| dc.description.abstract | Social networks play a pivotal role in connecting individuals and fostering interactions
in various domains. They serve as platforms for communication, information
sharing, and community building. Predicting the nature of relationships,
specifically negative ties, within social networks has garnered significant attention
due to its potential impact on user experiences and network dynamics. This
study focuses on the prediction of negative ties in social networks, specifically
in the context of the labeled Wikipedia Requests for Adminship online social
network. Three distinct models are employed to accomplish this task. Firstly,
a Light Gradient Boosting Model (LGBM) utilizes graph topology attributes to
make predictions. The LGBM leverages the structural characteristics of the network,
such as node centrality and connectivity, to identify negative ties.
Secondly, a DistilBert language model is employed to process text data between
users and their corresponding vote labels. The DistilBert model captures
the semantic information embedded within the textual interactions, allowing
for a more nuanced understanding of user sentiments and intentions. Finally,
a Stacking Ensemble Model is employed to combine the predictions from the
LGBM and DistilBert models. The Stacking Ensemble Model aggregates the
predictions of the base models and employs a meta-learner to make the final
predictions. Performance evaluation measures, including accuracy, precision,
recall, F1-score, and elements of the confusion matrix, are used to assess the
models’ predictive capabilities. Presently, all models exhibit strong performance
in detecting positive and negative signed links within the network. Notably,
the DistilBert and Stacking Ensemble models consistently demonstrate superior
performance across all classes. Future research should focus on addressing class
distribution issues, incorporating diverse data, and exploring ensemble techniques
to further enhance the predictive capabilities of these models. | |
