A geofencing-based recent trends identification from twitter data

M., Saef Ullah Miah and M., Sadid Tahsin and Azad, Saiful and Rabby, Gollam and M., Sirajul Islam and Uddin, Shihab and M., Masuduzzaman (2020) A geofencing-based recent trends identification from twitter data. In: IOP Conference Series: Materials Science and Engineering, 6th International Conference on Software Engineering and Computer Systems (ICSECS 2019) , 25 - 27 September 2019 , Vistana Kuantan City Center, Kuantan, Pahang. pp. 1-10., 769 (012008). ISSN 1757-8981 (Print), 1757-899X (Online)

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For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires data based on a geofence. Afterwards, they are cleaned and the weight of these tweet data is calculated. For that, the frequency of tweet texts and hashtags are taken into account along with a boosting factor. Thereafter, they are ranked to recommend recent trends to the user. This proposed technique is applied in developing a system using Java and python. It is compared with other relevant systems, where it demonstrates that the performance of the proposed system is comparable. Over and above, since the proposed system integrates geofencing feature, it is more preferable over other systems.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Extensive data; Twitter data; Microblogging social media platforms; Geofencing feature
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Centre of Excellence: IBM Centre of Excellence
Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 20 Jun 2022 03:02
Last Modified: 20 Jun 2022 03:02
URI: http://umpir.ump.edu.my/id/eprint/28788
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