Abstract
Due to the limited length of tweets, hashtags are often used by users in their tweets. Thus, hashtag recommendation is highly desirable for users in Twitter to find useful hashtags when they type in tweets. However, there are many factors that may affect the effectiveness of hashtag recommendation, which includes social relationships, textual information and user profiling based on hashtag preference. In this paper, we aim to analyse the effect of these factors in hashtag recommendation on the detected communities in Twitter. In details, we seek answers to the two questions: What is the most significant factor in recommending hashtags in the context of detected communities? How the different community detection algorithms and the size of the communities affect the performance of hashtag recommendation?
To answer these questions, we detect the communities using two algorithms: Breadth First Search (BFS) and Clique Percolation Method (CPM). On the randomly detected communities, we investigate the quality and the behaviour of the recommended hashtags people consumed. From the extensive experimental results, we have the following conclusions. First, social factor is the most significant factor along with the textual factor for hashtag recommendation. Second, we find that the quality of the hashtag recommendation in the community detected using CPM clearly outperforms that using BFS. Third, incorporating user profiling increases the quality of the recommended hashtags.
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This work is partially supported by the ARC Discovery Project under grant No. DP160102114 and UWA startup grant.
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Alsini, A., Datta, A., Li, J., Huynh, D. (2017). Empirical Analysis of Factors Influencing Twitter Hashtag Recommendation on Detected Communities. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_9
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DOI: https://doi.org/10.1007/978-3-319-69179-4_9
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