Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of social media, the unstructured and dynamic nature of exchanged data and the information overload contribute to the growth of the number of research works proposing methods to improve performance of intelligent analytics services considering both time and semantics of the shared content. The presented paper focuses on the definition of a knowledge tracking framework to ansr questions, such as “What is the semantic evolution of a topic (or news) along the time?”, “How did  arrive to a specific event?”, “What is the evolution of the topics of interest of a user?”, and so on. Our interest is about the elicitation of temporal patterns revealing the evolution of concepts along the time from a social media data stream;  focus on Twitter. Such patterns can be extracted at different levels of abstraction by considering different-sized time intervals and different scopes driven by the conceptualization of users’ queries. To address the proposed aim,  extend Temporal Concept Analysis and  use Description Logic to reason on semantically represented tet streams. The evaluation activity reveals promising results from both sides quantitative and qualitative.
['Carmen De Maio', 'Giuseppe Fenza', 'Vincenzo Loia', 'Francesco Orciuoli']
I am writing a research paper on a idea Unfolding social content evolution along time and semantics
[{"from": "human", "value": "I am writing a research paper on a idea Unfolding social content evolution along time and semantics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of social media, the unstructured and dynamic nature of exchanged data and the information overload contribute to the growth of the number of research works proposing methods to improve performance of intelligent analytics services considering both time and semantics of the shared content. The presented paper focuses on the definition of a knowledge tracking framework to ansr questions, such as \u201cWhat is the semantic evolution of a topic (or news) along the time?\u201d, \u201cHow did  arrive to a specific event?\u201d, \u201cWhat is the evolution of the topics of interest of a user?\u201d, and so on. Our interest is about the elicitation of temporal patterns revealing the evolution of concepts along the time from a social media data stream;  focus on Twitter. Such patterns can be extracted at different levels of abstraction by considering different-sized time intervals and different scopes driven by the conceptualization of users\u2019 queries. To address the proposed aim,  extend Temporal Concept Analysis and  use Description Logic to reason on semantically represented tet streams. The evaluation activity reveals promising results from both sides quantitative and qualitative."}]