ArticleProcedia computer science2020
A Spatial-Temporal Model for Event Detection in Social Media.
Article in Procedia computer science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 authors.
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Abstract
Nowadays, the interest in data modelling from the spatial-temporal perspective is constantly increasing. Moreover, a wide variety of applications, such as social network data, need to be done to study spatiotemporal patterns. In general, however, these patterns are highly complex and challenging, so it is a demanding process to analyze or to classify them as the conventional context in various types of event data. In order to analyze the traffic viral within the text from the perspective of impressive negative effects, we should spatial-temporally localize the event and geographical regions and give a semantically interpreting of what happened. We propose a review of the best models and techniques applied for social media data processing to formalize a novel theory of action and time. This investigation intends to draw the basic knowledge level over which research intended to decipher in texts the occurrence of events, together with their involved characters, and their relationship with time and space.
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