Within a context of recent and rapid transformation in authorship and participation practices on the Internet, this thesis explores the implications of an emerging digital culture for heritage institutions, such as museums and archives. Combining insights from internet, education and museum theory it explores different experiences of participation and meaning making around digital heritage collections opened to public engagement and contribution. In particular, the investigation analyses and contrasts the online activities of the Royal Commission on the Ancient and Historical Monuments of Scotland (RCAHMS), partner in the research, with alternative approaches. The thesis applies ethnographic research methods to investigate embodied and virtual settings. Based on the empirical findings, it identifies different theoretical models of online engagement with heritage content. It then extrapolates from these models a conceptual framework that could be used by heritage institutions to analyse and re-assess their online practices, intellectual positioning and strategic ambitions in the context of the paradigm shift brought about by digitality.

<p>The purpose of this thesis is to show what advantages and opportunities social media offer for B2B-companies (business-to-business), and how they can be used for marketing communications.</p><p>We have studied how companies can use social media, for example evolving the organization, developing products/services, staying up-to-date, building relations, creating customer loyalty and engagement, building the company’s identity and image, and affecting the buying decisions. We have also studied how B2B-companies can choose media channels, set goals, and measure the results.</p><p>We have carried out interviews, through phone and visits, with four chosen companies that fit our criterias: Swedish B2B-company that is successful in social media.</p><p>The result of the interviews show that B2B-companies use social media for various purposes, for example networking (to create and build long term relations with other companies) and to improve the company’s image among the end customers. Most of our interviewees reach their end customers (consumers) in first place and customers (companies) in second place, since many B2B-companies do not yet use social media.</p><p>Half of our respondents mention that social media should not be used for advertising and campaigns (one way communication). Instead they should be used for building relations and conversations (two way communication).</p><p>The majority of our respondents believe that it is essential to participate in the social channels where their consumers are, and let the target group control the choice of the channels. By being next to the consumers, a relationship will be formed that will lead to an increased loyalty. Three of the interviewed companies have noticed a growth in demand and sales, and a better recognition of the company.</p><p>Most of our respondents are strongly convinced that their presence in social media has an effect on the customers’ buying decisions, though they cannot statistically prove it. They do however see that the preference that individuals form around the brand have a significant meaning.</p><p>All of the companies that we interviewed state that they use both quantitative and qualitative measurements to find out what effect their activities in social media have on sales, commercial benefits, and what subjects that will raise interest and engagement. From one extreme to another, one of the companies can directly see, with help of statistics, if the activities in social media are increasing the sales, while another company claims that it is impossible to jump to conclusions about that connection, since there are many things that affect the customers’ buying decisions.</p>

No description available.

Image indexing research today is either conducted on a visual attribute level or on a higher semantic level, forming a semantic gap between the two. There is much to gain if research progress from the two fields is combined. Image retrieval using access points in both visual and semantic significations could improve retrieval and bridge the gap. In social media today, images are often the primary communication agent and the number of images on the web is increasing in an uncontrolled way. New and efficient ways to index and retrieve the images are needed.The purpose of this study is to examine if emotions could be a semantic access point for image retrieval and if folksonomy indexing is useful when searching for images that represent emotions. Images are retrieved from Flickr and Sara Shatford’s matrix for image indexing is used to classify image tags into categories.The result shows that for some emotions it is useful and there is a clear pattern in the retrieved relevant images. For other emotions there are a lot of images that have been tagged on a cluster of images and all images in the cluster is not relevant. Therefore the search result is ambiguous.An interesting observation is that index words expressing abstractions and feelings are more common in folksonomies compared to professional indexers. For specific web image collections where searches could be conducted on feelings, folksonomies is a successful method for the indexing and retrieval of images.

