HeartBeat - An interactive installation to reflect the sentiments of Canadians during pandemics like Covid-19

Social media has given citizens an avenue to express their views on various subjects in their personal lives, policies, and even a way to communicate with each other about their sentiments and emotions. This is key during a pandemic such as Covid-19 where the world is facing a global impact and the need for a pandemic-related public art framework has been sought globally by art societies and researchers to revitalize the society. However, due to the pace of this pandemic, most city art strategy papers require a framework for pandemic related public art especially in Toronto which has an agenda of moving towards becoming a smart city and public art should reflect that. This thesis investigates how might public art installations reflect the sentiment of smart communities in a pandemic. I designed 'HeartBeat', an interactive installation and visualization to reflect the emotions of citizens during the pandemic using Research Through Design and user-centered design approaches. The goal is to reflect the sentiment of Canadians during the current pandemic. HeartBeat uses tweets from Canada and visualizes the popular emotion groups during the pandemic period in an interactive installation. To evaluate HeartBeat, I conducted case study evaluation for various time periods and semi-structured interviews by selecting experts such as artists, designers, curators, policymakers, and data journalists. The contributions from HeartBeat could provide designers and artists exploring the pandemic to consider these design choices and methodologies; discussion shows the ways available to understand emotions of citizens during a pandemic in a smart city; detailed process design and technology stack architecture for pandemic related public art which could be used as public art frameworks during pandemics

Visual Analysis of Sentiment and Stance in Social Media Texts

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

StanceVis Prime: visual analysis of sentiment and stance in social media texts

Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest for this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by existing approaches. The challenges associated with this problem include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert

StanceVis Prime : Visual Analysis of Sentiment and Stance in Social Media Texts

Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest for this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by existing approaches. The challenges associated with this problem include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert