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  • 1.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Li, Yuan-Fang
    Monash University, Australia.
    Schreiber, Falk
    University of Konstanz, Germany ; Monash University, Australia.
    BioVis Explorer: A visual guide for biological data visualization techniques2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 11, article id e0187341Article in journal (Refereed)
    Abstract [en]

    Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for the visualization of biological data have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers and visualization researchers. To address this challenge, we have reviewed visualization research especially performed for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. The tool is publicly available online and has been designed as community-based system which allows users to add their works easily.

  • 2.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Li, Yuan-Fang
    Monash University, Australia.
    Schreiber, Falk
    University of Konstanz, Germany ; Monash University, Australia.
    MDS-based Visual Survey of Biological Data Visualization Techniques2017In: EuroVis 2017 - Posters / [ed] Anna Puig Puig and Tobias Isenberg, Eurographics - European Association for Computer Graphics, 2017, p. 85-87Conference paper (Refereed)
    Abstract [en]

    Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for biological data visualization have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers as well as visualization researchers. To address this challenge, we have reviewed visualization research for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. 

  • 3.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Sentiment and Stance Visualization of Textual Data for Social Media2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Rapid progress in digital technologies has transformed the world in many ways during the past few decades, in particular, with the new means of communication such as social media. Social media platforms typically rely on textual data produced or shared by the users in multiple timestamped posts. Analyses of such data are challenging for traditional manual methods that are unable to scale up to the volume and the variety of the data. While computational methods can partially address these challenges, they have to be used together with the methods developed within information visualization and visual analytics to gain knowledge from the text data by using interactive visual representations.

    One of the most interesting aspects of text data is related to expressions of sentiments and opinions. The corresponding task of sentiment analysis has been studied within computational linguistics, and sentiment visualization techniques exist as well. However, there are gaps in research on the related task of stance analysis, dedicated to subjectivity that is not expressible only in terms of sentiment. Research on stance is an area of interest in linguistics, but support by computational and visual methods has been limited so far. The challenges related to definition, analysis, and visualization of stance in textual data call for an interdisciplinary research effort. The StaViCTA project addressed these challenges with a focus on written text in English. The corresponding results in the area of visualization are reported in this work, based on multiple publications.

    The main goal of this dissertation is to define, categorize, and implement means for visual analysis of sentiment and stance in textual data, in particular, for social media. Our work is based on the theoretical framework and automatic classifier of stance developed by our project collaborators, involving multiple non-exclusive stance categories such as certainty and prediction. We define a design space for sentiment and stance visualization techniques based on literature surveys. We discuss multiple visualization and visual analytics approaches developed by us to facilitate the underlying research on stance analysis, data collection and annotation, and visual analysis of sentiment and stance in real-world text data from several social media sources. The work described in this dissertation was carried out in cooperation with domain experts in linguistics and computational linguistics, and our approaches were validated with case studies, expert user reviews, and critical discussion. The results of this work open up further opportunities for research in text visualization and visual text analytics. The potential application areas are academic research, business intelligence, social media monitoring, and journalism.

  • 4.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Cernea, Daniel
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Visualizing Excitement of Individuals and Groups2016In: Proceedings of the ACM IUI 2016 Workshop on Emotion and Visualization (EmoVis '16) / [ed] Andreas Kerren, Daniel Cernea, and Margit Pohl, Linköping, Sweden: Linköping University Electronic Press, 2016, p. 15-22Conference paper (Refereed)
    Abstract [en]