The purpose of this thesis is to show what advantages and opportunities social media offer for B2B-companies (business-to-business), and how they can be used for marketing communications. We have studied how companies can use social media, for example evolving the organization, developing products/services, staying up-to-date, building relations, creating customer loyalty and engagement, building the company’s identity and image, and affecting the buying decisions. We have also studied how B2B-companies can choose media channels, set goals, and measure the results. We have carried out interviews, through phone and visits, with four chosen companies that fit our criterias: Swedish B2B-company that is successful in social media. The result of the interviews show that B2B-companies use social media for various purposes, for example networking (to create and build long term relations with other companies) and to improve the company’s image among the end customers. Most of our interviewees reach their end customers (consumers) in first place and customers (companies) in second place, since many B2B-companies do not yet use social media. Half of our respondents mention that social media should not be used for advertising and campaigns (one way communication). Instead they should be used for building relations and conversations (two way communication). The majority of our respondents believe that it is essential to participate in the social channels where their consumers are, and let the target group control the choice of the channels. By being next to the consumers, a relationship will be formed that will lead to an increased loyalty. Three of the interviewed companies have noticed a growth in demand and sales, and a better recognition of the company. Most of our respondents are strongly convinced that their presence in social media has an effect on the customers’ buying decisions, though they cannot statistically prove it. They do however see that the preference that individuals form around the brand have a significant meaning. All of the companies that we interviewed state that they use both quantitative and qualitative measurements to find out what effect their activities in social media have on sales, commercial benefits, and what subjects that will raise interest and engagement. From one extreme to another, one of the companies can directly see, with help of statistics, if the activities in social media are increasing the sales, while another company claims that it is impossible to jump to conclusions about that connection, since there are many things that affect the customers’ buying decisions.

The aim of this work is to create an application on Windows Mobile 6 platform for photo publication on the internet. Photos will be associated with geographic information. User will be able to attach geographic information to the photograph with use of connected GPS device and subsequently send these photographs to online publication system directly from mobile device.

Die vorliegende Masterarbeit behandelt den Entwurf und die exemplarische Umsetzung eines Arbeitsablaufs zur Aufbereitung von georeferenziertem Microblogging Content. Als beispielhafte Datenquelle wurde Twitter herangezogen. Darauf basierend, wurden Überlegungen angestellt, welche Arbeitsschritte nötig und mit welchen Mitteln sie am besten realisiert werden können. Dabei zeigte sich, dass eine ganze Reihe von Bausteinen aus dem Bereich des Data Mining und des Text Mining für eine Pipeline bereits vorhanden sind und diese zum Teil nur noch mit den richtigen Einstellungen aneinandergereiht werden müssen. Zwar kann eine logische Reihenfolge definiert werden, aber weitere Anpassungen auf die Fragestellung und die verwendeten Daten können notwendig sein. Unterstützt wird dieser Prozess durch verschiedenen Visualisierungen mittels Histogrammen, Wortwolken und Kartendarstellungen. So kann neues Wissen entdeckt und nach und nach die Parametrisierung der Schritte gemäß den Prinzipien des Geovisual Analytics verfeinert werden. Für eine exemplarische Umsetzung wurde nach der Betrachtung verschiedener Softwareprodukte die für statistische Anwendungen optimierte Programmiersprache R ausgewählt. Abschließend wurden die Software mit Daten von Twitter und Flickr evaluiert. / This Master's Thesis deals with the conception and exemplary implementation of a workflow for georeferenced Microblogging Content. Data from Twitter is used as an example and as a starting point to think about how to build that workflow. In the field of Data Mining and Text Mining, there was found a whole range of useful software modules that already exist. Mostly, they only need to get lined up to a process pipeline using appropriate preferences. Although a logical order can be defined, further adjustments according to the research question and the data are required. The process is supported by different forms of visualizations such as histograms, tag clouds and maps. This way new knowledge can be discovered and the options for the preparation can be improved. This way of knowledge discovery is already known as Geovisual Analytics. After a review of multiple existing software tools, the programming language R is used to implement the workflow as this language is optimized for solving statistical problems. Finally, the workflow has been tested using data from Twitter and Flickr.

