Given its relative infancy, there is a dearth of research on a comprehensive view of business social media analytics (SMA). This dissertation first examines current literature related to SMA and develops an integrated, unifying definition of business SMA, providing a nuanced starting point for future business SMA research. This dissertation identifies several benefits of business SMA, and elaborates on some of them, while presenting recent empirical evidence in support of foregoing observations. The dissertation also describes several challenges facing business SMA today, along with supporting evidence from the literature, some of which also offer mitigating solutions in particular contexts. The second part of this dissertation studies one SMA implication focusing on identifying social influencer. Growing social media usage, accompanied by explosive growth in SMA, has resulted in increasing interest in finding automated ways of discovering influencers in online social interactions. Beginning 2008, many variants of multiple basic approaches have been proposed. Yet, there is no comprehensive study investigating the relative efficacy of these methods in specific settings. This dissertation investigates and reports on the relative performance of multiple methods on Twitter datasets containing between them tens of thousands to hundreds of thousands of tweets. Accordingly, the second part of the dissertation helps further an understanding of business SMA and its many aspects, grounded in recent empirical work, and is a basis for further research and development. This dissertation provides a relatively comprehensive understanding of SMA and the implementation SMA in influencer identification.

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<div>Real-time social media data can provide useful information on evolving events and situations. In addition, various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. Informed by discussions with first responders and government officials, we focus on two major barriers limiting the widespread adoption of social media for situational awareness: the lack of geotagged data and the deluge of irrelevant information during events. Geotags are naturally useful, as they indicate the location of origin and provide geographic context. Only a small portion of social media is geotagged, however, limiting its practical use for situational awareness. The deluge of irrelevant data provides equal difficulties, impeding the effective identification of semantically relevant information. Existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time, limiting situational awareness. In this work, we first adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We then present a novel interactive learning framework in which users rapidly identify relevant data by iteratively correcting the relevance classification of tweets in real-time. We integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system adapted for real-time situational awareness.</div>

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects.

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects.

Den växande globala användningen av sociala medier skapar enorma mängder social data online, kallat för Big Social Data (BSD). Tidigare forskning lyfter problem med att BSD ofta har bristande tillförlitlighet som underlag vid beslutsfattande och att det är starkt kopplat till dataoch informationskvalitet. Det finns dock en avsaknad av forskning som redogör för praktikers perspektiv på detta. Därför undersökte denna studie vad som upplevs problematiskt kring transformation av BSD till tillförlitlig information för beslutsfattande i en organisationskontext, samt hur detta skiljer sig i teori jämfört med praktik. En fallstudie gjordes av mjukvaruföretaget SAS Institute (SAS). Datainsamlingen genomfördes via intervjuer samt insamling av dokument och resultaten analyserades kvalitativt. Studien gjorde många intressanta fynd gällande upplevda problem kopplat till transformation av BSD, bl.a. hög risk för partisk data och låg analysmognad, samt flera skillnader mellan teori och praktik. Tidigare forskning gör inte heller skillnad mellan begreppen datakvalitet och informationskvalitet, vilket görs i praktiken. / The growing use of social media generates enormous amounts of online social data, called Big Social Data (BSD). Previous research highlights problems with BSD reliability related to decision making, and that reliability is strongly connected to data quality and information quality. However, there is a lack of research with a focus on practitioners’ perspectives on this matter. To address this gap, this study set out to investigate what is perceived as a problem when transforming BSD into reliable information for decision making in an organisational context, and also how this differs in theory compared with practice. A case study was conducted of the software company SAS Institute (SAS). Data collection was done through interviews and gathering of documents, and results were analysed qualitatively. The study resulted in many interesting findings regarding perceived problems connected to the transformation of BSD, e.g. high risk of biased data and low maturity regarding data analysis, as well as several differences between theory and practice. Furthermore, previous research makes no distinction between the terms data quality and information quality, but this is done in practice.

The diploma thesis deals with the problem of using social networks in the field of car repair services. The primary aim is to provide the recommendations for businesses in a structured way on how to better utilize these networks to get better competitiveness. This work is divided into three parts. The first theoretical part deals with basic description of networks and their statistics. This part also covers some basic tools that we can use to analyze and maintain social network platforms. The second analytical part deals with competitive analysis in car repair services and gives an overview of the most popular Runet social network platforms. The third practical part provides structured view on all important aspects of social platform maintenance and behaviour. In this section, you can find the description of various views and the recommendations I give to the specific car repair service.

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