ThreadReconstructor: Modeling Reply‐Chains to Untangle Conversational Text through Visual Analytics

El‐Assady, Mennatallah; Sevastjanova, Rita; Keim, Daniel A.; Collins, Christopher

doi:10.1111/cgf.13425

Cited by 21 publications

(16 citation statements)

References 28 publications

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“…Ahmed et al [AYMW11] use a qualitative color scheme in order to encode cluster groupings in all views of their visualization tool for steering mixed‐dimensional KD‐KMeans clustering. Color is used in almost every paper we examined [CSG*18, EASKC18, KS12, ML14, PSF17, XCH*16]. DeepCompare [MMD*19] uses opacity and size , which are the two second‐most occurring visual variables .…”

Section: In‐depth Categorization Of Trust Against Facets Of Interamentioning

confidence: 99%

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Chatzimparmpas

Martins

Jusufi

et al. 2020

Computer Graphics Forum

143

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Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

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Section: In‐depth Categorization Of Trust Against Facets Of Interamentioning

confidence: 99%

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Chatzimparmpas

Martins

Jusufi

et al. 2020

Computer Graphics Forum

143

View full text Add to dashboard Cite

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“…Modality -The analysis modality refers to the employed concepts and categorizes into the three common aspects: meta-data (e.g., [47]), network (e.g., [40]), and content (e.g., [73]), as used in many technical descriptions of communication analysis.…”

Section: Model Scopementioning

confidence: 99%

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

Fischer¹,

Dennig²,

Seebacher³

et al. 2021

Preprint

Self Cite

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<div>The automated analysis of digital human communication data often focuses on specific aspects like content or network structure in isolation, while classical communication research stresses the importance of a holistic analysis approach. This work aims to formalize digital communication analysis and investigate how classical results can be leveraged as part of visually interactive systems, which offers new analysis opportunities to allow for less biased, skewed, or incomplete results. For this, we construct a conceptual framework and design space based on the existing research landscape, technical considerations, and communication research that describes the properties, capabilities, and composition of such systems through 30 criteria in four analysis dimensions. We make the case how visual analytics principles are uniquely suited for a more holistic approach by tackling the automation complexity and leverage domain knowledge, paving the way to generate design guidelines for building such approaches. Our framework provides a common language and description of communication analysis systems to support existing research, highlights relevant design areas while promoting and supporting the mutual exchange between researchers. Additionally, our framework identifies existing gaps and highlights opportunities in research areas that are worth investigating further. With this contribution, we pave the path for the formalization of digital communication analysis through visual analytics.</div>

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“…While StanceVis Prime supports the export of some of the analytical findings as annotated document lists, our approach could be extended with further means to externalize and present analytical results, thus following the suggestions by Chen et al (2018) on bridging the gap between visual analytics and storytelling. Visual analysis of the public discourse structure (Cao et al 2015;El-Assady et al 2018;Lu et al 2018) enriched by sentiment and stance data is also an interesting prospect. Combining such an approach with the analyses discussed above would make it possible to shift the focus of the workflow from the initial selection of targets of interests-as currently supported by StanceVis Prime-towards the monitoring and investigation of patterns and themes emerging in the incoming data in a dynamic way.…”

Section: Overall Utility and Generalizabilitymentioning

confidence: 99%

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

Kucher

Martins

Paradis

et al. 2020

J Vis

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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 in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the 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. Graphic abstract

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ThreadReconstructor: Modeling Reply‐Chains to Untangle Conversational Text through Visual Analytics

Cited by 21 publications

References 28 publications

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers

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

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