# Theory and Applications of Web 3.0 in the Media Sector
## 2 Contributions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Contributions** & **Research Area/Focus** & **Involved Technologies/Solutions** \\ \hline Contribution 1 (C1) & Hate speech detection and emotion analysis & Natural language processing and machine/deep learning algorithms in news semantics, web interface for data crowdsourcing and datasets creation \\ \hline Contribution 2 (C2) & Financial forecasting from news websites and social networks using huge volumes of data (big data) & Natural language processing and machine/deep learning algorithms in stock forecasting, sentiment analysis, investment recommendations \\ \hline Contribution 3 (C3) & Assessment of news homepages over the years (aesthetic-, functional-, content-wise) & Content analysis, aesthetics, and responsive design aspects (web, mobile, multifarant), audience analytics/insights, social instrument (interviews) \\ \hline Contribution 4 (C4) & Automated news generation/publishing for earth observation/disaster reporting & Big data and artificial intelligence algorithms in breaking news detection, sentiment analysis, and automated/personalized news-report generation \\ \hline Contribution 5 (C5) & Semantic preprocessing for breaking news detection (in Drone Journalism) & News streams monitoring/semantics, spatiotemporal and contextual detection of (breaking) news events, Drone Journalism recommendations \\ \hline Contribution 6 (C6) & Semantic web integration analysis (in Art and Culture websites) & Contextual features, metrics, art/culture websites features, semantic web integration assessment \\ \hline Contribution 7 (C7) & Audio semantic analysis and visualizations for audiovisual forensics & Semantic visualizations of machine/deep learning and signal processing to detect audio tampering \\ \hline Contribution 8 (C8) & Social network analysis and visualization for tracing disinformation & Natural language processing and machine/deep learning algorithms in tracing and visualizing suspicious/inaccurate informatory streams \\ \hline Contribution 9 (C9) & Analysis of aesthetic trends and semantic web adoption of media outlets & Automated archival data extraction and analysis to assess Semantic Web integration trends, DOM structure complexity, graphics, and color usage \\ \hline Contribution 10 (C10) & News recommendation systems (NRS) modeling and evaluation & Experimental approach treating the NRS as a black box, entailing users as testers of algorithmic systems (algorithmic/collaborative audit methods) \\ \hline Contribution 11 (C11) & Chatbot Media Automations in Professional Journalism & Experimental approach on the use of chatbot tools that are evaluated metric-wise and through social instruments (workshops) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contributions by research areas, involved technologies, and proposed solutions. processing, and deep learning [31]. [PERSON] et al. (2021) present the supportive platform ASPENDYS, developed as part of the homonymous European research project, intending to facilitate the management and decision making of investment actions through personalized recommendations. The implicated processing relies on technical analysis and machine learning methods for the financial data treatment, with textual data being analyzed in terms of reliability and sentiments towards an investment. As an outcome, investment signals are generated for a certain transaction combining the financial and sentiment analysis insights, which are finally recommended to the investors. A watchful assessment is conducted concerning the interface and its functionalities (i.e., portfolio management, sentiment analysis, extracted investment signals), with the application use cases illustrating practical uses and validating the approach's helpfulness and impact (Contribution 2).