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Simaki, V., Panagiotis, S., Paradis, C. & Kerren, A. (2018). Detection of Stance-Related Characteristics in Social Media Text. In: : . Paper presented at The 10th Hellenic Conference on Artificial Intelligence (SETN '18), 9-15 July 2018, Patras, Greece. ACM Digital Library
Open this publication in new window or tab >>Detection of Stance-Related Characteristics in Social Media Text
2018 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
ACM Digital Library, 2018
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-71735 (URN)
Conference
The 10th Hellenic Conference on Artificial Intelligence (SETN '18), 9-15 July 2018, Patras, Greece
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Note

TO BE PUBLISHED!

Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2018-04-12
Kucher, K., Paradis, C. & Kerren, A. (2018). DoSVis: Document Stance Visualization. In: Alexandru C. Telea, Andreas Kerren, and José Braz (Ed.), Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18): . Paper presented at International Conference on Information Visualization Theory and Applications (IVAPP), Funchal-Madeira, Portugal, 27-29 January, 2018 (pp. 168-175). SciTePress, 3
Open this publication in new window or tab >>DoSVis: Document Stance Visualization
2018 (English)In: 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, Published 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. 

Place, publisher, year, edition, pages
SciTePress, 2018
Keyword
Stance Visualization, Sentiment Visualization, Text Visualization, Stance Analysis, Sentiment Analysis, Text Analytics, Information Visualization, Interaction
National Category
Computer Sciences Human Computer Interaction Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-68428 (URN)10.5220/0006539101680175 (DOI)978-989-758-289-9 (ISBN)
Conference
International Conference on Information Visualization Theory and Applications (IVAPP), Funchal-Madeira, Portugal, 27-29 January, 2018
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2018-04-12Bibliographically approved
Simaki, V., Paradis, C. & Kerren, A. (2018). Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis. ICAME Journal/International Computer Archive of Modern English, 42(1), 133-166
Open this publication in new window or tab >>Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis
2018 (English)In: ICAME Journal/International Computer Archive of Modern English, ISSN 0801-5775, E-ISSN 1502-5462, Vol. 42, no 1, p. 133-166Article in journal (Refereed) Published
Abstract [en]

This paper offers a formally driven quantitative analysis of stance-annotated sentences in the Brexit Blog Corpus (BBC). Our goal is to highlight linguistic features that determine the formal profiles of six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty) in a subset of the BBC. The study has two parts: firstly, it examines a large number of formal linguistic features that occur in the sentences in order to describe the specific characteristics of each category, and secondly, it compares characteristics in the entire data set in order to determine linguistic similarities throughout the data set. We show that among the six stance categories in the corpus, contrariety and necessity are the most discriminative ones, with the former using longer sentences, more conjunctions, more repetitions and shorter forms than the sentences expressing other stances. The latter has longer lexical forms but shorter sentences, which are syntactically more complex. We show that stance in our data set is expressed in sentences with around 21 words per sentence. The sentences consist mainly of alphabetical characters forming a varied vocabulary without special forms, such as digits or special characters.

Place, publisher, year, edition, pages
De Gruyter Open, 2018
Keyword
stance-taking, corpus annotation, political blog text, statistical analysis, formal features
National Category
Language Technology (Computational Linguistics) Specific Languages
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-70768 (URN)10.1515/icame-2018-0007 (DOI)
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-04-19
Telea, A. C., Kerren, A. & Braz, J. (Eds.). (2018). Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018, Funchal, Madeira - Portugal, January 27-29, 2018: Volume 3. Paper presented at International Conference on Information Visualization Theory and Applications (IVAPP '18), Funchal, Madeira - Portugal, January 27-29, 2018. SciTePress
Open this publication in new window or tab >>Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018, Funchal, Madeira - Portugal, January 27-29, 2018: Volume 3
2018 (English)Conference proceedings (editor) (Refereed)
Abstract [en]

