si los metadatos garantizan procedencia, autenticidad y cómo es la gestión de derechos y tipos de acceso a los datos. Los resultados apuntan que, aunque el foco principal de estas iniciativas no es directamente la preservación, esta cuestión está intrínsecamente contemplada. Twitter es la plataforma principal de recolección de datos utilizada por los investigadores. Varios son los desafíos enumerados, es preciso especificar cómo y por quién los datos recolectados serán utilizados y relevante debe ser la actuación curatorial en estos proyectos, con el objetivo de seleccionar lo que de hecho debe ser guardado. Por último, las restricciones de las plataformas de medios sociales y la falta de transparencia y comprensión sobre sus prácticas de gestión de datos actualmente dificultan la preservación de este tipo de contenido.O estudo realiza um mapeamento de experiências científicas que realizam coleta e arquivamento de conteúdos de mídias sociais. Apresenta inicialmente um breve referencial teórico relacionado à preservação digital e Web 2.0. Pesquisa de abordagem qualitativa e de cunho exploratório uma vez que buscou identificar nas experiências inovadoras questões pouco investigadas, tais como as características: tecnológicas (plataforma de mídia social utilizada, solução tecnológica utilizada para a coleta dos dados e formato de armazenamento dos dados coletados); e contextuais verificando se os metadados garantem proveniência, autenticidade e como é a gestão de direitos e tipos de acesso aos dados. Os resultados apontam que, embora o foco principal destas iniciativas não seja diretamente a preservação, esta questão está intrinsecamente contemplada. O Twitter é a plataforma principal de coleta de dados utilizada por pesquisadores. Vários são os desafios elencados, é preciso especificar como e por quem os dados coletados serão utilizados e relevante deve ser a atuação curatorial nestes projetos, visando selecionar o que de fato deve ser guardado. Por fim, as restrições das plataformas de mídias sociais e a falta de transparência e compreensão sobre suas práticas de gestão de dados atualmente dificultam a preservação deste tipo de conteúdo