The goal of this research is to provide linguists with visualisations for analysing the results of their hate speech annotation. These visualisations consist of a set of interactive graphs for analysing the global distribution of annotated messages, finding relationships between features, and detecting inconsistencies in the annotation. We used a corpus that includes 1,262 comments posted in response to different Spanish online new articles. The comments were annotated with features such as sarcasm, mockery, insult, improper language, constructivity and argumentation, as well as with level of toxicity ('not-toxic', 'mildly toxic', 'toxic' or 'very toxic'). We evaluated the selected visualisations with users to assess the graphs' comprehensibility, interpretability and attractiveness. One of the lessons learned from the study is the usefulness of mixed visualisations that include simple graphs (Bar, Heat map) -to facilitate the familiarisation with the results of the annotated corpus together with more complex ones (Sankey, Spider or Chord) -to explore and identify relationships between features and to find inconsistencies.
Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users’ interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods.
The goal of this research is to provide linguists with visualisations for analysing the results of their hate speech annotation. These visualisations consist of a set of interactive graphs for analysing the global distribution of annotated messages, finding relationships between features, and detecting inconsistencies in the annotation. We used a corpus that includes 1,262 comments posted in response to different Spanish online new articles. The comments were annotated with features such as sarcasm, mockery, insult, improper language, constructivity and argumentation, as well as with level of toxicity ('not-toxic', 'mildly toxic', 'toxic' or 'very toxic'). We evaluated the selected visualisations with users to assess the graphs' comprehensibility, interpretability and attractiveness. One of the lessons learned from the study is the usefulness of mixed visualisations that include simple graphs (Bar, Heat map) -to facilitate the familiarisation with the results of the annotated corpus together with more complex ones (Sankey, Spider or Chord) -to explore and identify relationships between features and to find inconsistencies.
Multivariate hierarchical data has an important role in many applications. To find the best visualisation that best fits a concrete data is crucial to explore and understand the relationships between the data. This paper proposes a categorisation – Elongated and Compact – of hierarchical data based on the inner shapes of the hierarchies, that is the connectivity degree of the internal nodes, the number of nodes, etc, that can be applied to any hierarchical data. Based on this taxonomy, we explore implicit and explicit layouts – Tree, Circle Packing, Force and Radial – to provide users with a complete view of the data. We hypothesise that Tree and Circle Packing fit with Elongated structures, and Force and Radial fit with Compact ones. In addition, we cluster multivariate features to embed them in the hierarchical layouts. Especially, we propose two different glyphs – one-by-one and all-in-one, and we bet for the one-by-one glyphs as the most suitable for showing the distribution of several features along with the hierarchical structures. To validate our hypotheses, we conducted a user study with 35 participants using a hate speech annotated corpus. This corpus comes from 4359 comments posted in online Spanish newspapers. The results indicated that users preferred the Tree layout over the other three layouts (Circle, Force, Radial) with both types of structures (EC and CC). However, when we focused the analysis only on Radial and Force layouts, both of them scored significantly higher with Compact than with Elongated data. Moreover, participants scored the one-by-one glyph higher than the all-in-one glyph, but the difference was not significant.
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