Identifying the Top-k Local Users in Geo-tagged Social Media Information
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Abstract
The use of location-based services and social media platforms is growing in popularity. When put together, they form location-based social media, in which users are linked not only by their internet acquaintances but also by their physical whereabouts. Because of this duality, new methods of querying and using social media data are made feasible. We describe a new and practical issue in the form of geo-tagged tweets: top-k local user search (or TkLUS for short). In order to discover the top-k people who have tweeted about a location within a certain distance from a given location (q), the TkLUS query takes a collection of keywords (W) and a location (q) as inputs. Many application situations may benefit from TkLus enquiries, including spatal decision-making, buddy suggestion, and many more. In order to effectively answer such requests, we develop a set of methods. To begin, we provide two approaches to local user ranking that combine text relevance with geographical proximity in a TkLUS query. After that, we build a hybrid index using scalable tweets. On top of that, we come up with two techniques to handle TkLUS requests. Lastly, in order to test the suggested methods, we do an experiential research using actual twitter datasets. Our ideas are successful, efficient, and scalable, as shown by the experimental findings.
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