Enhanced Elephant Herding Optimization for Large Scale Information Access on Social Media

TL;DR

This work tackles large-scale information access on social media by fusing information-foraging theory (IFT) with a novel enhanced Elephant Herding Optimization (EHO). The authors formalize an information-foraging model for social platforms and adapt EHO to a discrete surfing-path problem, introducing territories via clustering and a migration mechanism to handle scalability. The main contributions include (i) a formal IFT-based model for social media information access, (ii) the Enhanced EHO with territorial clustering (EEHOLSIF), (iii) a large Twitter-based dataset (over $1.4$ million tweets) for evaluation, and (iv) a comparative study against ACS and PSO showing higher relevance scores and faster convergence. The results demonstrate improved relevance, reduced response times, and better convergence, highlighting the practical potential for scalable, bio-inspired information retrieval on social networks.

Abstract

In this article, we present a novel information access approach inspired by the information foraging theory (IFT) and elephant herding optimization (EHO). First, we propose a model for information access on social media based on the IFT. We then elaborate an adaptation of the original EHO algorithm to apply it to the information access problem. The combination of the IFT and EHO constitutes a good opportunity to find relevant information on social media. However, when dealing with voluminous data, the performance undergoes a sharp drop. To overcome this issue, we developed an enhanced version of EHO for large scale information access. We introduce new operators to the algorithm, including territories delimitation and clan migration using clustering. To validate our work, we created a dataset of more than 1.4 million tweets, on which we carried out extensive experiments. The outcomes reveal the ability of our approach to find relevant information in an effective and efficient way. They also highlight the advantages of the improved version of EHO over the original algorithm regarding different aspects. Furthermore, we undertook a comparative study with two other metaheuristic-based information foraging approaches, namely ant colony system and particle swarm optimization. Overall, the results are very promising.

Figures (14)

• Figure 1: Analogy between Information Foraging and Food Foraging
• Figure 2: Social graph structure
• Figure 3: User's interests extraction
• Figure 4: Elephants society structure
• Figure 5: A population of elephants distributed over a social graph