Historically, online data has provided meaningful insights for information mining, leading to the adoption of knowledge graphs for application to online data. Knowledge embedding has become an important aspect of encoding and decoding links, relationships, and predicting the ties of an entity to an existing knowledge graph. This study applied topic modeling to extract topics, entities, and themes from heterogeneous web data from different sources around the Indo-Pacific region and modeled a knowledge graph. The knowledge graph was subjected to knowledge embedding by applying four scoring mechanisms: ComplEx, TransE, DistMult, and HolE, on a domain knowledge graph of Indo-Pacific Belt and Road initiatives to determine whether it was capable of revealing missing insights. This work significantly uses knowledge graphs and embedding to understand socioeconomic-related discussions online. Valuable insights were gained from the data in this research's clustering results of knowledge embedding. Important themes such as NASAKOM and BRI were identified in Cluster 0. Cluster 1 contained themes that discussed Marxist movements synonymous with Indonesia, and Cluster 2 showed themes on China's road policies, such as Asia-Pacific Economic Cooperation and Export-Import Bank China. Cluster 3 focused mainly on China's economic policies and the Philippines. Overall, this study demonstrates the usefulness of topic modeling and knowledge embedding in uncovering insights from online data and has implications for understanding socioeconomic trends in the Indo-Pacific region.

YouTube has revolutionized content consumption and global user interaction. It has become a central hub for video sharing, entertainment, and information dissemination. However, as the user base continues to expand and actively engage with the platform, concerns have arisen regarding the presence of suspicious behavior among commenters. This study presents an approach based on social network analysis to detect suspicious commenter behaviors and identify similarities across various YouTube channels in relation to such behaviors. The analysis involves 20 YouTube channels that disseminated false views about the U.S. Military. The dataset included 7,782 videos, 294,199 commenters, and 596,982 comments. We employ a combination of methods, including Graph2vec, UMAP, K-means, Hierarchical clustering, qualitative and quantitative analyses. The objective is to categorize channels based on the level of suspicious behavior and reveal common patterns exhibited among them. To assess the effectiveness of the proposed methodology, the outcomes revealed the presence of commenter mobs and significant similarities among these channels, providing valuable insights into the prevalence of suspicious commenter behavior.

Complexity and dynamicity of the social networks are categorized as NP-hard problems to solve and analyze. These variables on social networks such as actions and interrelationships between the network's users, different behaviors, users' feedbacks and the networks' dynamics make them intractable. Systems thinking and modeling methods orient the relationships between all parts in online social networks. Complexity theories, system dynamics, and game theoretic approaches implemented by system thinkers help investigate the system's local parties' relationships. These methods also present a useful tool to interpret the social networks' complex interactions, dynamic activities in online networks and communities between users and their online dynamic interactions. In this paper, systems thinking concepts and organizational cybernetics are employed to analyze the armed protest demonstration against COVID-19 lockdown at Michigan capitol on May 12th through May 15th on Twitter. Utilizing these methods, we also present a systematic analysis to control, analyze, and comprehend the actions, tweets, and retweets exchanged between users' supporting and opposing the campaign in a complex and dynamic environment.