In recent years emotion-related applications like smartphone apps that document and analyse the emotions of the user, have become very popular. But research also can deal with human emotions in a very technology-driven approach. Thus space-related emotions are of interest as well which can be visualised cartographically and can be captured in different ways. The research project of this dissertation deals with the extraction of georeferenced emotions from the written language in the metadata of Flickr and Panoramio photos, thus from user-generated content, as well as with their modelling and visualisation. Motivation is the integration of an emotional component into location-based services for tourism since only factual information is considered thus far although places have an emotional impact. The metadata of those user-generated photos contain descriptions of the place that is depicted within the respective picture. The words used have affective connotations which are determined with the help of emotional word lists. The emotion that is associated with the particular word in the word list is described on the basis of the two dimensions ‘valence’ and ‘arousal’. Together with the coordinates of the respective photo, the extracted emotion forms a georeferenced emotion. The algorithm that was developed for the extraction of these emotions applies different approaches from the field of computer linguistics and considers grammatical special cases like the amplification or negation of words. The algorithm was applied to a dataset of Flickr and Panoramio photos of Dresden (Germany). The results are an emotional characterisation of space which makes it possible to assess and investigate specific features of georeferenced emotions. These features are especially related to the temporal dependence and the temporal reference of emotions on one hand; on the other hand collectively and individually perceived emotions have to be distinguished. As a consequence, a place does not necessarily have to be connected with merely one emotion but possibly also with several. The analysis was carried out with the help of different cartographic visualisations. The temporal occurrence of georeferenced emotions was examined detailed. Hence the dissertation focuses on fundamental research into the extraction of space-related emotions from georeferenced user-generated content as well as their visualisation. However as an outlook, further research questions and core themes are identified which arose during the investigations. This shows that this subject is far from being exhausted. / In den letzten Jahren sind emotionsbezogene Anwendungen, wie Apps, die die Emotionen des Nutzers dokumentieren und analysieren, sehr populär geworden. Ebenfalls in der Forschung sind Emotionen in einem sehr technologiegetriebenen Ansatz ein Thema. So auch ortsbezogene Emotionen, die sich somit kartographisch darstellen lassen und auf verschiedene Art und Weisen gewonnen werden können. Das Forschungsvorhaben der Dissertation befasst sich mit der Extraktion von georeferenzierten Emotionen aus geschriebener Sprache unter Verwendung von Metadaten verorteter Flickr- und Panoramio-Fotos, d.h. aus nutzergenerierten Inhalten, sowie deren Modellierung und Visualisierung. Motivation hierfür ist die Einbindung einer emotionalen Komponente in ortsbasierte touristische Dienste, da diese bisher nur faktische Informationen berücksichtigen, obwohl Orte durchaus eine emotionale Wirkung haben. Die Metadaten dieser nutzergenerierten Inhalte stellen Beschreibungen des auf dem Foto festgehaltenen Ortes dar. Die dafür verwendeten Wörter besitzen affektive Konnotationen, welche mit Hilfe emotionaler Wortlisten ermittelt werden. Die Emotion, die mit dem jeweiligen Wort in der Wortliste assoziiert wird, wird anhand der zwei Dimensionen Valenz und Erregung beschrieben. Die extrahierten Emotionen bilden zusammen mit der geographischen Koordinate des jeweiligen Fotos eine georeferenzierte Emotion. Der zur Extraktion dieser Emotionen entwickelte Algorithmus bringt verschiedene Ansätze aus dem Bereich der Computerlinguistik zum Einsatz und berücksichtigt ebenso grammatikalische Sonderfälle, wie Intensivierung oder Negation von Wörtern. Der Algorithmus wurde auf einen Datensatz von Flickr- und Panoramio-Fotos von Dresden angewendet. Die Ergebnisse stellen eine emotionale Raumcharakterisierung dar und ermöglichen es, spezifische Eigenschaften verorteter Emotionen festzustellen und zu untersuchen. Diese Eigenschaften beziehen sich sowohl auf die zeitliche Abhängigkeit und den zeitlichen Bezug von Emotionen, als auch darauf, dass zwischen kollektiv und individuell wahrgenommenen Emotionen unterschieden werden muss. Das bedeutet, dass ein Ort nicht nur mit einer Emotion verbunden sein muss, sondern möglicherweise auch mit mehreren. Die Auswertung erfolgte mithilfe verschiedener kartographischer Visualisierungen. Eingehender wurde das zeitliche Auftreten der ortsbezogenen Emotionen untersucht. Der Fokus der Dissertation liegt somit auf der Grundlagenforschung zur Extraktion verorteter Emotionen aus georeferenzierten nutzergenerierten Inhalten sowie deren Visualisierung. Im Ausblick werden jedoch weitere Fragestellungen und Schwerpunkte genannt, die sich im Laufe der Untersuchungen ergeben haben, womit gezeigt wird, dass dieses Forschungsgebiet bei Weitem noch nicht ausgeschöpft ist.

