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The purpose of this paper is to examine how the introduction of new communication channels facilitates interactive information sharing and collaboration between various actors over social networking services and how social networking fits in the existing European legal framework on data protection. The paper also aims to discuss some specific data protection issues, focusing on the role of the relevant actors, using the example of photo tagging.

Privacy in social networks is one of the main concerns for providers and users. This paper examines the role of the main actors in social networking, i.e. the providers and the users, scrutinised under the light of the European data protection legislation. Specifically, how social networking service providers deal with users' privacy and how users handle their personal information, if this manipulation is complied with the respective legislation and how “tagging”, one of the most familiar services provided by the social networking providers, may cause privacy risks.

Social networking is one of the most remarkable cultural phenomena that has blossomed in the Web 2.0 era. They enable the connection of users and they facilitate the exchange of information among them. However, the users reveal vast amounts of personal information over social networking services, without realising the privacy and security risks arising from their actions. The European data protection legislation could be used as a means for protecting the users against the unlawful processing of their personal information, although a number of problems arise regarding its applicability.

The paper discusses some privacy concerns involved in social networks and examines how social networking service providers and users deal with personal information with regard to the European data protection legislation.

This chapter critically discusses implications of working with ‘big data’ from the perspective of qualitative research and methodology. A critique is developed of the analytic troubles that come with integrating qualitative methodologies with ‘big data’ analyses and, moreover, the ways in which qualitative traditions themselves offer a challenge, as well as contributions, to computational social science.

The chapter draws on Interactionist understandings of social organisation as an ongoing production, tied to and accomplished in the actual practices of actual people. This is a matter of analytic priority but also points to a distinctiveness of sociological work which may be undermined in moving from the study of such actualities, suggesting an alternative coming crisis of empirical sociology.

A cautionary tale is offered regarding the contribution and character of sociological analysis within the ‘digital turn’. It is suggested that ‘big data’ analyses of traces abstracted from actual people and their practices not only miss and distort the relation of social practice to social product but, consequentially, can take on an ideological character.

The chapter offers an original contribution to current discussions and debates surrounding ‘big data’ by developing enduring critiques of sociological methodology and analysis. It concludes by pointing to contributions and interventions that such an empirical programme of qualitative research might make in the context of the ‘digital turn’ and is of value to those working at the interface of traditional and digital(ised) inquiries and methods.

To outline the current trajectories in digital social research and to highlight the roles of qualitative research in those trajectories.

A secondary analysis of the primary literature.

Qualitative research has shifted over time in relation to rapidly changing digital phenomena, but arguably finds itself in ‘crisis’ when faced with algorithms and ubiquitous digital data. However, there are many highly significant qualitative approaches that are being pursued and have the potential to contextualize, situate and critique narratives and practices of data.

To situate current debates around methods within longer trajectories of digital social research, recognizing their conceptual, disciplinary and empirical commitments.

Social media platforms, along with networked devices and applications, enable their user base to produce, access and circulate large volumes of data. On the one hand, this development contains an empowering potential for users, who can make otherwise obscured aspects of social life visible, and coordinate social action in accordance. Yet the preceding activities in turn render these users visible to governments as well as the multinational companies that operate these services. Between these two visions lie more nuanced accounts of individuals coordinating via social data for reactionary purposes, as well as policing and intelligence agencies struggling with the affordances of big data.

This chapter considers how individual users as well as police agencies respectively actualise the supposedly revolutionary and repressive potentials associated with big data. It briefly considers the broader social context in which ‘big data’ is situated, which includes the hardware, software, individuals and cultural values that render big data meaningful and useful. Then, in contrast to polarising visions of the social impact of big data, it considers two sets of practices that speak to a more ambivalent potentiality. First, recent examples suggest a kind of crowd-sourced vigilantism, where individuals rely on ubiquitous data and devices in order to reproduce law and order politics. Second, police agencies in various branches of European governments report a sense of obligation to turn to social data as a source of intelligence and evidence, yet attempts to do so are complicated by both practical and procedural challenges. A combination of case studies and in-depth interviews offers a grounded understanding of big data in practice, in contrast to commonly held visions of these technologies.

First, big data is only ever meaningful in use. While they may be contained in databases in remote locations, big data do not exist in a social vacuum. Their impact cannot be fully understood in the context of newly assembled configurations or ‘game-changing’ discourses. Instead, they are only knowable in the context of existing practices. These practices can initially be the sole remit of public discourse shaped by journalists, tech-evangelists and even academics. Yet embodied individual and institutional practices also emerge, and this may contradict or at least complicate discursive assertions. Secondly, the range of devices and practices that make up big data are engaged in a bilateral relation with these practices. They may be a platform to further reproduce relations of information exchange and power relations. Yet they may also reconfigure these relations.

