Web Intelligence Meets Computational Social Science

🏛️⚖️🤝 How can we ensure that digital technology supports rather than undermines democratic decision-making and shared societal values? Presentation of the DWBI Project ("Digital Well-Being Index for Vienna") funded by the Vienna Science and Technology Fund (WWTF) as part of the lecture series “Foundations & Methods of Digital Humanism" at the University of Vienna. The lecture focused on semantic methods to extract regional indicators of subjective well-being from digital content streams (Web sites, social media, etc.) as well as key challenges such as disinformation and filter bubbles. DWBI is jointly pursued by researchers of the Research Center for New Media Technology and the School of Sustainability, Governance and Methods of Modul University Vienna.

Transcript

2. 2 Analyzing Digital Content Streams DWBI Vienna Human-Centered Analytic Approaches • Web Structure: Social Network Analysis, PageRank • Web Usage: Log File Analysis, Engagement Metrics • Web Content: Semantic Technologies, Knowledge Extraction – Factual Knowledge: Verifiable; e.g., the Hofburg is in Vienna – Affective Knowledge: Sentiment, Emotions, Perceptions of Well-Being Semiotics – Study of Signs and Symbols • Syntax: Check conformity with formal rules; e.g., spell checker • Semantics: Capture and process meaning with automated methods • Pragmatics: Study how context affects the perception of language
3. 3 Analyzing Digital Content Streams DWBI Vienna Human-Centered Analytic Approaches • Web Structure: Social Network Analysis, PageRank • Web Usage: Log File Analysis, Engagement Metrics • Web Content: Semantic Technologies, Knowledge Extraction – Factual Knowledge: Verifiable; e.g., the Hofburg is in Vienna – Affective Knowledge: Sentiment, Emotions, Perceptions of Well-Being Semiotics – Study of Signs and Symbols • Syntax: Check conformity with formal rules; e.g., spell checker • Semantics: Capture and process meaning with automated methods • Pragmatics: Study how context affects the perception of language
6. 6 Goals and Challenges DWBI Vienna Measuring Life Satisfaction – References to Life Satisfaction are often subtle and not expressed in a direct and obvious manner Data Capture and Aggregation • 4.6 million Twitter postings, 1.8 million with reference to “Vienna” (curated samples to obtain a critical mass of Viennese authors) • Twitter API restrictions --> EU Digital Services Act, Copyright Directive • Alternative sources of stakeholder communication
8. 8 Lapowsky, Issie (2023): Tech Companies’ Friendly New Strategy to Destroy One Another. The Atlantic: July 28, 2023.
9. 9 Goals and Challenges DWBI Vienna Measuring Life Satisfaction – References to Life Satisfaction are often subtle and not expressed in a direct and obvious manner Data Capture and Aggregation • 4.6 million Twitter postings, 1.8 million with reference to “Vienna” (curated samples to reflect a critical mass of Viennese authors) • Twitter API restrictions --> EU Digital Services Act, Copyright Directive • Alternative sources of stakeholder communication Algorithmic Work to capture well-being indicators and continually increase the precision of computed metrics Visual Analytics Dashboard – dwbi.webLyzard.com
13. 13 Metrics and Affective Models DWBI Vienna Wheel of Emotions Robert Plutchik Share of Voice Sentiment
14. 14 Structure of Subjective Well-Being (SWB) DWBI Vienna
15. 15 15
20. 20 Dashboard Access and Research Outlook DWBI Vienna DWBI Dashboard Email scharl@modul.ac.at to request an account Sustainability and Political Communication • SDG Intelligence for UN Environment www.weblyzard.com/unep-live • US Election 2024 Web Monitor www.weblyzard.com/us-election-2024 Research Roadmap Public Spaces for Citizen Deliberation • Share insights and best practices across languages and cultures • Increase personal efficacy (being heard; making a difference) • Engage citizens and support democratic processes