    Excitement or arousal is one of the main emotional dimensions that affects our lives on a daily basis. We win a tennis match, watch a great movie, get into an argument with a colleague—all of these are instances when most of us experience excitement, yet we do not pay much attention to it. Today, there are few systems that capture our excitement levels and even fewer that actually promote awareness of our most exciting moments. In this paper, we propose a visualization concept for representing individual and group-level excitement for emotional self-awareness and group-level awareness. The data used for the visualization is obtained from smart wristbands worn by each of the users. The visualization uses animated glyphs to generate a real-time representation for each individual’s excitement levels. We introduce two types of encodings for these glyphs: one focusing on capturing both the current excitement and the excitement history, as well as another focusing only on real-time values and previous peaks. The excitement levels are computed based on measurements of the user’s galvanic skin response and accelerometer data from the wristbands, allowing for a classification of the excitement levels into experienced (excitement without physical manifestation) and manifested excitement. A dynamic clustering of the individual glyphs supports the scalability of our visualization, while at the same time offering an overview of the group-level excitement and its distribution. The results of a preliminary evaluation suggest that the visualization allows users to intuitively and accurately perceive both individual and group-level excitement. 

  • 5.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Text Visualization Browser: A Visual Survey of Text Visualization Techniques2014In: Poster Abstracts of IEEE VIS 2014, 2014Conference paper (Refereed)
    Abstract [en]

    Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this poster, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. 

  • 6.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Text Visualization Revisited: The State of the Field in 20192019In: Posters of the 21th EG/VGTC Conference on Visualization (EuroVis '19) / [ed] João M. Pereira and Renata G. Raidou, Eurographics - European Association for Computer Graphics, 2019, p. 29-31Conference paper (Refereed)
    Abstract [en]

    Text and document data visualization is an important research field within information visualization and visual analytics with multiple application domains including digital humanities and social media, for instance. During the past five years, we have been collecting text visualization techniques described in peer-reviewed literature, categorizing them according to a detailed categorization schema, and providing the resulting manually curated collection in an online survey browser. In this poster paper, we present the updated results of analyses of this data set as of spring 2019. Compared to the recent surveys and meta-analyses that mainly focus on particular aspects and problems related to text visualization, our results provide an overview of the current state of the text visualization field and the respective research community in general.

  • 7.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Text Visualization Techniques: Taxonomy, Visual Survey, and Community Insights2015In: Proceedings of the 8th IEEE Pacific Visualization Symposium (PacificVis '15), / [ed] Shixia Liu, Gerik Scheuermann, and Shigeo Takahashi, IEEE, 2015, p. 117-121Conference paper (Refereed)
    Abstract [en]

    Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and compare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data. 

  • 8.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Methodology and Applications of Visual Stance Analysis: An Interactive Demo2016In: International Symposium on Digital Humanities, Växjö 7-8 November 2016: Book of Abstracts, Linnaeus University , 2016, p. 56-57Conference paper (Refereed)
    Abstract [en]

    Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.

    Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages. 

  • 9.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Stance Markers in Online Social Media2014In: Poster Abstracts of IEEE VIS 2014, 2014Conference paper (Refereed)
    Abstract [en]

    Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers’ attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection. 

  • 10.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University .
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Text Annotations for Stance Classification with ALVA2016In: EuroVis Posters 2016 / [ed] Tobias Isenberg & Filip Sadlo, Eurographics - European Association for Computer Graphics, 2016, p. 49-51Conference paper (Refereed)
    Abstract [en]

    The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring. 

  • 11.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Martins, Rafael Messias
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Analysis of VINCI 2009–2017 Proceedings2018In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18), 13-15 August 2018, Växjö, Sweden / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, Association for Computing Machinery (ACM), 2018, p. 97-101Conference paper (Refereed)
    Abstract [en]

    Both the metadata and the textual contents of scientific publications can provide us with insights about the development and the current state of the corresponding scientific community. In this short paper, we take a look at the proceedings of VINCI from the previous years and conduct several types of analyses. We summarize the yearly statistics about different types of publications, identify the overall authorship statistics and the most prominent contributors, and analyze the current community structure with a co-authorship network. We also apply topic modeling to identify the most prominent topics discussed in the publications. We hope that the results of our work will provide insights for the visualization community and will also be used as an overview for researchers previously unfamiliar with VINCI.

