Multimodal Analytics Dashboard for Story Detection and Visualization

Scharl, Arno; Hubmann-Haidvogel, Alexander; Göbel, Max; Schäfer, Tobi; Fischl, Daniel; Nixon, Lyndon

doi:10.1007/978-3-030-26752-0_10

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…sarcasm detection, aspect extraction) layers [22]. The authors have developed components across all these layers, including an aspect-based sentiment analysis engine [56], a named entity linking (NEL) engine [57] and a NLP and visualisation pipeline that includes topic, concept and story detection [58]. These components are used as a basis for the extraction of social indicators.…”

Section: Methodsmentioning

confidence: 99%

Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications

et al. 2021

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Sentic computing relies on well-defined affective models of different complexity—polarity to distinguish positive and negative sentiment, for example, or more nuanced models to capture expressions of human emotions. When used to measure communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation’s strategic positioning goals. Such goals often deviate from the assumptions of standardised affective models. While certain emotions such as Joy and Trust typically represent desirable brand associations, specific communication goals formulated by marketing professionals often go beyond such standard dimensions. For instance, the brand manager of a television show may consider fear or sadness to be desired emotions for its audience. This article introduces expansion techniques for affective models, combining common and commonsense knowledge available in knowledge graphs with language models and affective reasoning, improving coverage and consistency as well as supporting domain-specific interpretations of emotions. An extensive evaluation compares the performance of different expansion techniques: (i) a quantitative evaluation based on the revisited Hourglass of Emotions model to assess performance on complex models that cover multiple affective categories, using manually compiled gold standard data, and (ii) a qualitative evaluation of a domain-specific affective model for television programme brands. The results of these evaluations demonstrate that the introduced techniques support a variety of embeddings and pre-trained models. The paper concludes with a discussion on applying this approach to other scenarios where affective model resources are scarce.

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Section: Methodsmentioning

confidence: 99%

Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications

et al. 2021

Self Cite

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“…These projects aim at developing tools for image/video metadata analysis, key-frame extraction, reverse image search, magnifier, forensic analysis and contextual data analysis [18], [146], [147]. Under the InVID project, researchers have also developed a social media analytics dashboard to find and track trending stories across several social media websites [148].…”

Section: Mappingmentioning

confidence: 99%

Visual User-Generated Content Verification in Journalism: An Overview

et al. 2023

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Over the past few years, social media has become an indispensable part of the news generation and dissemination cycle on the global stage. These digital channels along with the easy-to-use editing tools have unfortunately created a medium for spreading mis-/disinformation containing visual content. Media practitioners and fact-checkers continue to struggle with scrutinising and debunking visual user-generated content (UGC) quickly and thoroughly as verification of visual content requires a high level of expertise and could be exceedingly complex amid the existing computational tools employed in newsrooms. The aim of this study is to present a forward-looking perspective on how visual UGC verification in journalism can be transformed by multimedia forensics research. We elaborate on a comprehensive overview of the five elements of the UGC verification and propose multimedia forensics as the sixth element. In addition, different types of visual content forgeries and detection approaches proposed by the computer science research community are explained. Finally, a mapping of the available verification tools media practitioners rely on is created along with their limitations and future research directions to gain the confidence of media professionals in using multimedia forensics tools in their day-to-day routine.

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“…Recent approaches in narrative digital humanities also automatize parts of these processes to automatically generate structured story points from large knowledge bases, or to extract them from texts for their subsequent visualization [2]. In other fields (e.g., data journalism) there are similar visualization-based storytelling systems with similar layout and workflows, such as for the analysis and content extraction of social media data [18]. However, aside from early, experimental work on fully AI-driven story creation, human minds remain the main drivers of the outlined multi-stage workflows.…”

Section: Workflows In Visualization-based Storytellingmentioning

confidence: 99%

A Workflow Approach to Visualization-Based Storytelling with Cultural Heritage Data

Liem,

Kusnick,

Beck

et al. 2023

2023 IEEE 8th Workshop on Visualization for the Digital Humanities (VIS4DH)

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Stories are as old as human history-and a powerful means for the engaging communication of information, especially in combination with visualizations. The InTaVia project is built on this intersection and has developed a platform which supports the workflow of cultural heritage experts to create compelling visualization-based stories: From the search for relevant cultural objects and actors in a cultural knowledge graph, to the curation and visual analysis of the selected information, and to the creation of stories based on these data and visualizations, which can be shared with the interested public.

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Multimodal Analytics Dashboard for Story Detection and Visualization

Cited by 4 publications

References 13 publications

Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications

Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications

Visual User-Generated Content Verification in Journalism: An Overview

A Workflow Approach to Visualization-Based Storytelling with Cultural Heritage Data

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