POPULARITY PREDICTION FOR SINGLE TWEET BASED ON HETEROGENEOUS BASS MODEL

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Vidhya Shenigaram
Katakam Krishna Chaitanya
Pallavi Bhramarautu
Mosheck Menta

Abstract

Predicting the popularity of a single tweet is useful for both users and enterprises. However, adopting existing topic or event prediction models cannot obtain satisfactory results. The reason is that one topic or event that consists of multiple tweets, has more features and characteristics than a single tweet. In this paper, we propose two variations of Heterogeneous Bass models (HBass), originally developed in the field of marketing science, namely Spatial-Temporal Heterogeneous Bass Model (ST-HBass) and Feature-Driven Heterogeneous Bass Model (FDHBass), to predict the popularity of a single tweet at the early stage and the stable stage. We further design an Interaction Enhancement to improve the performance, which considers the competition and cooperation from different tweets with the common topic. In addition, it is often difficult to depict popularity quantitatively. We design an experiment to get the weight of favorite, retweet and reply, and apply the linear regression to calculate the popularity. Furthermore, we design a clustering method to bound the popular threshold. Once the weight and popular threshold are determined, the status whether a tweet will be popular or not can be justified. Our model is validated by conducting experiments on real-world Twitter data, and the results show the efficiency and accuracy of our model, with less absolute percent error and the best Precision and F-score. In all, we introduce Bass model into social network single-tweet prediction to show it can achieve excellent performance.

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How to Cite
Shenigaram, V., Chaitanya, K. K. ., Bhramarautu, P. ., & Menta, M. (2018). POPULARITY PREDICTION FOR SINGLE TWEET BASED ON HETEROGENEOUS BASS MODEL. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 9(3), 1291–1299. https://doi.org/10.61841/turcomat.v9i3.14472
Section
Research Articles

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AUTHORS PROFILE

Mrs.P.Anusha, working as Associate

Professor Of Computer Science And

Engineering Department in Qis College Of

Engineering and Technology(Autonomous),

Ongole, Andhra Pradesh, India

K.Brahma Teja pursuing B.Tech in the

department of Computer Science

&Engineering from Qis college of

Engineering and Technology

(Autonomous&NAAC‘A’Grade),Pondur

uRoad,Vengamukkalapalem,Ongole,Prak

asamDist.AffiliatedtoJawaharlalNehruTe

chnologicalUniversity,Kakinadain2018-

respectively.

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