In recent years emotion-related applications like smartphone apps that document and analyse the emotions of the user, have become very popular. But research also can deal with human emotions in a very technology-driven approach. Thus space-related emotions are of interest as well which can be visualised cartographically and can be captured in different ways. The research project of this dissertation deals with the extraction of georeferenced emotions from the written language in the metadata of Flickr and Panoramio photos, thus from user-generated content, as well as with their modelling and visualisation. Motivation is the integration of an emotional component into location-based services for tourism since only factual information is considered thus far although places have an emotional impact. The metadata of those user-generated photos contain descriptions of the place that is depicted within the respective picture. The words used have affective connotations which are determined with the help of emotional word lists. The emotion that is associated with the particular word in the word list is described on the basis of the two dimensions ‘valence’ and ‘arousal’. Together with the coordinates of the respective photo, the extracted emotion forms a georeferenced emotion. The algorithm that was developed for the extraction of these emotions applies different approaches from the field of computer linguistics and considers grammatical special cases like the amplification or negation of words. The algorithm was applied to a dataset of Flickr and Panoramio photos of Dresden (Germany). The results are an emotional characterisation of space which makes it possible to assess and investigate specific features of georeferenced emotions. These features are especially related to the temporal dependence and the temporal reference of emotions on one hand; on the other hand collectively and individually perceived emotions have to be distinguished. As a consequence, a place does not necessarily have to be connected with merely one emotion but possibly also with several. The analysis was carried out with the help of different cartographic visualisations. The temporal occurrence of georeferenced emotions was examined detailed. Hence the dissertation focuses on fundamental research into the extraction of space-related emotions from georeferenced user-generated content as well as their visualisation. However as an outlook, further research questions and core themes are identified which arose during the investigations. This shows that this subject is far from being exhausted.:Statement of Authorship I Acknowledgements II Abstract III Zusammenfassung V Table of Contents VII List of Figures XI List of Tables XIV List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Research Questions 3 1.3 Thesis Structure 4 1.4 Underlying Publications 4 2 State of the Art 6 2.1 Emotions 6 2.1.1 Definitions and Terms 6 2.1.2 Emotion Theories 7 2.1.2.1 James-Lange Theory 9 2.1.2.2 Two-Factor Theory 9 2.1.3 Structuring Emotions 9 2.1.3.1 Dimensional Approaches 10 2.1.3.2 Basic Emotions 11 2.1.3.3 Empirical Similarity Categories 12 2.1.4 Acquisition of Emotions 14 2.1.4.1 Verbal Procedures 14 2.1.4.2 Non-Verbal Procedures 14 2.1.5 Relation between Emotions and Places 15 2.1.6 Emotions in Language 17 