Geographically-aware mobile application for managing events and interest group communications

• Main Author: Lee, Chong Yan
• Other Authors: Oh Hong Lye
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Computer science and engineering::Software; Engineering::Computer science and engineering::Data
• Online Access: https://hdl.handle.net/10356/148164
• Institution: Nanyang Technological University

Crowdsourcing has become increasingly prevalent [1] as people source for more information and services from one and another over social media platform. The proliferation of social media platforms, such as Instagram, Facebook, Twitter and Reddit, is attributed to humans’ need for more information to make more informed decision or to simply get together to fish out common gossips within the communities, with the likes of Reddit and Twitter. However, sometimes, people need timely information to make time-critical decision. For example, supposed one meets a roadblock during his road trip and there is no information in the Internet, one would prefer to have a fast and prompt reply to the resolution, whether to revert path or move forward, which a forum would not be able to do so. The reason is that forum is passive in nature, which means that usually people search for already available information rather than ask a question and wait for a reply as after all it requires refreshing of the page and we do not know when there will be a response. Besides, with the huge pool of information online, it would be hard for the relevant party to find this thread, let alone reply it. Beyond that, the nature of socializing is vastly different between forum-based chat and real-time chat as real-time chat turns these connections into personal relationship and increases user engagement as users have to keep the conversations going. Furthermore, taking the example of roadblock, some topics and events are very geographic specific in nature, hence having the geographic element will ensure that they are relevant to the users, thus increasing the hit rate of the channels. Hence, this project aims to build a production-ready and scalable social chatting platform where people can easily connect with other, based on their locations as well as their interests. Granted, users do not have to actively seek for these information as they will be actively recommended to them with the implementation of a recommendation engine based on collaborative filtering, which filters the interests of the users by collecting preferences from many other users. The implementation of frontend is done with Flutter, which is a cross native platform language, backend is done with Python and deployed on Google App Engine.