Social Media and Web Sensing with Semantic Integration on the Refugee Crisis

Stathopoulos, Evangelos A.; Diplaris, Sotiris; Karageorgiadis, Anastasios I.; Kokkalas, Alexandros; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.1109/smap56125.2022.9942201

Cited by 1 publication

(2 citation statements)

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“…In our research, as expounded in our previous work [37], we meticulously curated collections of keywords and key phrases with the help of cultural experts. These collections were centered on forced migration, human rights, and discrimination, aiming to retrieve relevant multimedia content.…”

Section: Methodsmentioning

confidence: 99%

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A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics?

et al. 2023

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This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms and how they are integrated in a semantic framework. The evaluation was based on cosine similarity metrics, including the Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), and normalized Discounted Cumulative Gain (nDCG), as well as the Mean Average Precision (MAP). Additionally, the paper discusses the use of semantic web technologies as a component in a pipeline for building thematic knowledge graphs from retrieved social media data with extended ontologies integrated for the refugee crisis. The paper begins by introducing the importance of query expansion in information retrieval and the potential benefits of incorporating semantic web technologies. The study then presents the methodologies and outlines the specific procedures for each query expansion technique. The results of the evaluation are presented, as well as the rest semantic framework, and the best-performing technique was identified, which was the curie-001 generative AI model. Finally, the paper summarizes the main findings and suggests future research directions.

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Section: Methodsmentioning

confidence: 99%

“…We limited our focus to the most-prominent keyword from each of the 14 collectionsthe keyword responsible for retrieving the most-comprehensive online content, as documented in [37]. Our rationale here was to ensure ample content availability online for benchmarking query expansion, thus mitigating any potential bias from data scarcity.…”

Section: Methodsmentioning

confidence: 99%