Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts

authored by
Ahmad Sakor, Kuldeep Singh, Maria Esther Vidal
Abstract

Social media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post; posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.

Organisation(s)
L3S Research Centre
External Organisation(s)
Zerotha Research
Type
Article
Journal
IEEE ACCESS
Volume
10
Pages
115351-115371
No. of pages
21
ISSN
2169-3536
Publication date
2022
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Computer Science(all), Materials Science(all), Engineering(all), Electrical and Electronic Engineering
Sustainable Development Goals
SDG 3 - Good Health and Well-being
Electronic version(s)
https://doi.org/10.1109/ACCESS.2022.3217492 (Access: Open)