  • 12.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    DoSVis: Document Stance Visualization2018In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18) / [ed] Alexandru C. Telea, Andreas Kerren, and José Braz, SciTePress, 2018, Vol. 3, p. 168-175Conference paper (Refereed)
    Abstract [en]

    Text visualization techniques often make use of automatic text classification methods. One of such methods is stance analysis, which is concerned with detecting various aspects of the writer’s attitude towards utterances expressed in the text. Existing text visualization approaches for stance classification results are usually adapted to textual data consisting of individual utterances or short messages, and they are often designed for social media or debate monitoring tasks. In this paper, we propose a visualization approach called DoSVis (Document Stance Visualization) that focuses instead on individual text documents of a larger length. DoSVis provides an overview of multiple stance categories detected by our classifier at the utterance level as well as a detailed text view annotated with classification results, thus supporting both distant and close reading tasks. We describe our approach by discussing several application scenarios involving business reports and works of literature. 

  • 13.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    The State of the Art in Sentiment Visualization2018In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 37, no 1, p. 71-96, article id CGF13217Article in journal (Refereed)
    Abstract [en]

    Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational, and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer-reviewed publications together with an interactive web-based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data. 

  • 14.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Visual Analysis of Sentiment and Stance in Social Media Texts2018In: EuroVis 2018 - Posters / [ed] Anna Puig and Renata Raidou, Eurographics - European Association for Computer Graphics, 2018, p. 49-51Conference paper (Refereed)
    Abstract [en]

    Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers’ output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.

  • 15.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Swedish Research Institute (RISE SICS).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Active Learning and Visual Analytics for Stance Classification with ALVA2017In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 7, no 3, article id 14Article in journal (Refereed)
    Abstract [en]

    The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine learning methods create an opportunity to gain insight into the speakers' attitudes towards their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. In order to facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA's interplay with the stance classifier follows an active learning strategy in order to select suitable candidate utterances for manual annotation. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.

  • 16.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Schamp-Bjerede, Teri
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena2016In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 15, no 2, p. 93-116Article in journal (Refereed)
    Abstract [en]

    Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

  • 17.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Potsdam University, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Application of Interactive Computer-Assisted Argument Extraction to Opinionated Social Media Texts2018In: Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18) / [ed] Karsten Klein, Yi-Na Li, and Andreas Kerren, Association for Computing Machinery (ACM), 2018, p. 102-103Conference paper (Refereed)
    Abstract [en]

    The analysis of various opinions and arguments in textual data can be facilitated by automatic topic modeling methods; however, the exploration and interpretation of the resulting topics and terms may prove to be difficult to the analysts. Opinions, stances, arguments, topics, terms, and text documents are usually connected with many-to-many relationships for such tasks. Exploratory visual analysis with interactive tools can help the analysts to get an overview of the topics and opinions, identify particularly interesting documents, and describe main themes of various arguments. In our previous work, we introduced an interactive tool called Topics2Themes that was used for topic and theme analysis of vaccination-related discussion texts with a limited set of stance categories. In this poster paper, we describe an application of Topics2Themes to a different genre of data, namely, political comments from Reddit, and multiple sentiment and stance categories detected with automatic classifiers.

  • 18.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Weyns, Danny
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    A Self-Adaptive Software System to Support Elderly Care2013In: Proceedings of Modern Information Technology, MIT, 2013, 2013Conference paper (Refereed)
  • 19.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Simaki, Vasiliki
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media2017Conference paper (Refereed)
    Abstract [en]

    The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stancetaking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier. 

