In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network’s structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health.

In today’s digital era, the internet is an indispensable platform for self-expression, facilitating communication, idea sharing, and community formation. Language, a pivotal tool in these online interactive spaces, is vital in reflecting personal identities, notably gender identification. This paper investigates gender identification on online discussion platforms, recognizing the crucial role of language in reflecting personal identities. The study employs Natural Language Processing techniques and machine learning algorithms to analyze data from a public discussion website. Beginning with a comprehensive literature review, the research explores the nexus between gender and language in online and offline contexts. The methodology involves data gathering, extensive preprocessing, and in-depth exploratory analysis, employing statistical methods and graphical representations. The study then rigorously evaluates their accuracy and effectiveness by applying diverse algorithms and models for gender-based text categorization. Results indicate the superior performance of transformer models, particularly distilBERT, in categorizing gender accurately. Additionally, the research underscores the challenges of gender-neutral analysis, emphasizing the need for inclusive methodologies in non-binary gender classification. The study contributes to the broader field of gender studies, providing valuable insights for future research and discussions on the interplay of gender and language in online spaces.

Social networks are an essential component of people’ daily lives, and as a result, much academic attention has been focused on them. The rapid adoption of machine learning as a problem-solving tool, which simplifies and accelerates numerous tasks while enabling the processing of large volumes of data, has played a significant role in this field of research. This is in contrast to the more traditional approaches that lacked this momentum. Characterization of linkages and cluster identification i n social networks are two of the research community’s most well-known issues. The goal of this study is to gather data for a set of users who are then divided into groups based on the hashtags they used in their Twitter postings. The procedure performed generates the numerical data, in following reduces the dimensions, and finally performs the clustering.

The identification of social media communities has recently been of major concern, since users participating in such communities can contribute to viral marketing campaigns. In this work, we focus on users' communication considering personality as a key characteristic for identifying communicative networks i.e., networks with high information flows. We describe the Twitter Personality based Communicative Communities Extraction (T-PCCE) system that identifies the most communicative communities in a Twitter network graph considering users' personality. We then expand existing approaches in users' personality extraction by aggregating data that represent several aspects of user behavior using machine learning techniques. We use an existing modularity based community detection algorithm and we extend it by inserting a post-processing step that eliminates graph edges based on users' personality. The effectiveness of our approach is demonstrated by sampling the Twitter graph and comparing the communication strength of the extracted communities with and without considering the personality factor. We define several metrics to count the strength of communication within each community. Our algorithmic framework and the subsequent implementation employ the cloud infrastructure and use the MapReduce Programming Environment. Our results show that the T-PCCE system creates the most communicative communities.