A geofencing-based recent trends identification from twitter data

• Main Authors: M., Saef Ullah Miah, M., Sadid Tahsin, Azad, Saiful, Rabby, Gollam, M., Sirajul Islam, Uddin, Shihab, M., Masuduzzaman
• Format: Conference or Workshop Item
• Language: English
• Published: IOP Publishing 2020
• Subjects: QA Mathematics; QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/28788/1/A%20geofencing-based%20recent%20trends%20identification%20from%20twitter%20data.pdf; http://umpir.ump.edu.my/id/eprint/28788/; https://doi.org/10.1088/1757-899X/769/1/012008

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.