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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.

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.

Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance.

Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided.

A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data.

This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.

This paper aims to examine tweet posts regarding Typhoon Washi to contend the usefulness of social media and big data as an aid of post-disaster management. Through topic modelling and content analysis, this study examines the priorities of the victims expressed in Twitter and how the priorities changed over a year.

Social media, particularly Twitter, was where the data gathered. Using big data technology, the gathered data were processed and analysed according to the objectives of the study. Topic modelling was used in clustering words from different topics. Clustered words were then used for content analysis in determining the needs of the victims. Word frequency count was also used in determining what words were repeatedly used during the course period. To validate the gathered data online, government documents were requested and concerned government agencies were also interviewed.

Findings of this study argue that housing and relief goods have been the top priorities of the victims. Victims are seeking relief goods, especially when they are in evacuation centres. Also, the lack of legal basis hinders government officials from integrating social media information unto policymaking.

This study only reports Twitter posts containing keywords either, Sendong, SendongPH, Washi or TyphoonWashi. The keywords were determined based on the words that trended after Typhoon Washi struck.

For social media and big data to be adoptable and efficacious, supporting and facilitating conditions are necessary. Structural, technical and financial support, as well as legal framework, should be in place. Maintaining and sustaining positive attitude towards it should be taken care of.

Although many studies have been conducted on the usefulness of social media in times of disaster, many of these focused on the use of social media as medium that can efficiently spread information, and little has been done on how the government can use both social media and big data in collecting and analysing the needs of the victims. This study fills those gaps in social big data literature.

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

The purpose of this paper was to examine how small- and medium-sized hospitality organisations engage with social media and how social media data are used by their managers to inform business decisions.

A qualitative approach was used in this research in which interviews were conducted with top management, comprising the owners/directors and other key managers from small- and medium-sized organisations based in Penang, Malaysia. Fan and Gordon's (2014) categorisation of the social media data analysis process and Simon's (1995) concept of the interactive and diagnostic usage of data were used in the analysis of data.

The managers of small- and medium-sized hospitality organisations engage with social media for customer relationship management and the understanding of key main competitors. Social media is used to understand, build and manage relationships with current and potential customers; these activities are also linked to actions taken to protect a company's reputation. Even though, for the companies concerned, data gathering is still at the capture stage with no formal procedures and processes in place, the data are utilised in an interactive way to inform two areas’ major business decisions-making, i.e. those related to pricing and promotion and the strategic formulation and reorientation of the business.

The respondents of this study were mainly from smaller hospitality organisations. Hence, the insights gained are limited to the context of smaller hotels.

A significant number of social media studies within the hospitality sector have focussed on marketing aspects. This study explored the wider use of social media in the case of smaller hospitality organisations and how they compete and position themselves in the competitive hospitality industry.

The purpose of this paper is to focus on measuring the contribution generated by social media when used for business purposes, distinguishing between metrics and methods for data collection and data analysis. Organizations worldwide have widely endorsed social media, but available studies on the contribution generated by these technologies for organizations are fragmented. A performance measurement system (PMS) framework to monitor social media is theoretically derived, highlighting the methods for data collection and data analysis and metrics to quantify social media impacts in terms of financials, network structure, interactions, conversations and users’ opinion.

This is a qualitative research based on a literature review of papers in management, information technology, marketing and public relations.

A PMS framework to quantify the contribution of social media is theoretically derived, distinguishing between metrics and methods. PMS metrics support the measurement of the financial and relational impact of social media, as well as the impact of social media conversations and users’ opinions. PMS methods comprise different approaches for data collection and data analysis that range from manual to automated data collection and from content to sentiment analysis techniques.

The PMS framework contributes to the academic literature by integrating a unique model of the available approaches for social media measurement that can serve as a basis for future research directions. The framework also supports practitioners that face necessity to quantify financial and relational contributions of social media as well as the contribution of social media conversation and users’ opinion.