This research is limited to a sample of respondents based in the European Union, and based at a particular stage of big data and social media monitoring uptake. Subsequent research should look at how this uptake is occurring elsewhere, along with the medium to long-term implications of big data monitoring. Finally, subsequent research should consider how citizens and other social actors are coping with these emerging practices.

This chapter considers practices associated with big data monitoring and draws from cross-national empirical data. It stands in contrast to overly optimistic as well as well as totalising accounts of the social costs and consequences of big data. For these reasons, this chapter will be of value to scholars in internet studies, as well as privacy advocates and policymakers who are responsive to big data developments.

The chapter will review significant changes in information technology (IT) affecting research over the 30-year history of Communication, Information Technology, and Media Sociology. It compares broad overviews of computers and the social sciences published shortly after the beginning of the section (1989 and 1990) with a contemporary overview of online research methods from 2017. It also draws on my own experiences from 1981 to the present as both an academic and a software entrepreneur. The author will discuss how changes in the section parallel developments in social science computing over this period, identifying some of the significant ways IT has transformed both the methods of research and the substantive foci of research. Finally, the author extrapolates into the future to consider how continuing changes in the Internet, big data, artificial intelligence, and natural language understanding may change how sociological research is conducted in the foreseeable future.

Organization development is often mourned as stagnant or perhaps dead, but most of these declarations seem to be insular, being supported primarily by anecdotal or survey research among organization development scholars and practitioners. This exploratory study seeks a more objective understanding of the state of organization development by examining big data from the social media platform Twitter. Drawn from over 5.7 million tweets extracted through Twitter's Application Program Interface (API) during 2 months in 2018, this research approaches the state of organization development through a quantitative, abductive study utilizing social network analyses. Organization development is examined through its characteristics as a social network on Twitter and how it relates to and interacts with other familial networks from management and organization studies. Findings show that organization development is relatively inactive as a social network on Twitter, as compared to other familial networks, and the relationships between the organization development network and these familial networks tend to be ones of inequality. Organization development references familial networks much more than any of the familial networks reference organization development. This inequality in social media presence is particularly surprising since several of these familial networks were founded from the field and principles of organization development. We locate organization development's generalist status, as compared to familial networks' specialist status, as generating this interaction disparity drawing on recent research that suggests specialized fields fare better in times of rapid change compared to generalist fields. We discuss the potential for greater specialization of organization development with a reemphasis on its process philosophy and focus.

The present research aimed to identify the motivations, needs, wants, preferences and limitations of corporate professionals with regard to business social analytics.

Online interviews were conducted with 26 professionals the majority of whom work at the management level at 20 reputable corporations in Turkey. Both qualitative and quantitative data was collected during these interviews, which lasted an average of one hour.

The findings shed light on the motivations of corporate professionals for monitoring social media and other digital media, their perceived capability and limitations in doing so, the media that they monitor and wanted to monitor if possible, their criteria and processes for working with service providers in the field of business social analytics, their needs which are not fully met by service providers, their suggestions on service improvement and their reflections on how internal and external customer data can be analyzed with an integrated approach.

This research is an attempt to bridge the gap between the priorities of engineers who generate artificial intelligence for the purposes of social listening and analytics and the end users, e.g. corporate communication professionals. Only by doing so, this field, which is getting more and more important as people spend more time online, will reach its full potential and benefit corporations by providing fruitful insight upon which strategic steps can be taken.

This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources.

The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques.

The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective.

The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field.

Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories.

The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.

Social media analytics uses data mining platforms, tools and analytics techniques to collect, monitor and analyze massive amounts of social media data to extract useful patterns, gain insight into market requirements and enhance business intelligence. The purpose of this paper is to propose a framework for social media competitive intelligence to enhance business value and market intelligence.

The authors conducted a case study to collect and analyze a data set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco in the past three months during December 1, 2014-February 28, 2015.

The results of the case study revealed the value of analyzing social media mentions and conducting sentiment analysis and comparison on individual product level. In addition to analyzing the social media data-at-rest, the proposed framework and the case study results also indicate that there is a strong need for creating a social media data application that can conduct real-time social media competitive intelligence for social media data-in-motion.

So far there is little research to guide businesses for social media competitive intelligence. This paper proposes a novel framework for social media competitive intelligence to illustrate how organizations can leverage social media analytics to enhance business value through a case study.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.