This book contains the proceedings of the 13th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), in cooperation with AFIG and Eurographics. The proceedings here published demonstrate new and innovative solutions and highlight technical problems in each field that are challenging and worthwhile being disseminated to the interested research audiences. VISIGRAPP 2018 was organized to promote a discussion forum about the conference’s research topics between researchers, developers, manufacturers and end-users, and to establish guidelines in the development of more advanced solutions. We received a high number of paper submissions for this edition of VISIGRAPP, 321 in total, with contributions from all five continents. This attests to the success and global dimension of VISIGRAPP. To evaluate each submission, we used a double-blind evaluation method where each paper was reviewed by two to six experts from the International Program Committee (IPC). The IPC selected for oral presentation and for publication as full papers 14 papers from GRAPP, 6 for HUCAPP, 12 papers for IVAPP, and 40 papers for VISAPP, which led to a result for the full-paper acceptance ratio of 22% and a high-quality program. Apart from the above full papers, the conference program also features 83 short papers and 68 poster presentations. We hope that these conference proceedings, which are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index, INSPEC, DBLP, and EI, will help the Computer Vision, Imaging, Visualization and Computer Graphics communities to find interesting research work. Moreover, we are proud to inform that the program also includes four plenary keynote lectures, given by internationally distinguished researchers, namely Carol O'Sullivan (Trinity College Dublin, Ireland), Alexander Bronstein (Israel Institute of Technology,Tel Aviv University and Intel Corporation, Israel), Falk Schreiber (University of Konstanz, Germany and Monash University Melbourne, Australia) and Catherine Pelachaud (CNRS/University of Pierre and Marie Curie, France), thus contributing to increase the overall quality of the conference and to provide a deeper understanding of the conference’s interest fields. Furthermore, a short list of the presented papers will be selected to be expanded into a forthcoming book of VISIGRAPP Selected Papers to be published by Springer during 2018 in the CCIS series. All papers presented at this conference will be available at the SCITEPRESS Digital Library. Two awards are delivered at the closing session, to recognize the best conference paper and the best student paper for each of the four tracks. The meeting is complemented with the Special Session on Visual Computing in Engineering Applications (VCEA) and two tutorials entitled “Visual Intelligence in Egocentric (First-Person) Vision Systems” and “Understanding Human Motion Primitives”. We would like to express our thanks, first of all, to the authors of the technical papers, whose work and dedication made possible to put together a program that we believe to be very exciting and of high technical quality. Next, we would like to thank the Area Chairs, all the members of the program committee and auxiliary reviewers, who helped us with their expertise and time. We would also like to thank the invited speakers for their invaluable contribution and for sharing their vision in their talks. Special thanks should be addressed to the INSTICC Steering Committee whose invaluable work made this event possible. We wish you all an exciting conference and an unforgettable stay in Funchal, Madeira, Portugal. We hope to meet you again for the next edition of VISIGRAPP, details of which are available at http://www. visigrapp.org.

Place, publisher, year, edition, pages
SciTePress, 2018. p. 365
Keyword
Information Visualization
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Information and software visualization; Computer and Information Sciences Computer Science
Identifiers
urn:nbn:se:lnu:diva-69833 (URN)978-989-758-289-9 (ISBN)
Conference
International Conference on Information Visualization Theory and Applications (IVAPP '18), Funchal, Madeira - Portugal, January 27-29, 2018
Available from: 2018-01-14 Created: 2018-01-14 Last updated: 2018-04-12Bibliographically approved
Kucher, K., Paradis, C. & Kerren, A. (2018). The State of the Art in Sentiment Visualization. Computer graphics forum (Print), 37(1), 71-96, Article ID CGF13217.
Open this publication in new window or tab >>The State of the Art in Sentiment Visualization
2018 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 37, no 1, p. 71-96, article id CGF13217Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
John Wiley & Sons, 2018
Keyword
sentiment visualization, text visualization, sentiment analysis, opinion mining
National Category
Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-62644 (URN)10.1111/cgf.13217 (DOI)000426151300007 ()
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-04-27 Created: 2017-04-27 Last updated: 2018-03-16Bibliographically approved
Skeppstedt, M., Kucher, K., Stede, M. & Kerren, A. (2018). Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics. In: : . Paper presented at 3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III) at LREC '18, 12 May, 2018, Miyazaki, Japan.
Open this publication in new window or tab >>Topics2Themes: Computer-Assisted Argument Extraction by Visual Analysis of Important Topics
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The large collections of opinionated text that are continuously being created online, e.g., in the form of forum posts or tweets, contain arguments that might help us to better understand why opinions are held. While the task of manually extracting arguments from these large collections is an intractable one, a tool for computer-assisted extraction can (i) automatically 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 automatically 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, e.g., when a topic is selected, the terms, texts and themes associated with this topic are sorted as the top-ranked elements in their respective lists. The text collection can thereby be explored from different angles, which can be used to facilitate the argument coding and gain an overview and understanding of the arguments found in the texts. 

Keyword
argument extraction, topic modelling, text analysis, argument visualization, stance visualization, text visualization, information visualization, interaction
National Category
Language Technology (Computational Linguistics) Computer Sciences
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-70911 (URN)
Conference
3rd Workshop on Visualization as Added Value in the Development, Use and Evaluation of Language Resources (VisLR III) at LREC '18, 12 May, 2018, Miyazaki, Japan
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Swedish Research Council, 2016-06681
Note

TO BE PUBLISHED!

Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-02-15
Skeppstedt, M., Kerren, A. & Stede, M. (2018). Vaccine Hesitancy in Discussion Forums: Computer-Assisted Argument Mining with Topic Models. In: : . Paper presented at 29th Medical Informatics Europe Conference (MIE '18), April 24-26, 2018, Gothenburg, Sweden. IOS Press
Open this publication in new window or tab >>Vaccine Hesitancy in Discussion Forums: Computer-Assisted Argument Mining with Topic Models
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Arguments used when vaccination is debated on Internet discussion forums might give us valuable insights into reasons behind vaccine hesitancy. In this study, we applied automatic topic modelling on a collection of 943 discussion posts in which vaccine was debated, and six distinct discussion topics were detected by the algorithm. When manually coding the posts ranked as most typical for these six topics, a set of semantically coherent arguments were identified for each extracted topic. This indicates that topic modelling is a useful method for automatically identifying vaccine-related discussion topics and for identifying debate posts where these topics are discussed. This functionality could facilitate manual coding of salient arguments, and thereby form an important component in a system for computer-assisted coding of vaccine-related discussions. 

Place, publisher, year, edition, pages
IOS Press, 2018
Keyword
vaccine hesitancy, topic modelling, argument mining
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-70919 (URN)
Conference
29th Medical Informatics Europe Conference (MIE '18), April 24-26, 2018, Gothenburg, Sweden
Projects
StaViCTA
Funder
Swedish Research Council, 2016-06681Swedish Research Council, 2012-5659
Note

TO BE PUBLISHED!

Available from: 2018-02-15 Created: 2018-02-15 Last updated: 2018-02-15
Kucher, K., Paradis, C., Sahlgren, M. & Kerren, A. (2017). Active Learning and Visual Analytics for Stance Classification with ALVA. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(3), Article ID 14.
Open this publication in new window or tab >>Active Learning and Visual Analytics for Stance Classification with ALVA
2017 (English)In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 7, no 3, article id 14Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Publications, 2017
Keyword
visualization, stance visualization, active learning, text visualization, sentiment visualization, annotation, visual analytics, sentiment analysis, stance analysis, NLP, text analytics
National Category
Computer Sciences Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-67173 (URN)10.1145/3132169 (DOI)000414322200005 ()
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2017-08-05 Created: 2017-08-05 Last updated: 2018-04-04Bibliographically approved
Simaki, V., Paradis, C., Skeppstedt, M., Sahlgren, M., Kucher, K. & Kerren, A. (2017). Annotating speaker stance in discourse: the Brexit Blog Corpus. Corpus linguistics and linguistic theory
Open this publication in new window or tab >>Annotating speaker stance in discourse: the Brexit Blog Corpus
Show others...
2017 (English)In: Corpus linguistics and linguistic theory, ISSN 1613-7027, E-ISSN 1613-7035Article in journal (Refereed) Epub ahead of print
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. 

Keyword
text annotation, blog post texts, modality, evaluation, positioning
National Category
Language Technology (Computational Linguistics) General Language Studies and Linguistics
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-67319 (URN)10.1515/cllt-2016-0060 (DOI)
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Note

TO BE PUBLISHED!

Available from: 2017-08-21 Created: 2017-08-21 Last updated: 2018-02-27
Skeppstedt, M., Kerren, A. & Stede, M. (2017). Automatic detection of stance towards vaccination in online discussion forums. In: Jitendra Jonnagaddala, Hong-Jie Dai, and Yung-Chun Chang (Ed.), Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017): . Paper presented at 1st International Workshop on Digital Disease Detection using Social Media (DDDSM), Taipei, Taiwan, 27 November, 2017 (pp. 1-8). Association for Computational Linguistics
Open this publication in new window or tab >>Automatic detection of stance towards vaccination in online discussion forums
2017 (English)In: Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017) / [ed] Jitendra Jonnagaddala, Hong-Jie Dai, and Yung-Chun Chang, Association for Computational Linguistics, 2017, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance against or for vaccination, or as undecided. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance against vaccination from stance for vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Fu- ture work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features. 

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2017
Keyword
stance, online forums, classifier, support vector machine, vaccination
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
urn:nbn:se:lnu:diva-68982 (URN)978-1-948087-07-0 (ISBN)
Conference
1st International Workshop on Digital Disease Detection using Social Media (DDDSM), Taipei, Taiwan, 27 November, 2017
Projects
StaViCTANavigating in streams of opinions
Funder
Swedish Research Council, 2016-06681Swedish Research Council, 2012-5659
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-02-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0519-2537

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