2.1.7 Affect Analysis and Sentiment Analysis 20 2.2 User-Generated Content 22 2.2.1 Definition and Characterisation 22 2.2.2 Advantages and Disadvantages 23 2.2.3 Tagging 24 2.2.4 Inaccuracies 28 2.2.5 Flickr and Panoramio 29 2.2.5.1 Flickr 30 2.2.5.2 Panoramio 31 2.3 Related Work on Georeferenced Emotions 32 2.3.1 Emotional Data Resulting from Biometric Measurements 33 2.3.1.1 Bio Mapping 33 2.3.1.2 EmBaGIS 34 2.3.1.3 Ein emotionales Kiezportrait 35 2.3.2 Emotional Data Resulting from Empirical Surveys 35 2.3.2.1 EmoMap 35 2.3.2.2 WiMo 36 2.3.2.3 ECDESUP 37 2.3.2.4 Map of World Happiness 38 2.3.2.5 Emotional Study of Yeongsan River Basin 39 2.3.3 Emotional Data Resulting from User-Generated Content 40 2.3.3.1 Emography 40 2.3.3.2 Twittermood 40 2.3.3.3 Tweetbeat 42 2.3.3.4 Beautiful picture of an ugly place 42 2.3.4 Visualisation in the Related Work 43 3 Methods 45 3.1 Approach for Extracting Georeferenced Emotions from the Metadata of Flickr and Panoramio Photos 45 3.2 Implemented Algorithm 45 3.3 Grammatical Special Cases 47 3.3.1 Degree Words 48 3.3.2 Negation 52 3.3.2.1 Syntactic Negation in English Language 55 3.3.2.2 Syntactic Negation in German Language 57 3.3.3 Modification of Words Affected by Grammatical Special Cases 60 4 Visualisation and Analysis of Extracted Georeferenced Emotions 62 4.1 Data Basis 62 4.2 Density Maps 67 4.3 Inverse Distance Weight 71 4.4 3D Visualisation 73 4.5 Choropleth Mapping 74 4.6 Point Symbols 78 4.7 Impact of Considering Grammatical Special Cases 80 5 Investigation in Temporal Aspects 85 5.1 Annually Occurrence of Emotions 85 5.2 Periodic Events 87 5.3 Single Events 91 5.4 Dependence of Georeferenced Emotions on Different Periods of Time 93 5.4.1 Seasons 95 5.4.2 Months 96 5.4.3 Weekdays 98 5.4.4 Times of Day 99 5.5 Potentials and Limits of Temporal Analyses 99 6 Discussion 100 6.1 Evaluation 100 6.2 Weaknesses and Problems 102 7 Conclusions and Outlook 105 7.1 Answers to the Research Questions 105 7.2 Outlook and Future Work 107 8 Bibliography 112 Appendices XVI / In den letzten Jahren sind emotionsbezogene Anwendungen, wie Apps, die die Emotionen des Nutzers dokumentieren und analysieren, sehr populär geworden. Ebenfalls in der Forschung sind Emotionen in einem sehr technologiegetriebenen Ansatz ein Thema. So auch ortsbezogene Emotionen, die sich somit kartographisch darstellen lassen und auf verschiedene Art und Weisen gewonnen werden können. Das Forschungsvorhaben der Dissertation befasst sich mit der Extraktion von georeferenzierten Emotionen aus geschriebener Sprache unter Verwendung von Metadaten verorteter Flickr- und Panoramio-Fotos, d.h. aus nutzergenerierten Inhalten, sowie deren Modellierung und Visualisierung. Motivation hierfür ist die Einbindung einer emotionalen Komponente in ortsbasierte touristische Dienste, da diese bisher nur faktische Informationen berücksichtigen, obwohl Orte durchaus eine emotionale Wirkung haben. Die Metadaten dieser nutzergenerierten Inhalte stellen Beschreibungen des auf dem Foto festgehaltenen Ortes dar. Die dafür verwendeten Wörter besitzen affektive Konnotationen, welche mit Hilfe emotionaler Wortlisten ermittelt werden. Die Emotion, die mit dem jeweiligen Wort in der Wortliste assoziiert wird, wird anhand der zwei Dimensionen Valenz und Erregung beschrieben. Die extrahierten