  • 20.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Rahimi, Afshin
    Hedges and Tweets: Certainty and Uncertainty in Epistemic Markers in Microblog Feeds2014In: Book of abstracts: 47th Annual Meeting of the Societas Linguistica Europaea 11–14 September 2014, Adam Mickiewicz University, Poznań, Poland, 2014, p. 199-199Conference paper (Refereed)
  • 21.
    Schamp-Bjerede, Teri
    et al.
    Lund University, Sweden.
    Paradis, Carita
    Lund University, Sweden.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB, Sweden.
    New Perspectives on Gathering, Vetting and Employing Big Data from Online Social Media: An Interdisciplinary Approach2015In: Abstracts Booklet, ICAME 36: Words, Words, Words – Corpora and Lexis, 2015, p. 153-155Conference paper (Refereed)
  • 22.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    The Signifier, Signified and Stance: Happy/Sad Emoticons as Emotionizers2014In: Book of Abstracts, IACS 2014, 2014, p. 219-219Conference paper (Refereed)
  • 23.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    Turning Face: Emoticons as Reinforcers/Attenuators2014Conference paper (Refereed)
  • 24.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University.
    Paradis, Carita
    Lund University.
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Swedish Research Institute (RISE SICS).
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Annotating speaker stance in discourse: the Brexit Blog Corpus2017In: Corpus linguistics and linguistic theory, ISSN 1613-7027, E-ISSN 1613-7035Article in journal (Refereed)
    Abstract [en]

    The aim of this study is to explore the possibility of identifying speaker stance in discourse, provide an analytical resource for it and an evaluation of the level of agreement across speakers. We also explore to what extent language users agree about what kind of stances are expressed in natural language use or whether their interpretations diverge. In order to perform this task, a comprehensive cognitive-functional framework of ten stance categories was developed based on previous work on speaker stance in the literature. A corpus of opinionated texts was compiled, the Brexit Blog Corpus (BBC). An analytical protocol and interface (ALVA) for the annotations was set up and the data were independently annotated by two annotators. The annotation procedure, the annotation agreements and the co-occurrence of more than one stance in the utterances are described and discussed. The careful, analytical annotation process has returned satisfactory inter- and intra-annotation agreement scores, resulting in a gold standard corpus, the final version of the BBC. 

  • 25.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Language Processing Components of the StaViCTA Project2017In: Proceedings of the Workshop on Logic and Algorithms in Computational Linguistics 2017 (LACompLing 2017) / [ed] Roussanka Loukanova and Kristina Liefke, Stockholm University ; KTH , 2017, p. 137-138Conference paper (Refereed)
    Abstract [en]

    The StaViCTA project is concerned with visualising the expression of stance in written text, and is therefore dependent on components for stance detection. These components are to (i) download and extract text from any HTML page and segment it into sentences, (ii) classify each sentence with respect to twelve different, notionally motivated, stance categories, and (iii) provide a RESTful HTTP API for communication with the visualisation components. The stance categories are certainty, uncertainty, contrast, recommendation, volition, prediction, agreement, disagreement, tact, rudeness, hypotheticality, and source of knowledge. 

  • 26.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Potsdam University, Germany.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Stede, Manfred
    Potsdam University, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics2018In: Proceedings of the LREC 2018 Workshop “The 3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III)” / [ed] Mennatallah El-Assady, Annette Hautli-Janisz, and Verena Lyding, Paris, France: European Language Resources Association, 2018, p. 9-16Conference paper (Refereed)
    Abstract [en]

    While the task of manually extracting arguments from large collections of opinionated text is an intractable one, a tool for computerassisted extraction can (i) select a subset of the text collection that contains re-occurring arguments to minimise the amount of text that the human coder has to read, and (ii) present the selected texts in a way that facilitates manual coding of arguments. We propose a tool called Topics2Themes that uses topic modelling to extract important topics, as well as the terms and texts most closely associated with each topic. We also provide a graphical user interface for manual argument coding, in which the user can search for arguments in the texts selected, create a theme for each type of argument detected and connect it to the texts in which it is found. Topics, terms, texts and themes are displayed as elements in four separate lists, and associations between the elements are visualised through connecting links. It is also possible to focus on one particular element through the sorting functionality provided, which can be used to facilitate the argument coding and gain an overview and understanding of the arguments found in the texts.

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