Tweet Length Matters: A Comparative Analysis on Topic Detection in Microblogs

Date

2021-01-01

Author

Şahinuç, Furkan
Toraman, Çağrı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Microblogs are characterized as short and informal text; and therefore sparse and noisy. To understand topic semantics of short text, supervised and unsupervised methods are investigated, including traditional bag-of-words and deep learning-based models. However, the effectiveness of such methods are not together investigated in short-text topic detection. In this study, we provide a comparative analysis on topic detection in microblogs. We construct a tweet dataset based on the recent and important events worldwide, including the COVID-19 pandemic and BlackLivesMatter movement. We also analyze the effect of varying tweet length in both evaluation and training. Our results show that tweet length matters in terms of the effectiveness of a topic-detection method.

Subject Keywords

Microblog, Short text, Topic detection, Tweet

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85107337560&origin=inward
https://hdl.handle.net/11511/109602

Collections

Department of Computer Engineering, Conference / Seminar