Emotionen bilden zusammen mit der geographischen Koordinate des jeweiligen Fotos eine georeferenzierte Emotion. Der zur Extraktion dieser Emotionen entwickelte Algorithmus bringt verschiedene Ansätze aus dem Bereich der Computerlinguistik zum Einsatz und berücksichtigt ebenso grammatikalische Sonderfälle, wie Intensivierung oder Negation von Wörtern. Der Algorithmus wurde auf einen Datensatz von Flickr- und Panoramio-Fotos von Dresden angewendet. Die Ergebnisse stellen eine emotionale Raumcharakterisierung dar und ermöglichen es, spezifische Eigenschaften verorteter Emotionen festzustellen und zu untersuchen. Diese Eigenschaften beziehen sich sowohl auf die zeitliche Abhängigkeit und den zeitlichen Bezug von Emotionen, als auch darauf, dass zwischen kollektiv und individuell wahrgenommenen Emotionen unterschieden werden muss. Das bedeutet, dass ein Ort nicht nur mit einer Emotion verbunden sein muss, sondern möglicherweise auch mit mehreren. Die Auswertung erfolgte mithilfe verschiedener kartographischer Visualisierungen. Eingehender wurde das zeitliche Auftreten der ortsbezogenen Emotionen untersucht. Der Fokus der Dissertation liegt somit auf der Grundlagenforschung zur Extraktion verorteter Emotionen aus georeferenzierten nutzergenerierten Inhalten sowie deren Visualisierung. Im Ausblick werden jedoch weitere Fragestellungen und Schwerpunkte genannt, die sich im Laufe der Untersuchungen ergeben haben, womit gezeigt wird, dass dieses Forschungsgebiet bei Weitem noch nicht ausgeschöpft ist.:Statement of Authorship I Acknowledgements II Abstract III Zusammenfassung V Table of Contents VII List of Figures XI List of Tables XIV List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Research Questions 3 1.3 Thesis Structure 4 1.4 Underlying Publications 4 2 State of the Art 6 2.1 Emotions 6 2.1.1 Definitions and Terms 6 2.1.2 Emotion Theories 7 2.1.2.1 James-Lange Theory 9 2.1.2.2 Two-Factor Theory 9 2.1.3 Structuring Emotions 9 2.1.3.1 Dimensional Approaches 10 2.1.3.2 Basic Emotions 11 2.1.3.3 Empirical Similarity Categories 12 2.1.4 Acquisition of Emotions 14 2.1.4.1 Verbal Procedures 14 2.1.4.2 Non-Verbal Procedures 14 2.1.5 Relation between Emotions and Places 15 2.1.6 Emotions in Language 17 2.1.7 Affect Analysis and Sentiment Analysis 20 2.2 User-Generated Content 22 2.2.1 Definition and Characterisation 22 2.2.2 Advantages and Disadvantages 23 2.2.3 Tagging 24 2.2.4 Inaccuracies 28 2.2.5 Flickr and Panoramio 29 2.2.5.1 Flickr 30 2.2.5.2 Panoramio 31 2.3 Related Work on Georeferenced Emotions 32 2.3.1 Emotional Data Resulting from Biometric Measurements 33 2.3.1.1 Bio Mapping 33 2.3.1.2 EmBaGIS 34 2.3.1.3 Ein emotionales Kiezportrait 35 2.3.2 Emotional Data Resulting from Empirical Surveys 35 2.3.2.1 EmoMap 35 2.3.2.2 WiMo 36 2.3.2.3 ECDESUP 37 2.3.2.4 Map of World Happiness 38 2.3.2.5 Emotional Study of Yeongsan River Basin 39 2.3.3 Emotional Data Resulting from User-Generated Content 40 2.3.3.1 Emography 40 2.3.3.2 Twittermood 40 2.3.3.3 Tweetbeat 42 2.3.3.4 Beautiful picture of an ugly place 42 2.3.4 Visualisation in the Related Work 43 3 Methods 45 3.1 Approach for Extracting Georeferenced Emotions from the Metadata of Flickr and Panoramio Photos 45 3.2 Implemented Algorithm 45 3.3 Grammatical Special Cases 47 3.3.1 Degree Words 48 3.3.2 Negation 52 3.3.2.1 