Cone-KG: A Semantic Knowledge Graph with News Content and Social Context for Studying Covid-19 News Articles on Social Media

Al-Obeidat, Feras; Adedugbe, Oluwasegun; Bani-Hani, Anoud; Benkhelifa, Elhadj; Majdalawieh, Munir

doi:10.1109/snams52053.2020.9336541

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…Their approach relied on deep learning techniques to extract relationships in the cybersecurity domain. The authors in [18]…”

Section: Knowledge Graphs and Relation Modelingmentioning

confidence: 99%

KG-CFSA: A Comprehensive Approach for Analyzing Multi-source Heterogeneous Social Network Knowledge Graph

Akinnubi,

Alassad,

Amure

et al. 2024

Preprint

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Analyzing opinions, extracting and modeling information, and performing network analysis in online information studies are challenging tasks with multi-source social network data. This complexity arises from the difficulty in harnessing data across various platforms and the absence of a unified data modeling approach. Although social network analysis has used a multiplex approach to study complex networks, no previous work has integrated data from multiple social networks, knowledge graph fusion, and contextual focal structure analysis (CFSA) for an online study. This study has developed a multi-source graph model and applied a Cartesian merge to model relations across multiple documents, entities, and topics. We improved the information modeled with third-party data sources such as WikiData and DiffBot. This approach has created a multiplex network instance for CFSA detection, incorporating topic-topic, entity-entity, and document-document models. We applied this method to a dataset from the Indo-Pacific region and identified 40,000 unique focal sets of influential topics, entities, and documents. The top sets discussed economics, elections, and policies such as the Indo-Pacific Economic Framework, Ekonomi baru, #NKRIHargaMati, #IndonesiaJaya, and the Xinjiang Supply Chain. Our model tracks information spread across multiple social media platforms and enhances the visibility of vital information using various relationships. The results underscore the effectiveness of KG-CFSA in contextualizing large-scale information from multiple sources.

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“…Their approach relied on deep learning techniques to extract relationships in the cybersecurity domain. The authors in [18]…”

Section: Knowledge Graphs and Relation Modelingmentioning

confidence: 99%

KG-CFSA: A Comprehensive Approach for Analyzing Multi-source Heterogeneous Social Network Knowledge Graph

Akinnubi,

Alassad,

Amure

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…This systematic overview can lead to a more insightful analysis of the changes in discourse over time. For instance, Al-Obeidat et al [39] constructed a KG that represents news related to COVID-19. This KG offers a platform for researchers, data analysts, and data scientists from various sectors to explore and suggest solutions for the challenges that COVID-19 creates for the global society.…”

Section: B Kgs On Newsmentioning

confidence: 99%

Exploring Environmental, Social, and Governance (ESG) Discourse in News: An AI-Powered Investigation Through Knowledge Graph Analysis

Angioni,

Consoli,

Dessì

et al. 2024

IEEE Access

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In recent years, the significance of Environmental, Social, and Governance criteria in assessing financial investments has grown significantly. This paper presents an AI-driven analysis of ESG concepts and their evolution from 1980 to 2022, with a specific focus on media sources from the United States and the United Kingdom. The primary data source utilized is the Dow Jones News Article dataset, providing a comprehensive and high-quality collection of news articles. The study introduces a novel technique for information extraction from news articles, involving the structuring of extracted data into a knowledge graph. The findings identified key trends associated with ESG aspects emerging in recent years. In the environmental dimension, we identified pronounced emphasis on climate change, renewable energy sources, and biodiversity conservation. Within the social aspect, the analysis pointed out the increasing significance of issues such as racism, gender identity, human rights as well all the increasing role of charities, and the ethical challenges of modern supply chains. Finally, in the governance domain, the findings emphasized issues related to corporate governance accountability, workplace ethics, and the conduct and remuneration of executives.

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“…Specifically, we use unsupervised latent Dirichlet allocation (LDA) [ 8 ] topic analysis for the semantic modeling of the OSN published content, that allows to build up quantitative vectorial semantic representations of both users and conversation threads, not much unlike the social semantics neurobiological model based on conceptual knowledge [ 7 ]. LDA is a powerful tool that has been used to summarize and build network models of contents, such as semantic graphs relating publications about COVID-19 [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Neuro-semantic prediction of user decisions to contribute content to online social networks

Cleveland

Ríos

Aguilera

et al. 2022

Neural Comput & Applic

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Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

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Cone-KG: A Semantic Knowledge Graph with News Content and Social Context for Studying Covid-19 News Articles on Social Media

Cited by 13 publications

References 4 publications

KG-CFSA: A Comprehensive Approach for Analyzing Multi-source Heterogeneous Social Network Knowledge Graph

KG-CFSA: A Comprehensive Approach for Analyzing Multi-source Heterogeneous Social Network Knowledge Graph

Exploring Environmental, Social, and Governance (ESG) Discourse in News: An AI-Powered Investigation Through Knowledge Graph Analysis

Neuro-semantic prediction of user decisions to contribute content to online social networks

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