Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts
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, particularl...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9931124/ |
| _version_ | 1811231383048159232 |
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| author | Ahmad Sakor Kuldeep Singh Maria-Esther Vidal |
| author_facet | Ahmad Sakor Kuldeep Singh Maria-Esther Vidal |
| author_sort | Ahmad Sakor |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T10:44:21Z |
| format | Article |
| id | doaj.art-1d52bc5ccbe54beab69de976be97ebe6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T10:44:21Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1d52bc5ccbe54beab69de976be97ebe62022-12-22T03:36:30ZengIEEEIEEE Access2169-35362022-01-011011535111537110.1109/ACCESS.2022.32174929931124Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media PostsAhmad Sakor0https://orcid.org/0000-0001-8007-7021Kuldeep Singh1Maria-Esther Vidal2https://orcid.org/0000-0003-1160-8727TIB Leibniz Information Centre for Science and Technology, Leibniz University of Hannover, Hannover, GermanyZerotha Research, Hannover, GermanyTIB Leibniz Information Centre for Science and Technology, Leibniz University of Hannover, Hannover, GermanySocial 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.https://ieeexplore.ieee.org/document/9931124/Social media networkscommunity detectionpost relatednessknowledge graphCOVID-19knowledge retrieval |
| spellingShingle | Ahmad Sakor Kuldeep Singh Maria-Esther Vidal Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts IEEE Access Social media networks community detection post relatedness knowledge graph COVID-19 knowledge retrieval |
| title | Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts |
| title_full | Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts |
| title_fullStr | Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts |
| title_full_unstemmed | Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts |
| title_short | Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts |
| title_sort | resorting to context aware background knowledge for unveiling semantically related social media posts |
| topic | Social media networks community detection post relatedness knowledge graph COVID-19 knowledge retrieval |
| url | https://ieeexplore.ieee.org/document/9931124/ |
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