Syntactic Negation in English Language 55 3.3.2.2 Syntactic Negation in German Language 57 3.3.3 Modification of Words Affected by Grammatical Special Cases 60 4 Visualisation and Analysis of Extracted Georeferenced Emotions 62 4.1 Data Basis 62 4.2 Density Maps 67 4.3 Inverse Distance Weight 71 4.4 3D Visualisation 73 4.5 Choropleth Mapping 74 4.6 Point Symbols 78 4.7 Impact of Considering Grammatical Special Cases 80 5 Investigation in Temporal Aspects 85 5.1 Annually Occurrence of Emotions 85 5.2 Periodic Events 87 5.3 Single Events 91 5.4 Dependence of Georeferenced Emotions on Different Periods of Time 93 5.4.1 Seasons 95 5.4.2 Months 96 5.4.3 Weekdays 98 5.4.4 Times of Day 99 5.5 Potentials and Limits of Temporal Analyses 99 6 Discussion 100 6.1 Evaluation 100 6.2 Weaknesses and Problems 102 7 Conclusions and Outlook 105 7.1 Answers to the Research Questions 105 7.2 Outlook and Future Work 107 8 Bibliography 112 Appendices XVI

Die vorliegende Masterarbeit behandelt den Entwurf und die exemplarische Umsetzung eines Arbeitsablaufs zur Aufbereitung von georeferenziertem Microblogging Content. Als beispielhafte Datenquelle wurde Twitter herangezogen. Darauf basierend, wurden Überlegungen angestellt, welche Arbeitsschritte nötig und mit welchen Mitteln sie am besten realisiert werden können. Dabei zeigte sich, dass eine ganze Reihe von Bausteinen aus dem Bereich des Data Mining und des Text Mining für eine Pipeline bereits vorhanden sind und diese zum Teil nur noch mit den richtigen Einstellungen aneinandergereiht werden müssen. Zwar kann eine logische Reihenfolge definiert werden, aber weitere Anpassungen auf die Fragestellung und die verwendeten Daten können notwendig sein. Unterstützt wird dieser Prozess durch verschiedenen Visualisierungen mittels Histogrammen, Wortwolken und Kartendarstellungen. So kann neues Wissen entdeckt und nach und nach die Parametrisierung der Schritte gemäß den Prinzipien des Geovisual Analytics verfeinert werden. Für eine exemplarische Umsetzung wurde nach der Betrachtung verschiedener Softwareprodukte die für statistische Anwendungen optimierte Programmiersprache R ausgewählt. Abschließend wurden die Software mit Daten von Twitter und Flickr evaluiert. / This Master's Thesis deals with the conception and exemplary implementation of a workflow for georeferenced Microblogging Content. Data from Twitter is used as an example and as a starting point to think about how to build that workflow. In the field of Data Mining and Text Mining, there was found a whole range of useful software modules that already exist. Mostly, they only need to get lined up to a process pipeline using appropriate preferences. Although a logical order can be defined, further adjustments according to the research question and the data are required. The process is supported by different forms of visualizations such as histograms, tag clouds and maps. This way new knowledge can be discovered and the options for the preparation can be improved. This way of knowledge discovery is already known as Geovisual Analytics. After a review of multiple existing software tools, the programming language R is used to implement the workflow as this language is optimized for solving statistical problems. Finally, the workflow has been tested using data from Twitter and Flickr.