TY - JOUR
T1 - Analyzing Social Landscapes
T2 - Visualizing the Key Elements of Social Media Dynamics
AU - Dezhboro, Amirhossein
AU - Babvey, Pouria
AU - Lipizzi, Carlo
AU - Emmanuel Ramirez-Marquez, Jose
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2025
Y1 - 2025
N2 - Social media provides valuable insights into societal opinions and user interactions. Social landscapes, created from these interactions, offer a comprehensive view of online conversations and social media dynamics. The development of advanced data analytics tools has made the creation of social landscapes for larger populations increasingly common and accessible for researchers. This underscores the importance of clearly defining and organizing the insights we seek from social landscapes. We introduce a methodology to analyze the social structure through social landscapes. We have identified 12 key elements that encapsulate the insights expected from social landscapes. Then, we integrate two comprehensive social landscape approaches into our methodology that effectively provide insights into the key elements previously outlined. These two approaches illuminate different facets of social media dynamics: one focuses on the content generated and the other on relationship-based interactions. First, we revisit the concept of galaxies as a single time-based snapshot of large-scale online conversations. Second, we introduce a technique that utilizes the network of user interactions on social media to map social structures. We propose a novel method to construct a sociopolitical spectrum using discourse trajectory inference (pseudotime transformation), marking its first use outside bioinformatics literature. Finally, we take the introduced methodology to evaluate how each social landscape enhances our understanding of social media dynamics. These insights can help to structure the insights we aim to extract from social landscapes and provide practical tools for media analysts and strategists aiming to analyze social media dynamics effectively.
AB - Social media provides valuable insights into societal opinions and user interactions. Social landscapes, created from these interactions, offer a comprehensive view of online conversations and social media dynamics. The development of advanced data analytics tools has made the creation of social landscapes for larger populations increasingly common and accessible for researchers. This underscores the importance of clearly defining and organizing the insights we seek from social landscapes. We introduce a methodology to analyze the social structure through social landscapes. We have identified 12 key elements that encapsulate the insights expected from social landscapes. Then, we integrate two comprehensive social landscape approaches into our methodology that effectively provide insights into the key elements previously outlined. These two approaches illuminate different facets of social media dynamics: one focuses on the content generated and the other on relationship-based interactions. First, we revisit the concept of galaxies as a single time-based snapshot of large-scale online conversations. Second, we introduce a technique that utilizes the network of user interactions on social media to map social structures. We propose a novel method to construct a sociopolitical spectrum using discourse trajectory inference (pseudotime transformation), marking its first use outside bioinformatics literature. Finally, we take the introduced methodology to evaluate how each social landscape enhances our understanding of social media dynamics. These insights can help to structure the insights we aim to extract from social landscapes and provide practical tools for media analysts and strategists aiming to analyze social media dynamics effectively.
KW - Content-aware visualization
KW - dimension reduction
KW - discourse trajectory mapping
KW - node2vec
KW - social landscape analysis
KW - social media dynamics
KW - visual summarization
UR - https://www.scopus.com/pages/publications/105012944203
UR - https://www.scopus.com/pages/publications/105012944203#tab=citedBy
U2 - 10.1109/TCSS.2025.3591139
DO - 10.1109/TCSS.2025.3591139
M3 - Article
AN - SCOPUS:105012944203
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
ER -