Discovering Influencers in Opinion Formation Over Social Graphs

Abstract

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over the graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the graph topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents by using their public tweets (posts).

Sentiment Analysis Framework and Its Application in Geopolitical Scenarios

“…The results of the study show that the framework can accurately predict political security threats using a combination of lexicon-based approach and machine learning techniques. The paper [19] explores how Natural Language Processing (NLP) techniques can be used to analyze the perception of world leaders on the popular social media platform, Reddit. The paper uses state-of-the-art NLP algorithms such as Flair, DistilBERT, and Text Blob Analysis to classify user comments collected from Reddit's API into positive, negative, or neutral sentiments.…”